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Beyond potentials: Integrated machine learning models for materials

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Beyond potentials: Integrated machine learning models for materials

Graph Chatbot

Chattez avec Graph Search

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

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Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Biomolecular Simulation: a Perspective from High Performance Computing

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Applications of Artificial Intelligence to Computational Chemistry

Atomic scale electronic structure and response in attosecond photoemission delays: A case study using non-centrosymmetric BiTeCI

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Incompleteness of Atomic Structure Representations

Mechanisms of fluorescence quenching in prototypical aggregation-induced emission systems: excited state dynamics with TD-DFTB

Chemical shifts in molecular solids by machine learning