Unit

Laboratory of Computational Science and Modelling

Laboratory

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Laboratory of Computational Science and Modelling

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

Iterative Unbiasing of Quasi-Equilibrium Sampling

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Machine learning for metallurgy II. A neural-network potential for magnesium

Incompleteness of Atomic Structure Representations

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

Recursive evaluation and iterative contraction of N-body equivariant features

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

From a week to less than a day: Speedup and scaling of coordinate-scaled exact exchange calculations in plane waves

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

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Iterative Unbiasing of Quasi-Equilibrium Sampling

Machine learning for metallurgy II. A neural-network potential for magnesium

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

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

From a week to less than a day: Speedup and scaling of coordinate-scaled exact exchange calculations in plane waves

Incompleteness of Atomic Structure Representations

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

Recursive evaluation and iterative contraction of N-body equivariant features