Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals

In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In t...

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Bibliographic Details
Published in:Physical review. B 2018-09, Vol.98 (9), p.094104, Article 094104
Main Authors: Li, Xiang-Guo, Hu, Chongze, Chen, Chi, Deng, Zhi, Luo, Jian, Ong, Shyue Ping
Format: Article
Language:English
Subjects:
Accuracy
Alloy systems
Alloys
Binary alloys
Binary systems
Computer simulation
CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Copper
Density functional theory
Elastic properties
Embedded atom method
Free energy
Heat of formation
Intermetallic compounds
Machine learning
Mathematical models
Melting points
Molybdenum
Nickel
Permutations
Phase diagrams
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Summary:In recent years, efficient interatomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, for translation, rotation, permutation of homonuclear atoms, among others. In this paper, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over the well-established high-performing embedded atom method (EAM) and modified EAM potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of a Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties, such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This paper provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyperparameters in the model fitting, and paves the way for long-time large-scale simulations of such systems.
ISSN:2469-9950
2469-9969
DOI:10.1103/PhysRevB.98.094104