Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules

Zongrui Pei, Junqi Yin, Jeffrey A. Hawk, David E. Alman, Michael C. Gao

Research output: Contribution to journalArticlepeer-review

139 Scopus citations

Abstract

The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, such as molar volume, bulk modulus, and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate (73%) but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks, each of which consists of 9 elements. The predictions encompass face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal closest packed (HCP) structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram (CALPHAD) calculations with high consistency (94%). Since the new thermodynamics-based rule employs only elemental properties, applicability in screening for solid solution high-entropy alloys is straightforward and efficient.

Original languageEnglish
Article number50
Journalnpj Computational Materials
Volume6
Issue number1
DOIs
StatePublished - Dec 1 2020

Funding

This work was performed in support of the US Department of Energy’s Fossil Energy Crosscutting Technology Research Program. The Research was executed through the NETL Research and Innovation Centers Advanced Alloy Development Field Work Proposal. Research performed by Leidos Research Support Team staff was conducted under the RSS contract 89243318CFE000003. This research was supported in part by an appointment to the U.S. Department of Energy (DOE) Postgraduate Research Program at the National Energy Technology Laboratory (NETL) administered by the Oak Ridge Institute for Science and Education. This research used resources of Oak Ridge National Laboratory’s Compute and Data Environment for Science (CADES) and the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Neither the United States Government nor any agency thereof, nor any of their employees, nor Leidos Research Support Team (LRST), nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.

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