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 …
Physics-inspired structural representations for molecules and materials
The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …
predict or elucidate the relationship between the atomic-scale structure of matter and its …
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …
energy surface of molecules and materials is a long-standing goal in the natural sciences …
Open catalyst 2020 (OC20) dataset and community challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
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 …
The design space of E (3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …
Roadmap on machine learning in electronic structure
In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
science. In fact, traditional methods, mostly developed in the second half of the XXth century …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon
The atomic cluster expansion is a general polynomial expansion of the atomic energy in
multi-atom basis functions. Here we implement the atomic cluster expansion in the …
multi-atom basis functions. Here we implement the atomic cluster expansion in the …
Cartesian atomic cluster expansion for machine learning interatomic potentials
B Cheng - npj Computational Materials, 2024 - nature.com
Abstract Machine learning interatomic potentials are revolutionizing large-scale, accurate
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …
atomistic modeling in material science and chemistry. Many potentials use atomic cluster …