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 …
Interatomic potentials: Achievements and challenges
Interatomic potentials approximate the potential energy of atoms as a function of their
coordinates. Their main application is the effective simulation of many-atom systems. Here …
coordinates. Their main application is the effective simulation of many-atom systems. Here …
LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
Linear atomic cluster expansion force fields for organic molecules: beyond rmse
We demonstrate that fast and accurate linear force fields can be built for molecules using the
atomic cluster expansion (ACE) framework. The ACE models parametrize the potential …
atomic cluster expansion (ACE) framework. The ACE models parametrize the potential …
The design space of e (3)-equivariant atom-centered interatomic potentials
The rapid progress of machine learning interatomic potentials over the past couple of years
produced a number of new architectures. Particularly notable among these are the Atomic …
produced a number of new architectures. Particularly notable among these are the Atomic …
Hyperactive learning for data-driven interatomic potentials
Data-driven interatomic potentials have emerged as a powerful tool for approximating ab
initio potential energy surfaces. The most time-consuming step in creating these interatomic …
initio potential energy surfaces. The most time-consuming step in creating these interatomic …
Atomic cluster expansion for quantum-accurate large-scale simulations of carbon
We present an atomic cluster expansion (ACE) for carbon that improves over available
classical and machine learning potentials. The ACE is parametrized from an exhaustive set …
classical and machine learning potentials. The ACE is parametrized from an exhaustive set …
Efficient parametrization of the atomic cluster expansion
The atomic cluster expansion (ACE) provides a general, local, and complete representation
of atomic energies. Here we present an efficient framework for parametrization of ACE …
of atomic energies. Here we present an efficient framework for parametrization of ACE …
Big data in a nano world: a review on computational, data-driven design of nanomaterials structures, properties, and synthesis
The recent rise of computational, data-driven research has significant potential to accelerate
materials discovery. Automated workflows and materials databases are being rapidly …
materials discovery. Automated workflows and materials databases are being rapidly …