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

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

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

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

Incompleteness of Atomic Structure Representations

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Electrical impedance tomography: Amplitudes of cardiac related impedance changes in the lung are highly position dependent

Molecular quantum mechanical gradients within the polarizable embedding approach-Application to the internal vibrational Stark shift of acetophenone

Minimizing Density Functional Failures for Non-covalent Interactions beyond van der Waals Complexes

How Important is Self-Consistency for the dDsC Density Dependent Dispersion Correction?

Minimizing Density Functional Failures for Non-covalent Interactions beyond van der Waals Complexes

Molecular quantum mechanical gradients within the polarizable embedding approach-Application to the internal vibrational Stark shift of acetophenone

How Important is Self-Consistency for the dDsC Density Dependent Dispersion Correction?

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

Incompleteness of Atomic Structure Representations

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

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Electrical impedance tomography: Amplitudes of cardiac related impedance changes in the lung are highly position dependent

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