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cond-mat.mtrl-sci2025
Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
Vincent G. Fletcher, Albert P. Bartók, Livia B. Pártay
We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a…
cond-mat.mtrl-sci2024
Experiment-driven atomistic materials modeling: A case study combining X-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon
Tigany Zarrouk, Rina Ibragimova, Albert P. Bartók +1
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous confi…