论文标题
高吞吐量筛选和优化高渗透合金的机器学习间潜力
Machine learning interatomic potential for high throughput screening and optimization of high-entropy alloys
论文作者
论文摘要
我们已经开发了基于机器学习的Quaternary Monbtaw（R4）和Quinary Monbtatiw（R5）高熵合金（HEAS）的基于机器学习的跨性质潜力（MLIP）。 MLIP启用了各种非e摩尔R4和R5合金的弹性和机械性能的准确高吞吐量计算，使用密度功能理论（DFT）进行时，它们在否则非常耗时。我们证明，MLIP预测的性质与各种测试用例的DFT结果很好地比较，并且与可用的实验数据一致。 MLIP还用于通过指导R4组合物的迭代调整来发现具有有希望的硬度延纹性组合的候选材料，用于对非摩尔R4候选者的高吞吐量优化。我们还使用这种方法来研究Ti浓度对R4弹性和机械性能的影响，从统计学上平均100多个随机结构的性能。使用实验测量的Vickers硬度和模量，对等摩尔R4和R5 HEAS的MLIP预测硬度和大量模量进行了验证。这种方法为使用MLIP进行了新的候选候选者优化开辟了新的途径。
We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurate high throughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases and are consistent with the available experimental data. The MLIPs are also utilized for high throughput optimization of non equimolar R4 candidates by guided iterative tuning of R4 compositions to discover candidate materials with promising hardness-ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. MLIP predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated using experimentally measured Vickers hardness and modulus. This approach opens a new avenue for employing MLIPs for HEA candidate optimization.
