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

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 de-veloped 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 pow-der 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.(C) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

In this study, a descriptor-based machine-learning approach was employed to assess the effect of chemical alloying on the formation enthalpy of rare-earth intermetallics. A rare-earth database was developed using high-throughput density-functional theory (DFT) with more than 600 compounds. The machine-learning method based on SISSO was used to train and test the formation enthalpies, and the complex lattice function was used to investigate the effect of transition metal alloying on energy stability. The study provides quantitative guidance for compositional considerations and facilitates the discovery of new metastable materials.