Thermal conductivity predictions with foundation atomistic models

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Póta, Balázs</dc:creator>
  <dc:creator>Ahlawat, Paramvir</dc:creator>
  <dc:creator>Csányi, Gábor</dc:creator>
  <dc:creator>Simoncelli, Michele</dc:creator>
  <dc:date>2024-10-22</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2024.171</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:2d-4b</dc:identifier>
  <dc:identifier>mcid:2024.171</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2420</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>thermal conductivity</dc:subject>
  <dc:subject>foundation machine learning potential</dc:subject>
  <dc:subject>wigner transport equation</dc:subject>
  <dc:subject>interatomic potential fine-tuning</dc:subject>
  <dc:subject>matbench-discovery</dc:subject>
  <dc:title>Thermal conductivity predictions with foundation atomistic models</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>