Generated:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Membrane:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Functional Assay:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Solubility:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
High Throughput Screening Assay:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Selection:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Microelectrode Array:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Solvent:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Polymer:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
Electron Microscopy:Article Title: Metal-Dependent Mechanism of the Electrocatalytic Reduction of CO 2 by Bipyridine Complexes Bearing Pendant Amines: A DFT Study.
Article Snippet: The study employs classical DFT with the solvation model based on density (DFT/SMD) and ab-initio Molecular Dynamics (AIMD).
Article Title: Benchmark Ab Initio Mapping of the F - + CH 2 ClI S N 2 and Proton-Abstraction Reactions.
Article Snippet: The experimental and theoretical studies of gasphase SN2 reactions have significantly broadened our understanding of the mechanisms governing even the simplest chemical processes.. These investigations have not only advanced our knowledge of reaction pathways but also provided critical insights into the fundamental dynamics of chemical systems.. Nevertheless, in the case of the prototypical X− + CH3Y → Y− + CH3X [X, Y = F, Cl, Br, and I] SN2 reactions, the effect of the additional halogenation of CH3Y has not been thoroughly explored.
Article Title: Charlie Ruffman.
Article Snippet: International Edition: DOI: 10.1002/anie.202419076 German Edition: DOI: 10.1002/ange.202419076 Research Fellow, University of Auckland (New Zealand) Homepage : https://profiles.auckland.ac.nz/charlie-ruffman ORCID: orcid.org/0000-0002-3595-634X Education : 2018 BSc(Hons) Chemistry, University of Otago (New Zealand) 2021 PhD Chemistry, University of Otago (New Zealand), Supervisors: Associate Professor Anna Garden, Professor Sally Brooker 2022 Rutherford Postdoctoral Fellow, University of Auckland (New Zealand), Supervisor: Professor Nicola Gaston Research: Catalysis, liquid metals, density functional theory, ab-initio molecular dynamics Hobbies: Trail running, rock climbing, board games The author presented on this page has published his first article as a submitting corresponding author in Angewandte Chemie: “An Atomic-scale Explanation For The High Selectivity Towards Carbon Dioxide Reduction Observed On Liquid Metal Catalysts ”: C. Ruffman, K. G. Steenbergen, N. Gaston, Angew.
Article Title: Machine learning for the advancement of membrane science and technology: A critical review
Article Snippet: ML G. Ignacz et al. Journal of Membrane Science 713 (2025) 123256 Abbreviations Abbreviation & Definition AI Artificial Intelligence AIMD Ab-initio Molecular Dynamics ANNs Artificial Neural Networks AUC Area Under the Curve BT Boosted Tree CGMD Coarse-Grained Molecular Dynamics CNN Convolutional Neural Network COF Covalent Organic Frameworks CoRE-MOF Computation-Ready Experiment Metal–Organic Framework DFT Density Functional Theory DL Deep Learning DNN Deep Neural Network DTs Decision Trees EDSR Enhanced Deep Residual Networks FAIR Findability, Accessibility, Interoperability, Reuse FEM Finite Element Method FF Force Field FVV Fractional Free Volume GA Genetic Algorithm GAN Generative Adversarial Network GBM Gradient Boosting Machine GNN Graph Neural Network GPR Gaussian Process Regression HF Hartree–Fock HSP Hansen Solubility Parameter HTVS High-Throughput Virtual Screening InChI Keys International Chemical Identifier Keys kNN k-Nearest Neighbors Lasso Least Absolute Shrinkage and Selection Operator MACCS Keys Molecular ACCess System Keys Fingerprint MAE Mean Absolute Error MC Monte Carlo MD Molecular Dynamics MEA Membrane Electrode Assembly MF Microfiltration ML Machine Learning MLPANN Multilayer Perceptron ANN MMM Mixed Matrix Membrane MOF Metal–Organic Framework OC20 Open Catalysts Challenge 2020 OC22 Open Catalysts Challenge 2022 OEM Original Equipment Manufacturing OMD Open Membrane Database OMG Open Macromolecular Genome OpenDAC Open Direct Air Capture OSN Organic Solvent Nanofiltration OSRO Organic Solvent Reverse Osmosis PAT Process Analytical Technologies PEMFCs Proton Exchange Membrane Fuel Cells PES Polyethersulfone PIM Polymer of Intrinsic Microporosity PIMS Polymers of Intrinsic Microporosity PS Polysulfone PSO Particle Swarm Optimization PVDF Polyvinylidene Fluoride QM Quantum Mechanics QM/MM Quantum Mechanics/Molecular Mechanics QSAR Quantitative Structure–Activity Relationship QSPR Quantitative Structure–Property Relationship R2 Coefficient of Determination reaxFF Reactive Force Field RL Reinforcement Learning RMSE Root Mean Squared Error RNN-LSTM Recurrent Neural Networks and Long ShortTerm Memory RO Reverse Osmosis ROC Receiver Operating Characteristic RSM Response Surface Methodology SEM Scanning Electron Microscopy SMILES Simplified Molecular Input Line Entry System SRNF Solvent-Resistant Nanofiltration SVMs Support Vector Machines TFN Thin Film Nanocomposite UF Ultrafiltration VAE Variational Autoencoder WWTPs Wastewater Treatment Plants XGBoost Extreme Gradient Boosting ZIF Zeolitic Imidazolate Frameworks algorithms are particularly effective in solving hyperdimensional problems [9–11].
Article Title: Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study.
Article Snippet: Get e-Alerts CONDENSED MATTER, INTERFACES, AND MATERIALS | February 20, 2025 Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study , , and Journal of Chemical Theory and Computation Cite this: J. Chem.. Theory Comput.. 2025, 21, 5, 2582–2597 https://doi.org/10.1021/acs.jctc.4c01372 Copyright © 2025 American Chemical Society Request reuse permissions Cite Share Jump to Shakti Singh* Manan Dholakia* Sharat Chandra* Open PDF Supporting Information (1) Article Views 173 Altmetric Citations Learn about these metrics Published February 20, 2025 5/29/25, 12:56 PM Medium-Range Order in Iron Phosphate Glass Models Obtained Using Various Randomization Techniques: A Molecular Dynamics Study... https://pubs.acs.org/doi/10.1021/acs.jctc.4c01372 2/33 Glasses are known to have medium-range order (MRO), but their link to any experimentally measurable quantity is still ambiguous.
Article Title: Fabrication of core–shell Fe3O4@SiO2/graphite catalyst to improve the charge separation for enhanced photocatalytic toluene oxidation
Article Snippet: Magnetite (Fe3O4) has been regarded as a potential photocatalyst for VOCs degradation.. Nevertheless, the magnetism of Fe3O4 always leads to the aggregation of particles, which is adverse for gaseous VOCs degradation.. Meanwhile, the fast charge recombination of Fe3O4 seriously limits the photocatalytic activity.
Article Title: Supporting Information for Intrinsic Tensile Ductility in Strain Hardening Multi-Principal Element Metallic Glass
Article Snippet: AIMD: Ab-initio Molecular Dynamics, and nnMD: neutral network molecular dynamics.
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