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EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
March 27, 2024, 4:41 a.m. | Bangchen Yin, Yue Yin, Yuda W. Tang, Hai Xiao
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields. However, the abundance of MLFF models and the challenge of accurately predicting atomic forces pose significant obstacles in their practical application. In this paper, we propose a novel ensemble learning framework, EL-MLFFs, which leverages the stacking method to integrate predictions from diverse MLFFs and enhance force prediction accuracy. By …
abstract accuracy application arxiv bridge challenge cs.lg efficiency ensemble fields however machine machine learning obstacles physics.chem-ph practical quantum type
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