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12/24/2022
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Kernel machines have sustained continuous progress in the field of quant...
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08/25/2022
Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields
Reconstructing force fields (FF) from atomistic simulation data is a cha...
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06/08/2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Machine-learning force fields (MLFF) should be accurate, computationally...
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06/04/2021
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
Attention mechanisms are developing into a viable alternative to convolu...
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05/01/2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
In recent years, machine-learned force fields (ML-FFs) have gained incre...
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10/14/2020
Machine Learning Force Fields
In recent years, the use of Machine Learning (ML) in computational chemi...
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05/04/2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Gradient-domain machine learning (GDML) is an accurate and efficient app...
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06/26/2017