Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN

Fuente: arXiv

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Bibliographic Details
Main Authors:	Pasini, Massimiliano Lupo, Choi, Jong Youl, Mehta, Kshitij, Zhang, Pei, Rogers, David, Bae, Jonghyun, Ibrahim, Khaled Z., Aji, Ashwin M., Schulz, Karl W., Polo, Jorda, Balaprakash, Prasanna
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Physics 68T07, 68T09 C.2.4; I.2.11
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2406.12909