Machine-learning enabled thermodynamic model for the design of new rare-earth compounds
Abstract
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 discoveringmore »
- Authors:
-
- Ames Lab., and Iowa State Univ., Ames, IA (United States)
- Texas A & M Univ., College Station, TX (United States)
- Publication Date:
- Research Org.:
- Ames Lab., Ames, IA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
- OSTI Identifier:
- 1856819
- Alternate Identifier(s):
- OSTI ID: 1856841
- Report Number(s):
- IS-J 10,753; IS-J 10,735
Journal ID: ISSN 1359-6454
- Grant/Contract Number:
- AC02-07CH11358
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Acta Materialia
- Additional Journal Information:
- Journal Volume: 229; Journal ID: ISSN 1359-6454
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; rare-earths; thermodynamic stability; density-functional theory; machine learning; x-ray powder diffraction
Citation Formats
Singh, Prashant, Del Rose, Tyler, Vazquez, Guillermo, Arroyave, Raymundo, and Mudryk, Yaroslav. Machine-learning enabled thermodynamic model for the design of new rare-earth compounds. United States: N. p., 2022.
Web. doi:10.1016/j.actamat.2022.117759.
Singh, Prashant, Del Rose, Tyler, Vazquez, Guillermo, Arroyave, Raymundo, & Mudryk, Yaroslav. Machine-learning enabled thermodynamic model for the design of new rare-earth compounds. United States. https://doi.org/10.1016/j.actamat.2022.117759
Singh, Prashant, Del Rose, Tyler, Vazquez, Guillermo, Arroyave, Raymundo, and Mudryk, Yaroslav. Wed .
"Machine-learning enabled thermodynamic model for the design of new rare-earth compounds". United States. https://doi.org/10.1016/j.actamat.2022.117759. https://www.osti.gov/servlets/purl/1856819.
@article{osti_1856819,
title = {Machine-learning enabled thermodynamic model for the design of new rare-earth compounds},
author = {Singh, Prashant and Del Rose, Tyler and Vazquez, Guillermo and Arroyave, Raymundo and Mudryk, Yaroslav},
abstractNote = {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.},
doi = {10.1016/j.actamat.2022.117759},
journal = {Acta Materialia},
number = ,
volume = 229,
place = {United States},
year = {Wed Feb 23 00:00:00 EST 2022},
month = {Wed Feb 23 00:00:00 EST 2022}
}
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