journal article·2024-01-01Open access

Composition-based phase stability model for multicomponent metal alloys

Jay C. Spendlove, Bryan H. Fong, John H. Martin, Mark R. O’Masta, Andrew Pan, Tobias A. Schaedler, Eric B. Isaacs

AIP Advances· Volume 14· Issue 1

Abstract

The vastness of the space of possible multicomponent metal alloys is hoped to provide improved structural materials but also challenges traditional, low-throughput materials design efforts. Computational screening could narrow this search space if models for materials stability and desired properties exist that are sufficiently inexpensive and accurate to efficiently guide experiments. Toward this effort, here we develop a method to rapidly assess the thermodynamic stability of a metal alloy composition of an arbitrary number of elements, stoichiometry, and temperature based on density functional theory (DFT) data. In our model, the Gibbs free energy of the solid solution contains binary enthalpy contributions and ideal configurational entropy, whereas only enthalpy is considered for intermetallic competing phases. Compared to a past model for predicting the formation of single-phase high-entropy alloys [M. C. Troparevsky et al., Phys. Rev. X 5, 011041 (2015)], our method is similarly inexpensive, since it assesses enthalpies based on existing DFT data, but less heuristic, more broadly applicable, and more accurate (70%–75%) compared to experiment.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: High Entropy Alloys Studies · High-Temperature Coating Behaviors · Chalcogenide Semiconductor Thin Films

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

HRL Laboratories, LLC , 3011 Malibu Canyon Road, Malibu, California 90265, USA

Funding

HRL Laboratories, LLC

References (44)

Show source-provided references
1. A critical review of high entropy alloys and related concepts10.1016/j.actamat.2016.08.081 ↗
2. High-entropy alloys10.1038/s41578-019-0121-4 ↗
3. Frequency estimate for multicomponent crystalline compounds10.1007/s10955-016-1680-z ↗
4. High-throughput synthesis of Mo-Nb-Ta-W high-entropy alloys via additive manufacturing10.1016/j.matdes.2019.108358 ↗
5. High-throughput additive manufacturing and characterization of refractory high entropy alloys10.1016/j.apmt.2020.100560 ↗
6. High-throughput rapid experimental alloy development (HT-READ)10.1016/j.actamat.2021.117352 ↗
7. Emerging capabilities for the high-throughput characterization of structural materials10.1146/annurev-matsci-080619-022100 ↗
8. High throughput discovery of lightweight corrosion-resistant compositionally complex alloys10.1007/s44210-023-00020-0 ↗
9. Design of high-strength refractory complex solid-solution alloys10.1038/s41524-018-0072-0 ↗
10. The evolving landscape for alloy design10.1557/mrs.2019.69 ↗
11. Progress and challenges in the theory and modeling of complex concentrated alloys10.1557/s43577-022-00306-5 ↗
12. Design of refractory multi-principal-element alloys for high-temperature applications10.1038/s41524-023-01095-4 ↗
13. Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy10.1038/s41467-023-38111-6 ↗
14. High entropy alloys: A focused review of mechanical properties and deformation mechanisms10.1016/j.actamat.2019.12.015 ↗
15. Controlling the corrosion resistance of multi-principal element alloys10.1016/j.scriptamat.2020.06.065 ↗
16. C. Zeng , A.Neils, J.Lesko, and N.Post, “Machine learning accelerated discovery of corrosion-resistant high-entropy alloys,” arXiv:2307.06384 [cond-mat] (2023).10.1016/j.commatsci.2024.112925 ↗
17. More than entropy in high-entropy alloys: Forming solid solutions or amorphous phase10.1016/j.intermet.2013.05.002 ↗
18. Guidelines in predicting phase formation of high-entropy alloys10.1557/mrc.2014.11 ↗
19. Thermodynamics of concentrated solid solution alloys10.1016/j.cossms.2017.08.001 ↗
20. Equilibrium high entropy alloy phase stability from experiments and thermodynamic modeling10.1016/j.scriptamat.2017.10.027 ↗
21. Machine-learning model for predicting phase formations of high-entropy alloys10.1103/physrevmaterials.3.095005 ↗
22. Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules10.1038/s41524-020-0308-7 ↗
23. Insights on phase formation from thermodynamic calculations and machine learning of 2436 experimentally measured high entropy alloys10.1016/j.jallcom.2022.165173 ↗
24. A deep neural network regressor for phase constitution estimation in the high entropy alloy system Al-Co-Cr-Fe-Mn-Nb-Ni10.1038/s41524-023-01021-8 ↗
25. First-principles prediction of high-entropy-alloy stability10.1038/s41524-017-0049-4 ↗
26. The search for high entropy alloys: A high-throughput ab-initio approach10.1016/j.actamat.2018.07.042 ↗
27. Ab initio phase stabilities and mechanical properties of multicomponent alloys: A comprehensive review for high entropy alloys and compositionally complex alloys10.1016/j.matchar.2018.06.019 ↗
28. Multi-cell Monte Carlo method for phase prediction10.1038/s41524-019-0259-z ↗
29. Criteria for predicting the formation of single-phase high-entropy alloys10.1103/physrevx.5.011041 ↗
30. The open quantum materials database (OQMD): Assessing the accuracy of DFT formation energies10.1038/npjcompumats.2015.10 ↗
31. Reflections on one million compounds in the open quantum materials database (OQMD)10.1088/2515-7639/ac7ba9 ↗
32. First-principles study of vibrational entropy effects on the PbTe-SrTe phase diagram10.1103/physrevmaterials.2.095402 ↗
33. Efficient ab initio modeling of random multicomponent alloys10.1103/physrevlett.116.105501 ↗
34. Algebraic representation of thermodynamic properties and the classification of solutions10.1021/ie50458a036 ↗
35. Zur Berechnung der thermodynamischen Daten eines ternären Systems aus den zugehörigen binären Systemen10.1007/bf00899814 ↗
36. Enthalpies de formation des alliages liquides bismuth-étain-gallium à 723 k. Choix d’une représentation analytique des grandeurs d’excès intégrales et partielles de mélange10.1051/jcp/1975720083 ↗
37. Empirical methods of predicting and representing thermodynamic properties of ternary solution phases10.1016/0364-5916(80)90016-4 ↗
38. 2007
39. Combinatorial screening for new materials in unconstrained composition space with machine learning10.1103/physrevb.89.094104 ↗
40. The CALPHAD method and its role in material and process development10.4322/2176-1523.1059 ↗
41. The quickhull algorithm for convex hulls10.1145/235815.235821 ↗
42. Predicting the formation and stability of single phase high-entropy alloys10.1016/j.actamat.2015.11.040 ↗
43. Expanded dataset of mechanical properties and observed phases of multi-principal element alloys10.1038/s41597-020-00768-9 ↗
44. Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD10.1103/physrevmaterials.7.053805 ↗