Understanding material surfaces and interfaces is vital in applications like
catalysis or electronics. Ab initio simulations, combining energies from
electronic structure with statistical mechanics, can, in principle, predict the
structure of material surfaces as a function of thermodynamic variables.
However, accurate energy simulations are prohibitive when coupled to the vast
phase space that must be statistically sampled. Here, we present a bi-faceted
computational loop to predict surface phase diagrams of multi-component
materials that accelerates both the energy scoring and statistical sampling
methods. Fast, scalable, and data-efficient machine learning interatomic
potentials are trained on high-throughput density-functional theory
calculations through closed-loop active learning. Markov-chain Monte Carlo
sampling in the semi-grand canonical ensemble is enabled by using virtual
surface sites. The predicted surfaces for GaN(0001) and SrTiO3(001) are in
agreement with past work and suggest that the proposed strategy can model
complex material surfaces and discover previously unreported surface
terminations.

使用机器学习插值位能函数和 Markov-chain Monte Carlo 采样方法，加快了多组分材料表面相图预测中的能量和统计抽样方法，可模拟复杂的材料表面并发现以前未报告的表面终止。