2 papers
cs.AI2026
An Ontology for Machine Learning Interatomic Potentials
Daniel Hernández, Jong Hyun Jung, Yuji Ikeda +11
Machine learning interatomic potentials (MLIPs) approximate quantum-mechanical energies and forces---conventionally computed by density functional theory (DFT) or wave-function met…
cond-mat.mtrl-sci2024
Accelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW
Li-Fang Zhu, Fritz Koermann, Qing Chen +3
Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these pr…