A combined machine learning and high-energy x-ray diffraction approach to understanding liquid and amorphous metal oxides
G Sivaraman, G Csanyi… - Journal of the Physical …, 2022 - journals.jps.jp
Determining the structure-property relations of liquid and amorphous metal oxides is
challenging, due to their variable short-range order and polyhedral connectivity. To predict …
challenging, due to their variable short-range order and polyhedral connectivity. To predict …
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO.
G Sivaraman, C Benmore - Journal of Physics: Condensed …, 2024 - iopscience.iop.org
Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted
structures derived from atom-atom pair potentials in disordered materials, has been a …
structures derived from atom-atom pair potentials in disordered materials, has been a …
A Unified Machine-Learning Force Field for Sodium and Chlorine in Both Neutral and Ionic States
Using a small-cell active learning approach, we generate a moment tensor potential (MTP)
trained on only 609 configurations, jointly describing solid/liquid Na, gaseous Cl, and …
trained on only 609 configurations, jointly describing solid/liquid Na, gaseous Cl, and …