Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
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 …
[HTML][HTML] Perspective: How good is DFT for water?
MJ Gillan, D Alfe, A Michaelides - The Journal of chemical physics, 2016 - pubs.aip.org
Kohn-Sham density functional theory (DFT) has become established as an indispensable
tool for investigating aqueous systems of all kinds, including those important in chemistry …
tool for investigating aqueous systems of all kinds, including those important in chemistry …
The uniform electron gas at warm dense matter conditions
Motivated by the current high interest in the field of warm dense matter research, in this
article we review the uniform electron gas (UEG) at finite temperature and over a broad …
article we review the uniform electron gas (UEG) at finite temperature and over a broad …
Challenges in large scale quantum mechanical calculations
During the past decades, quantum mechanical methods have undergone an amazing
transition from pioneering investigations of experts into a wide range of practical …
transition from pioneering investigations of experts into a wide range of practical …
Hydrogen-bond structure dynamics in bulk water: insights from ab initio simulations with coupled cluster theory
An accurate and efficient ab initio molecular dynamics (AIMD) simulation of liquid water was
made possible using the fragment-based approach (JF Liu, X. He and JZH Zhang, Phys …
made possible using the fragment-based approach (JF Liu, X. He and JZH Zhang, Phys …
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] Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks
In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
Nuclear quantum effect and its temperature dependence in liquid water from random phase approximation via artificial neural network
Y Yao, Y Kanai - The journal of physical chemistry letters, 2021 - ACS Publications
We report structural and dynamical properties of liquid water described by the random
phase approximation (RPA) correlation together with the exact exchange energy (EXX) …
phase approximation (RPA) correlation together with the exact exchange energy (EXX) …
Interatomic force from neural network based variational quantum Monte Carlo
Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology,
and materials science, which have witnessed rapid development in the last couple of years …
and materials science, which have witnessed rapid development in the last couple of years …