Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data.

Autor:	Witman MD; Sandia National Laboratories, Livermore, California 94551-0969, United States., Bartelt NC; Sandia National Laboratories, Livermore, California 94551-0969, United States., Ling S; Advanced Materials Research Group, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, U.K., Guan PW; Sandia National Laboratories, Livermore, California 94551-0969, United States., Way L; Sandia National Laboratories, Livermore, California 94551-0969, United States., Allendorf MD; Sandia National Laboratories, Livermore, California 94551-0969, United States., Stavila V; Sandia National Laboratories, Livermore, California 94551-0969, United States.
Jazyk:	angličtina
Zdroj:	The journal of physical chemistry letters [J Phys Chem Lett] 2024 Feb 08; Vol. 15 (5), pp. 1500-1506. Date of Electronic Publication: 2024 Feb 01.
DOI:	10.1021/acs.jpclett.3c03369
Abstrakt:	Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.
Databáze:	MEDLINE
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