Zongo Karim, Sun Hao, Ouellet-Plamondon Claudiane, Béland Laurent Karim
Département de génie de la construction, École de technologie supérieure, Université du Québec, Montréal, QC Canada.
Department of Mechanical and Materials Engineering, Queen's university, Kingston, ON Canada.
NPJ Comput Mater. 2024;10(1):218. doi: 10.1038/s41524-024-01390-8. Epub 2024 Sep 13.
Si and its oxides have been extensively explored in theoretical research due to their technological importance. Simultaneously describing interatomic interactions within both Si and SiO 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/O system, based on the moment tensor potential (MTP) framework. This MTP is trained using a comprehensive database generated using density functional theory simulations, encompassing diverse crystal structures, point defects, extended defects, and disordered structure. Extensive testing of the MTP is performed, indicating it can describe static and dynamic features of very diverse Si, O, and SiO atomic structures with a degree of fidelity approaching that of DFT.
由于硅及其氧化物具有重要的技术价值,因此在理论研究中得到了广泛的探索。考虑到其中涉及的电荷转移,在不使用从头算方法的情况下同时描述硅和二氧化硅内部的原子间相互作用被认为具有挑战性。在此,通过基于矩张量势(MTP)框架开发一种统一的机器学习原子间势来描述Si/SiO/O系统,克服了这一挑战。该MTP使用通过密度泛函理论模拟生成的综合数据库进行训练,该数据库涵盖了各种晶体结构、点缺陷、扩展缺陷和无序结构。对MTP进行了广泛的测试,结果表明它能够以接近密度泛函理论(DFT)的保真度描述非常多样的硅、氧和二氧化硅原子结构的静态和动态特征。