摘要
Si and its oxides have been extensively explored in theoretical research due to their technological importance.Simultaneously describing interatomic interactions within both Si and SiO2 without the use of ab initio methods is considered challenging,given the charge transfers involved.Herein,this challenge is overcome by developing a unified machine learning interatomic potentials describing the Si/SiO_(2)/O system,based on themoment tensor potential(MTP)framework.ThisMTPis trained using a comprehensive database generated using density functional theory simulations,encompassing diverse crystal structures,point defects,extended defects,and disordered structure.
基金
Financial support was provided by the Natural Sciences and Engineering Research Council of Canada(NSERC)
the Nuclear Waste Management Organization(NWMO)
the Association canadienne-française pour l’avancement des sciences(ACFAS)
the Canada Research Chair on Sustainable Multifunctional Construction Materials(CRC-2019-00074).
