Machine-learning-accelerated simulations to enable automatic surface reconstruction.
Nat Comput Sci
; 3(12): 1034-1044, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-38177720
ABSTRACT
Understanding material surfaces and interfaces is vital in applications such as catalysis or electronics. By combining energies from electronic structure with statistical mechanics, ab initio simulations 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 multicomponent 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 semigrand canonical ensemble is enabled by using virtual surface sites. The predicted surfaces for GaN(0001), Si(111) and SrTiO3(001) are in agreement with past work and indicate that the proposed strategy can model complex material surfaces and discover previously unreported surface terminations.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
Nat Comput Sci
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos