# Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark

## TL;DR

This paper introduces a machine learning-accelerated high-throughput workflow for discovering magnetic materials, specifically Heusler alloys, combining transfer learning and validation with density functional theory to improve prediction accuracy and generalization.

## Contribution

It presents a novel ML-HTP workflow for magnetic material discovery using transfer learning and benchmarking of interatomic potentials, advancing high-throughput screening methods.

## Key findings

- High predictive accuracy confirmed by DFT validation
- Effective transfer learning improves model generalization
- Benchmarking shows performance of different uMLIPs

## Abstract

A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline anisotropy energy ($E_{\mathrm{aniso}}$). Structure optimization and evaluation of formation energy and distance to hull convex were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and $E_{\mathrm{aniso}}$ were predicted by eSEM models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential.