129 citations · 188 across the 7 of their papers we have counts for
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cond-mat.mtrl-sci2023★ 2 cited
Understanding defects in amorphous silicon with million-atom simulations and machine learning
Joe D. Morrow, Chinonso Ugwumadu, David A. Drabold +3
The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it part…
cond-mat.mtrl-sci2021★ 24 cited
Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations
Joe D. Morrow, Volker L. Deringer
Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one…