Machine learning for electronically excited states of molecules
Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …
as well as photobiology and also play a role in material science. Their theoretical description …
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 …
Schnet–a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …
image search, speech recognition, as well as bioinformatics, with growing impact in …
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
We introduce a scheme for molecular simulations, the deep potential molecular dynamics
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
[HTML][HTML] DFTB+, a software package for efficient approximate density functional theory based atomistic simulations
DFTB+ is a versatile community developed open source software package offering fast and
efficient methods for carrying out atomistic quantum mechanical simulations. By …
efficient methods for carrying out atomistic quantum mechanical simulations. By …
Advanced capabilities for materials modelling with Quantum ESPRESSO
Q uantum ESPRESSO is an integrated suite of open-source computer codes for quantum
simulations of materials using state-of-the-art electronic-structure techniques, based on …
simulations of materials using state-of-the-art electronic-structure techniques, based on …
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Recent developments in many-body potential energy representation via deep learning have
brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular …
brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular …
Promoting transparency and reproducibility in enhanced molecular simulations
Nature methods, 2019 - nature.com
The PLUMED consortium unifies developers and contributors to PLUMED, an open-source
library for enhanced-sampling, free-energy calculations and the analysis of molecular …
library for enhanced-sampling, free-energy calculations and the analysis of molecular …
Machine learning of accurate energy-conserving molecular force fields
Using conservation of energy—a fundamental property of closed classical and quantum
mechanical systems—we develop an efficient gradient-domain machine learning (GDML) …
mechanical systems—we develop an efficient gradient-domain machine learning (GDML) …
Towards exact molecular dynamics simulations with machine-learned force fields
Molecular dynamics (MD) simulations employing classical force fields constitute the
cornerstone of contemporary atomistic modeling in chemistry, biology, and materials …
cornerstone of contemporary atomistic modeling in chemistry, biology, and materials …