Accurate and Efficient ML Force Fields for Hundreds of Atoms
ORAL
Abstract
In order to faithfully represent non-local interatomic interactions, a molecular force field has to allow interactions between all degrees of freedom, without resorting to localization or other approximations. This has been a challenge for machine learning (ML) based approaches, because even a simple pairwise correlation implies a poor quadratic scaling behavior in the number of atoms, which quickly becomes computationally prohibitive for training. To date, no fully-correlated global ML models exist that are applicable to systems with more than a few dozen atoms.
To overcome this limitation, we develop an efficient iterative, parameter-free solver to train symmetric gradient domain machine learning (sGDML) [Chmiela et al., 2018] potentials for systems with several hundred atoms. Our approach keeps all correlations of this global model intact, allowing the accurate description of complex molecules, materials and molecular assemblies.
To overcome this limitation, we develop an efficient iterative, parameter-free solver to train symmetric gradient domain machine learning (sGDML) [Chmiela et al., 2018] potentials for systems with several hundred atoms. Our approach keeps all correlations of this global model intact, allowing the accurate description of complex molecules, materials and molecular assemblies.
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Presenters
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Stefan Chmiela
- Tech Univ Berlin