A neuroevolution machine learning potential trained on 302 DFT structures predicts tobermorite and C-S-H properties accurately and enables 100k+ atom GPU molecular dynamics.
Advances in atomistic modeling and understanding of drying shrinkage in cementitious materials, Cement andConcreteResearch.148(2021)106536
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A high-efficiency neuroevolution potential for tobermorite and calcium silicate hydrate systems with ab initio accuracy
A neuroevolution machine learning potential trained on 302 DFT structures predicts tobermorite and C-S-H properties accurately and enables 100k+ atom GPU molecular dynamics.