820 citations · 12k across the 115 of their papers we have counts for
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Ab initio thermodynamics of liquid and solid water
Bingqing Cheng, Edgar A. Engel, Jörg Behler +2
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously ta…
Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
Felix Musil, Michael J. Willatt, Mikhail A. Langovoy +1
We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties…
i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
Venkat Kapil, Mariana Rossi, Ondrej Marsalek +23
Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomi…
A Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi, David M. Wilkins, Benjamin A. R. Meyer +3
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structu…
Accurate molecular polarizabilities with coupled-cluster theory and machine learning
David M. Wilkins, Andrea Grisafi, Yang Yang +3
The molecular polarizability describes the tendency of a molecule to deform or polarize in response to an applied electric field. As such, this quantity governs key intra- and inte…
Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics
Bingqing Cheng, Christoph Dellago, Michele Ceriotti
Estimating the homogeneous ice nucleation rate from undercooled liquid water is at the same time crucial for understanding many important physical phenomena and technological appli…