增强势方法：面向谱缺陷基因组的多尺度建模
The Augmented Potential Method: Multiscale Modeling Toward a Spectral Defect Genome
AI总结：
针对低对称缺陷溶质化学建模成本高的问题，提出结合机器学习势与经典势的增强势多尺度方法，构建了规模更大、精度更高的晶界偏聚数据库，为缺陷基因组研究铺路。
摘要：
从第一性原理计算热力学性质的计算成本高昂，因此材料中低对称缺陷处的溶质化学建模历来颇具挑战。本文提出一种名为增强势方法（augmented potential method）的混合多尺度方法，将缺陷位点处通用机器学习势的化学灵活性与近量子精度，与缺陷位点外缓冲区中采用的长程经典势的计算速度相结合。利用该方法，我们快速计算了1050种二元合金对（包括Ag、Al、Au、Cr、Cu、Fe、Mo、Nb、Ni、Pd、Pt、Ta、V、W溶剂）的晶界偏聚能分布，构建了多晶晶界偏聚数据库。该数据库规模约为此前发表的谱汇编的5倍，且精度更高。该方法还能解决其他任何方法都远无法处理的问题，例如处理体心立方（bcc）铁基合金，或随机多晶中复杂的溶质-溶质相互作用。因此，该方法为构建晶体材料的完整缺陷基因组铺平了道路。
英文摘要：
The modeling of solute chemistry at low-symmetry defects in materials is historically challenging, due to the computation cost required to evaluate thermodynamic properties from first principles. Here, we offer a hybrid multiscale approach called the augmented potential method that connects the chemical flexibility and near-quantum accuracy of a universal machine learning potential at the site of the defect, with the computational speed of a long-range classical potential implemented away from the defect site in a buffer zone. The method allows us to rapidly compute distributions of grain boundary segregation energy for 1,050 binary alloy pairs (including Ag, Al, Au, Cr, Cu, Fe, Mo, Nb, Ni, Pd, Pt, Ta and V, W solvent), creating a database for polycrystalline grain boundary segregation. This database is ~5x larger than previously published spectral compilations, and yet has improved accuracy. The approach can also address problems far beyond the reach of any other method, such as handling bcc Fe-based alloys, or the complex solute-solute interactions in random polycrystals. The approach thus paves a pathway toward a complete defect genome in crystalline materials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
