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physics.chem-ph2024
Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
Rina Ibragimova, Mikhail S. Kuklin, Tigany Zarrouk +1
We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these el…
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…