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/SiO₂/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 SiO₂ atomic structures with a degree of fidelity approaching that of DFT.

由于其技术重要性，硅及其氧化物在理论研究中得到了广泛的探索。考虑到所涉及的电荷转移，在不使用从头计算方法的情况下同时描述 Si 和 SiO ₂内的原子间相互作用被认为具有挑战性。在此，通过开发基于矩张量势（MTP）框架的描述Si/SiO ₂ /O系统的统一机器学习原子间势来克服这一挑战。该 MTP 使用密度泛函理论模拟生成的综合数据库进行训练，涵盖不同的晶体结构、点缺陷、扩展缺陷和无序结构。对 MTP 进行了广泛的测试，表明它可以描述非常不同的 Si、O 和 SiO ₂原子结构的静态和动态特征，其保真度接近 DFT。