Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

Singh, Prashant; Del Rose, Tyler; Vazquez, Guillermo; Arroyave, Raymundo; Mudryk, Yaroslav

doi:10.1016/j.actamat.2022.117759

Title: Machine-learning enabled thermodynamic model for the design of new rare-earth compounds

Journal Article · Wed Feb 23 00:00:00 EST 2022 · Acta Materialia

DOI:https://doi.org/10.1016/j.actamat.2022.117759· OSTI ID:1856819

^[1];

^[1]; Vazquez, Guillermo ^[2]; Arroyave, Raymundo ^[2];

^[1]

Ames Lab., and Iowa State Univ., Ames, IA (United States)
Texas A & M Univ., College Station, TX (United States)

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been sparse due to limited availability of reliable datasets. In this work, we developed an ‘in-house’ rare-earth database with more than 600 + compounds, each entry was populated with formation enthalpy and related atomic features using high-throughput density-functional theory (DFT). The SISSO (sure independence screening and sparsifying operator) based machine-learning method with meaningful atomic features was used for training and testing the formation enthalpies of rare earth compounds. The complex lattice function coupled with the machine-learning model was used to explore the effect of transition metal alloying on the energy stability of Ce based cubic Laves phases (MgCu₂ type). The SISSO predictions show good agreement with high-fidelity DFT calculations and X-ray powder diffraction measurements. Our study provides quantitative guidance for compositional considerations within a machine-learning model and discovering new metastable materials. The electronic-structure of Ce-Fe-Cu based compound was also analyzed to get an in-depth understanding of the electronic origin of phase stability. The interpretable analytical models in combination with density-functional theory and experiments provide a fast and reliable design guide for discovering technologically useful materials.

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Research Organization:: Ames Lab., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-07CH11358

OSTI ID:: 1856819

Alternate ID(s):: OSTI ID: 1856841

Report Number(s):: IS-J 10,753; IS-J 10,735

Journal Information:: Acta Materialia, Vol. 229; ISSN 1359-6454

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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