71 citations · 126 across the 4 of their papers we have counts for
8 papers
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…
Quantum-mechanical exploration of the phase diagram of water
Aleks Reinhardt, Bingqing Cheng
The phase diagram of water harbours many mysteries: some of the phase boundaries are fuzzy, and the set of known stable phases may not be complete. Starting from liquid water and a…
Extracting ice phases from liquid water: why a machine-learning water model generalizes so well
Bartomeu Monserrat, Jan Gerit Brandenburg, Edgar A. Engel +1
We investigate the structural similarities between liquid water and 53 ices, including 20 knowncrystalline phases. We base such similarity comparison on the local environments that…
Computing the heat conductivity of fluids from density fluctuations
Bingqing Cheng, Daan Frenkel
Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the…
Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification
Bingqing Cheng, Michele Ceriotti, Gareth A. Tribello
Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometime…
Predicting the phase diagram of titanium dioxide with random search and pattern recognition
Aleks Reinhardt, Chris J. Pickard, Bingqing Cheng
Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searchin…