基准测试由原子团簇展开导出的平均原子势

中文摘要 AI 辅助

平均原子（A-atom）势提供了化学无序合金的平均场描述，并用于预测无短程有序固溶体的性质。此类势通常由现有的原子间势平均而来，因此其精度仅与母体模型相当。精确的原子间势本身难以参数化，且可能需要大型训练数据集。在此，我们对一种最近发展的形式体系进行基准测试，该体系直接从线性原子团簇展开（ACE）计算精确的A-atom势。我们首先将线性ACE拟合到由嵌入原子方法（EAM）势生成的Fe-W数据上。所得的A-atom势重现了由显式随机超胞和由同一EAM势平均得到的传统A-atom势计算的无序相性质。随后，我们将线性ACE拟合到约1500个平均每个结构含7个原子的小型Mo-Nb结构的电子结构计算能量和力上。由此ACE导出的A-atom势重现了特殊准随机结构的DFT弹性常数、晶格参数、混合焓和Bain路径。在三种化学位点基组中，只有占据数基组还重现了合金的DFT表面能。对于Mo-Nb，A-atom势还预测理想溶液熵超过了无序相有限温度自由能中不稳定的振动贡献。这些基准测试表明，当化学位点基组被仔细选择时，无序合金的性质可以从小型训练数据集中恢复。

英文摘要

Average atom (A-atom) potentials provide a mean-field description of a chemically disordered alloy and are used to predict the properties of solid solutions without short-range order. Such potentials are usually averaged from an existing interatomic potential and are therefore only as accurate as the parent model. Accurate interatomic potentials are themselves difficult to parameterize and can require large training datasets. Here we benchmark a recently developed formalism that computes an exact A-atom potential directly from a linear atomic cluster expansion (ACE). We first fit a linear ACE to Fe-W data generated with an embedded atom method (EAM) potential. The resulting A-atom potential reproduces the properties of the disordered phase computed from an explicit random supercell and from a conventional A-atom potential averaged from the same EAM potential. We then fit a linear ACE to energies and forces computed from electronic structure calculations for about 1500 small Mo-Nb structures with an average of 7 atoms per structure. The A-atom potential derived from this ACE reproduces the DFT elastic constants, lattice parameter, mixing enthalpy, and Bain path of special quasirandom structures. Of the three chemical site bases, only the occupation basis also reproduces the DFT surface energies of the alloy. For Mo-Nb, the A-atom potential also predicts that the ideal solution entropy outweighs the destabilizing vibrational contribution to the finite-temperature free energy of the disordered phase. These benchmarks show that the properties of disordered alloys can be recovered from small training datasets when the chemical site basis is chosen carefully.

发表机构

• Laboratory of Materials Design and Simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所材料设计与模拟实验室）

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