machine-learning-based predictive modeling (Marrone Bio Innovations)
90
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Marrone Bio Innovations
machine-learning-based predictive modeling
Machine Learning Based Predictive Modeling, supplied by Marrone Bio Innovations, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine-learning-based+predictive+modeling/machine+learning+based+predictive+modeling/pm40560148-342-13-12
Average 90 stars, based on 1 article reviews
Machine Learning Based Predictive Modeling, supplied by Marrone Bio Innovations, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/machine-learning-based+predictive+modeling/machine+learning+based+predictive+modeling/pm40560148-342-13-12
Average 90 stars, based on 1 article reviews
machine-learning-based predictive modeling - by Bioz Stars,
2026-09
90/100 stars
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other:Article Title: Predicting Young's Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. Article Snippet: Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales.. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales.. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Article Title: Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review. Article Snippet: There has been a tremendous increase in demand for virgin and postconsumer recycled (PCR) polymers due to their wide range of chemical and physical characteristics.. Despite the numerous potential benefits of using a data-driven approach to polymer design, major hurdles exist in the development of polymer informatics due to the complicated hierarchical polymer structures.. In this review, a brief introduction on virgin polymer structure, PCR polymers, compatibilization of polymers to be recycled, and their characterization using sensor array technologies as well as factors affecting the polymer properties are provided. Article Title: High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning. 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Despite their great potential, the structure and properties of PHA remain unexplored due to their theoretically infinite chemical space. Article Title: Enhancing Copolymer Property Prediction through the Weighted-Chained-SMILES Machine Learning Framework Article Snippet: ACS Omega 2022, 7 (14), 12268−12277. (38) Pilania, G.; Iverson, C. N.; Article Title: Atomistic Understanding of Ion Exchange Strengthening of Boroaluminosilicate Glasses: Insights from Molecular Dynamics Simulations and QSPR Analysis. Article Snippet: Ion exchange (IOX) is an effective and widely used method to enhance mechanical properties of various glass products ranging from the touch screen of consumer electronics to window shields of airplanes and spacecrafts.. IOX or chemical strengthening is achieved through the creation of a compressive surface layer on the glass product.. Although widely studied experimentally, the fundamental understanding of the IOX strengthening process is still limited. Article Title: Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study. Article Snippet: Machine learning (ML) has emerged as a transformative tool in material science, enabling accelerated discovery and design of novel molecules while reducing experimental costs.. Uncertainty quantification (UQ) is crucial for enhancing the reliability of ML predictions, particularly in high-stakes applications, such as functional polymer discovery.. 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In contrast, our paper introduces a novel ML approach that predicts the change of the storage modulus as a function of temperature within the entire glass transition zone for thermoset polymers, thus offering a more comprehensive prediction of this phase transition.. This method differentiates itself by using features across three scales�microscopic, mesoscopic, and macroscopic�as inputs to develop a multiscale fingerprinting technique. |