摘要
The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models attractive.However,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations.Here,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using thermalization.Our ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty quantification.We show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom scales.Moreover,such predictions can be carried out using only modest computational resources.
基金
supported by grant DE-SC0023432 funded by the U.S.Department of Energy,Office of Science
This research used resources of the National Energy Research Scientific Computing Center,a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231,using NERSC awards BES-ERCAP0025205,BES-ERCAP0025168,and BESERCAP0028072
SG and SP acknowledge Research Excellence Fund from MTU
ASB acknowledges startup support from the Samueli School Of Engineering at UCLA,as well as funding from UCLA’s Council on Research(COR)Faculty Research Grant
ASB also acknowledges support through a UCLA SoHub seed grant and a Faculty Career Development Award from UCLA’s Office of Equity,Diversity,and Inclusion。
