.png){kind=icon}
Return
Machine-learning-accelerated simulations to enable automatic surface reconstruction
.png){kind=icon}
PRE
AI
DOI：10.1038/s43588-023-00571-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. VSSR-MC is a Markov chain method based on virtual adsorption sites that interfaces with a neural network force field to provide fast, accurate and comprehensive sampling of material surfaces.
Keywords:
TOTAL-ENERGY CALCULATIONS
STRUCTURE SEARCH
NI
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSave
View more