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 …
Machine learning for alloys
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …
data-science-inspired work. The dawn of computational databases has made the integration …
Four generations of high-dimensional neural network potentials
J Behler - Chemical Reviews, 2021 - ACS Publications
Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …
an important tool in the field of atomistic simulations. After the initial decade, in which neural …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
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 …
Artificial intelligence applied to battery research: hype or reality?
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
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 …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
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 …
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
We develop a neuroevolution-potential (NEP) framework for generating neural network-
based machine-learning potentials. They are trained using an evolutionary strategy for …
based machine-learning potentials. They are trained using an evolutionary strategy for …