Nuclear quantum effects in water and aqueous systems: Experiment, theory, and current challenges
Nuclear quantum effects influence the structure and dynamics of hydrogen-bonded systems,
such as water, which impacts their observed properties with widely varying magnitudes. This …
such as water, which impacts their observed properties with widely varying magnitudes. This …
G aussian approximation potentials: A brief tutorial introduction
We present a swift walk‐through of our recent work that uses machine learning to fit
interatomic potentials based on quantum mechanical data. We describe our Gaussian …
interatomic potentials based on quantum mechanical data. We describe our Gaussian …
Machine learning a general-purpose interatomic potential for silicon
The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …
chemistry, solid-state physics, and materials science is constrained by the limitations on …
Big data meets quantum chemistry approximations: the Δ-machine learning approach
Chemically accurate and comprehensive studies of the virtual space of all possible
molecules are severely limited by the computational cost of quantum chemistry. We …
molecules are severely limited by the computational cost of quantum chemistry. We …
Modeling polymorphic molecular crystals with electronic structure theory
GJO Beran - Chemical reviews, 2016 - ACS Publications
Interest in molecular crystals has grown thanks to their relevance to pharmaceuticals,
organic semiconductor materials, foods, and many other applications. Electronic structure …
organic semiconductor materials, foods, and many other applications. Electronic structure …
Accuracy and transferability of Gaussian approximation potential models for tungsten
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects
within the Gaussian approximation potential framework, fitted to a database of first-principles …
within the Gaussian approximation potential framework, fitted to a database of first-principles …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Self-interaction error overbinds water clusters but cancels in structural energy differences
We gauge the importance of self-interaction errors in density functional approximations
(DFAs) for the case of water clusters. To this end, we used the Fermi–Löwdin orbital self …
(DFAs) for the case of water clusters. To this end, we used the Fermi–Löwdin orbital self …
Noncovalent interactions by quantum Monte Carlo
Quantum Monte Carlo (QMC) is a family of stochastic methods for solving quantum many-
body problems such as the stationary Schrödinger equation. The review introduces basic …
body problems such as the stationary Schrödinger equation. The review introduces basic …
[HTML][HTML] On the accuracy of van der Waals inclusive density-functional theory exchange-correlation functionals for ice at ambient and high pressures
Density-functional theory (DFT) has been widely used to study water and ice for at least 20
years. However, the reliability of different DFT exchange-correlation (xc) functionals for water …
years. However, the reliability of different DFT exchange-correlation (xc) functionals for water …