Machine learning for alloys

• Published in: Nature reviews. Materials Vol. 6; no. 8; pp. 730 - 755
• Main Authors: Hart, Gus L. W., Mueller, Tim, Toher, Cormac, Curtarolo, Stefano
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.08.2021; Nature Publishing Group
• Subjects: 639/301/1034/1035; 639/301/1034/1037; Alloys; Amorphous materials; Biomaterials; Catalysts; Chemistry and Materials Science; Condensed Matter Physics; High entropy alloys; Machine learning; Magnetic properties; Magnets; Materials Science; Metallic glasses; Metallurgy; Nanotechnology; Optical and Electronic Materials; Review Article; Shape memory alloys; Superalloys; Thermodynamic properties
• ISSN: 2058-8437; 2058-8437
• DOI: 10.1038/s41578-021-00340-w

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and enhanced data generation have created a fertile ground for computational materials science. Pairing machine learning and alloys has proven to be particularly instrumental in pushing progress in a wide variety of materials, including metallic glasses, high-entropy alloys, shape-memory alloys, magnets, superalloys, catalysts and structural materials. This Review examines the present state of machine-learning-driven alloy research, discusses the approaches and applications in the field and summarizes theoretical predictions and experimental validations. We foresee that the partnership between machine learning and alloys will lead to the design of new and improved systems. Machine learning is enabling a metallurgical renaissance. This Review discusses recent progress in representations, descriptors and interatomic potentials, overviewing metallic glasses, high-entropy alloys, superalloys and shape-memory alloys, magnets and catalysts, and the prediction of mechanical and thermal properties.