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Machine-learned interatomic potentials: Recent developments and prospective applications

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Machine-learned interatomic potentials: Recent developments and prospective applications

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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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Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Machine learning for metallurgy I. A neural-network potential for Al-Cu

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

Multipole moments for embedding potentials: Exploring different atomic allocation algorithms

The role of atomistic simulations in probing hydrogen effects on plasticity and embrittlement in metals

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