Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.06431 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913829486592000 |
|---|---|
| author | Brawand, Nicholas Leclerc, Nima Zumbro, Emiko |
| author_facet | Brawand, Nicholas Leclerc, Nima Zumbro, Emiko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06431 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Magnetothermal Properties with Sampled Effective Local Field Estimation Brawand, Nicholas Leclerc, Nima Zumbro, Emiko Materials Science Statistical Mechanics 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. |
| title | Magnetothermal Properties with Sampled Effective Local Field Estimation |
| topic | Materials Science Statistical Mechanics |
| url | https://arxiv.org/abs/2505.06431 |