摘要
Artificial neural network(ANN)potentials enable accurate atomistic simulations of complex materials at unprecedented scales,but training them for potential energy surfaces(PES)of diverse chemical environments remains computationally intensive,especially when the PES gradients are trained on atomic force data.Here,we present an efficient methodology incorporating forces intoANNtraining by translating them to synthetic energy data using Gaussian process regression(GPR),leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training.We evaluated the method on hybrid density-functional theory data for ethylene carbonate(EC)molecules and their interfaces with Li metal,which are relevant for Li-metal batteries.The GPR-ANN potentials achieved an accuracy comparable to fully force-trained ANN potentials with a significantly reduced computational and memory overhead,establishing the method as a powerful and scalable framework for constructing high-fidelity ANN potentials for complex materials systems.
基金
support by the Columbia Center for Computational Electrochemistry(CCCE)and computing resources from Columbia University’s Shared Research Computing Facility
J.L.Z.and N.A.thank the Project HPC-EUROPA3(Grant No.INFRAIA-2016-1-730897)for its support,provided through the EC Research and Innovation Action under the H2020 Program
N.A.also acknowledges support by a start-up grant(Dutch Sector Plan)from Utrecht University.The authors gratefully acknowledge discussions with JoséA.Garrido Torres and Thomas Bligaard.
