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

TL;DR

This paper introduces a machine-learning approach using a large DFT-derived database to predict formation enthalpies of rare-earth intermetallics, aiding the design of new materials with improved stability.

Contribution

It develops an in-house rare-earth compound database and applies SISSO machine learning to accurately predict formation enthalpies, facilitating the discovery of new metastable rare-earth materials.

Findings

SISSO model predictions agree well with DFT and experimental data.

The approach provides quantitative guidance for alloy design.

Electronic structure analysis explains phase stability mechanisms.

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 (MgCu$_{2}$ 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 in$-$depth to understand 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.