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Shinagawa Corporation universal neural network potential pfp
Universal Neural Network Potential Pfp, supplied by Shinagawa Corporation, 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/universal+neural+network+potential+pfp/universal+neural+network+potential/pm38190503-433-24-15
Average 90 stars, based on 1 article reviews
universal neural network potential pfp - by Bioz Stars, 2026-09
90/100 stars

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Article Title: Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations.
Article Snippet: Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems.. Molecular structures, which are commonly identified using order parameters, were recently identified using machine learning (ML), that is, the ML models acquire structural features using labeled crystals or phases via supervised learning.. However, these approaches may not identify unlabeled or unknown structures, such as the imperfect crystal structures observed in nonequilibrium systems and interfaces.



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