Magnetothermal Properties with Sampled Effective Local Field Estimation

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.