Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
Machine learning for electronically excited states of molecules
J Westermayr, P Marquetand - Chemical Reviews, 2020 - ACS Publications
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 …
Quantum chemistry in the age of machine learning
PO Dral - The journal of physical chemistry letters, 2020 - ACS Publications
As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …
new methods and applications based on the combination of QC and ML is surging. In this …
On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
Abstract Machine learned force fields typically require manual construction of training sets
consisting of thousands of first principles calculations, which can result in low training …
consisting of thousands of first principles calculations, which can result in low training …
High-fidelity potential energy surfaces for gas-phase and gas–surface scattering processes from machine learning
In this Perspective, we review recent advances in constructing high-fidelity potential energy
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …
DPA-2: a large atomic model as a multi-task learner
D Zhang, X Liu, X Zhang, C Zhang, C Cai… - npj Computational …, 2024 - nature.com
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes
in atomic modeling, simulation, and design. AI-driven potential energy models have …
in atomic modeling, simulation, and design. AI-driven potential energy models have …
Searching configurations in uncertainty space: Active learning of high-dimensional neural network reactive potentials
Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic
simulations with ab initio accuracy. While constructing NN PESs, their training data points …
simulations with ab initio accuracy. While constructing NN PESs, their training data points …
Bayesian machine learning for quantum molecular dynamics
RV Krems - Physical Chemistry Chemical Physics, 2019 - pubs.rsc.org
This article discusses applications of Bayesian machine learning for quantum molecular
dynamics. One particular formulation of quantum dynamics advocated here is in the form of …
dynamics. One particular formulation of quantum dynamics advocated here is in the form of …
Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation
Free energy simulations that employ combined quantum mechanical and molecular
mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly …
mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly …
[HTML][HTML] Frontiers in atomistic simulations of high entropy alloys
The field of atomistic simulations of multicomponent materials and high entropy alloys is
progressing rapidly, with challenging problems stimulating new creative solutions. In this …
progressing rapidly, with challenging problems stimulating new creative solutions. In this …