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Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys fromab initiotrained machine-learning potentials
Physical Review Materials ( IF 3.1 ) Pub Date : 2021-07-08 , DOI: 10.1103/physrevmaterials.5.073801 Konstantin Gubaev 1, 2 , Yuji Ikeda 2 , Ferenc Tasnádi 3 , Jörg Neugebauer 4 , Alexander V. Shapeev 5 , Blazej Grabowski 2 , Fritz Körmann 1, 4
Physical Review Materials ( IF 3.1 ) Pub Date : 2021-07-08 , DOI: 10.1103/physrevmaterials.5.073801 Konstantin Gubaev 1, 2 , Yuji Ikeda 2 , Ferenc Tasnádi 3 , Jörg Neugebauer 4 , Alexander V. Shapeev 5 , Blazej Grabowski 2 , Fritz Körmann 1, 4
Affiliation
An active learning approach to train machine-learning interatomic potentials (moment tensor potentials) for multicomponent alloys to ab initio data is presented. Employing this approach, the disordered body-centered cubic (bcc) system with varying Ta concentration is investigated via molecular dynamics simulations. Our results show a strong interplay between elastic properties and the structural phase stability, strongly affecting the mechanical properties. Based on these insights we systematically screen composition space for regimes where elastic constants show little or no temperature dependence (elinvar effect).
中文翻译:
来自ab initiotrained机器学习潜力的bcc多组分合金的结构稳定性、化学复杂性和弹性特性的有限温度相互作用
提出了一种主动学习方法,用于将多组分合金的机器学习原子间势(矩张量势)训练为ab initio数据。采用这种方法,无序体心立方 (bcc)通过分子动力学模拟研究了具有不同 Ta 浓度的系统。我们的结果表明弹性性能和结构之间有很强的相互作用相稳定性,强烈影响机械性能。基于这些见解,我们系统地筛选弹性常数显示很少或没有温度依赖性(elinvar 效应)的区域的组成空间。
更新日期:2021-07-08
中文翻译:
来自ab initiotrained机器学习潜力的bcc多组分合金的结构稳定性、化学复杂性和弹性特性的有限温度相互作用
提出了一种主动学习方法,用于将多组分合金的机器学习原子间势(矩张量势)训练为ab initio数据。采用这种方法,无序体心立方 (bcc)通过分子动力学模拟研究了具有不同 Ta 浓度的系统。我们的结果表明弹性性能和结构之间有很强的相互作用相稳定性,强烈影响机械性能。基于这些见解,我们系统地筛选弹性常数显示很少或没有温度依赖性(elinvar 效应)的区域的组成空间。