Gaussian process regression for materials and molecules
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 …
Construction of high accuracy machine learning interatomic potential for surface/interface of nanomaterials—A review
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …
interfaces bestow them with various exceptional properties. These properties, however, also …
Evidential deep learning for guided molecular property prediction and discovery
While neural networks achieve state-of-the-art performance for many molecular modeling
and structure–property prediction tasks, these models can struggle with generalization to out …
and structure–property prediction tasks, these models can struggle with generalization to out …
Machine learning potentials for complex aqueous systems made simple
Simulation techniques based on accurate and efficient representations of potential energy
surfaces are urgently needed for the understanding of complex systems such as solid–liquid …
surfaces are urgently needed for the understanding of complex systems such as solid–liquid …
Universal QM/MM approaches for general nanoscale applications
Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address
chemical phenomena in complex molecular environments. Whereas this modeling approach …
chemical phenomena in complex molecular environments. Whereas this modeling approach …
Uncertainty-driven dynamics for active learning of interatomic potentials
Abstract Machine learning (ML) models, if trained to data sets of high-fidelity quantum
simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a …
simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a …
Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations
Water adsorption and dissociation processes on pristine low-index TiO2 interfaces are
important but poorly understood outside the well-studied anatase (101) and rutile (110). To …
important but poorly understood outside the well-studied anatase (101) and rutile (110). To …
Active learning strategies for atomic cluster expansion models
The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …
interatomic potentials with a formally complete basis set. Since the development of any …
Recent advances in first-principles based molecular dynamics
Conspectus First-principles molecular dynamics (FPMD) and its quantum mechanical-
molecular mechanical (QM/MM) extensions are powerful tools to follow the real-time …
molecular mechanical (QM/MM) extensions are powerful tools to follow the real-time …
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Neural network (NN) interatomic potentials provide fast prediction of potential energy
surfaces, closely matching the accuracy of the electronic structure methods used to produce …
surfaces, closely matching the accuracy of the electronic structure methods used to produce …