Atomistic learning in the electronically grand-canonical ensemble  

作　　者：Xi Chen Muammar El Khatib Per Lindgren Adam Willard Andrew J.Medford Andrew A.Peterson 

机构地区：[1]School of Engineering,Brown University,Providence,RI 02912,USA [2]Department of Chemistry,Massachussetts Institute of Technology,Cambridge,MA,USA [3]School of Chemical and Biomolecular Engineering,Georgia Institute of Technology,Atlanta,GA 30318,USA

出　　处：《npj Computational Materials》2023年第1期1619-1627,共9页计算材料学（英文）

基　　金：The authors acknowledge support from the U.S.Department of Energy under Award DE-SC0019441;the National Science Foundation under award 1553365.Calculations were undertaken at Brown University’s Center for Computation and Visualization.

摘　　要：A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble.The approach relies upon a dual-learning scheme,where both the system charge and the system energy are predicted for each image.The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials,and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty.The method is also demonstrated to accelerate saddle-point searches,and to extrapolate to systems with one to five water layers.We anticipate that this method will allow for larger length-and time-scale simulations necessary for electrochemical simulations.

关 键 词：CANONICAL ENSEMBLE ELECTRONIC 

分 类 号：O562[理学—原子与分子物理]