AI 中文总结
研究如何从大规模原子构型中提取原子环境用于机器学习原子间势，通过对多种材料系统测试多种提取技术，提出名为删除的方法，该方法简单且性能优于其他方法。
AI 中文摘要
为通过诸如分子动力学（MD）的原子模拟适当地捕捉大规模材料特征和涌现现象，系统规模可达数亿原子。然而，驱动这些模拟的力场模型通常用密度泛函理论（DFT）参考数据训练，限于约100或1000个原子的相对小构型。为计算感兴趣区域中原子的DFT力，需从较大模拟盒中提取一小部分原子。本文给出从大的体相构型中提取原子环境并将其嵌入适合DFT计算的较小构型的多种技术基准测试。测试了多种材料系统，结果表明一种名为删除的简单方法比其他提取方法性能更优。
英文摘要
In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to hundreds of millions of atoms. However, the force-field models that drive those simulations are generally trained with Density Functional Theory (DFT) reference data, limited to relatively small configurations on the order of 100s or 1000s of atoms. To compute DFT forces on atoms in regions of interest, for example for active-learning or on-the-fly training of interatomic potentials, one needs to extract a small set of atoms from the larger simulation box, and typically work with periodic boundary conditions for DFT. However, methods to select the shape and size of this extracted set of atoms, as well as to generate a potentially necessary passivating envelope, have not been systematically analyzed. In this work, we benchmark several techniques, including a generative diffusion-based artificial intelligence (AI) approach, for extracting atomic environments from large, bulk configurations and embedding them into smaller configurations suitable for DFT calculations with periodic boundary conditions. We test with a diverse set of material systems, which includes amorphous $\mathrm{SiO_2}$, Ta with screw dislocations, and molten C. We demonstrated a notably simple procedure, a method we refer to as deletions, yields superior performance over an array of alternative extraction methods.
Comments31 pages, 13 figures
