2026-06-16T21:10:40Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:2420 2024-10-22T17:03:10Z openaire_data community-mcarchive

Póta, Balázs Simoncelli, Michele Póta, Balázs Ahlawat, Paramvir Csányi, Gábor Simoncelli, Michele 2024-10-22 Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across diverse compounds at reduced computational cost. Hitherto, these models have been benchmarked relying on descriptors based on atoms' interaction energies or harmonic vibrations; their accuracy and efficiency in predicting observable and technologically relevant heat-conduction properties remains unknown. Here, we introduce a framework that leverages foundation models and the Wigner formulation of heat transport to overcome the major bottlenecks of current methods for designing heat-management materials: high cost, limited transferability, or lack of physics awareness. We present the standards needed to achieve first-principles accuracy in conductivity predictions through model's fine-tuning, discussing benchmark metrics and precision/cost trade-offs. We apply our framework to a database of solids with diverse compositions and structures, demonstrating its potential to discover materials for next-gen technologies ranging from thermal insulation to neuromorphic computing. application/zip text/markdown https://doi.org/10.24435/materialscloud:2d-4b oai:materialscloud.org:2420 mcid:2024.171 eng Materials Cloud https://doi.org/10.48550/arXiv.2408.00755 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:fp-a0 info:eu-repo/semantics/openAccess Academic Software Licence ("ASL") https://github.com/MPA2suite/autoWTE/blob/main/LICENSE thermal conductivity foundation machine learning potential wigner transport equation interatomic potential fine-tuning matbench-discovery Thermal conductivity predictions with foundation atomistic models info:eu-repo/semantics/other