Farnell Mackinzie S, McClure Zachary D, Tripathi Shivam, Strachan Alejandro
School of Materials Science and Engineering, University of California Berkeley, Berkeley, California 94720, USA.
School of Materials Engineering and Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907, USA.
J Chem Phys. 2022 Mar 21;156(11):114102. doi: 10.1063/5.0076584.
Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training.
复杂浓缩合金（CCAs）因其一系列理想性能，包括高温强度和耐辐射损伤能力，而在一系列应用中备受关注。它们的多主元成分性质导致大量可能的原子环境，伴随着化学和结构方面的相关变异性。这种原子级别的变异性是这些合金独特性能的核心，但也使其建模具有挑战性。我们将使用多体势的原子模拟与机器学习相结合，来开发基于CrFeCoNiCu的CCAs各种原子性质的预测模型：弛豫空位形成能、原子级内聚能、压力和体积。通过结合与局部原子几何形状相关的不变量和所涉及原子的元素周期表信息，获得局部原子环境的指纹。重要的是，所有描述符都基于未弛豫的原子结构；因此，计算它们的成本较低。这使得能够将这些模型纳入宏观模拟。这些模型显示出良好的准确性，并且我们探索了它们外推到训练期间未使用的成分和元素的能力。
