SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

• URL: http://arxiv.org/abs/2105.00304v1
• Date: Sat, 1 May 2021 17:06:40 GMT
• Title: SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• Authors: Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Sch\"utt, Huziel E. Sauceda, Klaus-Robert M\"uller
• Abstract summary: Machine-learned force fields (ML-FFs) have gained increasing popularity in the field of computational chemistry. This work introduces SpookyNet, a deep neural network for constructing ML-FFs with explicit treatment of electronic degrees of freedom and quantum nonlocality. SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets.
• Score: 1.5845117761091052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, machine-learned force fields (ML-FFs) have gained increasing popularity in the field of computational chemistry. Provided they are trained on appropriate reference data, ML-FFs combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electronic degrees of freedom, such as the total charge or spin, when forming their prediction. In addition, they often assume chemical locality, which can be problematic in cases where nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing ML-FFs with explicit treatment of electronic degrees of freedom and quantum nonlocality. Its predictions are further augmented with physically-motivated corrections to improve the description of long-ranged interactions and nuclear repulsion. SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it can leverage the learned chemical insights, e.g. by predicting unknown spin states or by properly modeling physical limits. Moreover, it is able to generalize across chemical and conformational space and thus close an important remaining gap for today's machine learning models in quantum chemistry.

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