Magnetothermal Properties with Sampled Effective Local Field Estimation

TL;DR

This paper presents a novel first-principles method called Sampled Effective Local Field Estimation for predicting magnetothermal properties of materials, significantly improving efficiency and accuracy, and enabling faster materials discovery.

Contribution

The paper introduces a scalable, automated approach that enhances sample efficiency by over two orders of magnitude for predicting magnetothermal properties, validated against experimental data.

Findings

Achieves over 100x sample efficiency improvement.

Shows excellent agreement with experimental measurements.

Suitable for high-throughput materials discovery workflows.

Abstract

We introduce a first-principles method for predicting the magnetothermal properties of solid-state materials, which we call Sampled Effective Local Field Estimation. This approach achieves over two orders of magnitude improvement in sample efficiency compared to current state-of-the-art methods, as demonstrated on representative material systems. We validate our predictions against experimental data for well-characterized magnetic materials, showing excellent agreement. The method is fully automated and requires minimal computational resources, making it well suited for integration into high-throughput materials discovery workflows. Our method offers a scalable and accurate predictive framework that can accelerate the design of next-generation materials for magnetic refrigeration, cryogenic cooling, and magnetic memory technologies.