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 …
Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
Schnet–a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …
image search, speech recognition, as well as bioinformatics, with growing impact in …
Performance and cost assessment of machine learning interatomic potentials
Machine learning of the quantitative relationship between local environment descriptors and
the potential energy surface of a system of atoms has emerged as a new frontier in the …
the potential energy surface of a system of atoms has emerged as a new frontier in the …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Machine learning a general-purpose interatomic potential for silicon
The success of first-principles electronic-structure calculation for predictive modeling in
chemistry, solid-state physics, and materials science is constrained by the limitations on …
chemistry, solid-state physics, and materials science is constrained by the limitations on …
Carbon nanodots from an in silico perspective
Carbon nanodots (CNDs) are the latest and most shining rising stars among
photoluminescent (PL) nanomaterials. These carbon-based surface-passivated …
photoluminescent (PL) nanomaterials. These carbon-based surface-passivated …
Machine learning for interatomic potential models
The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …
models is transforming molecular and materials research by greatly accelerating atomic …
An accurate and transferable machine learning potential for carbon
We present an accurate machine learning (ML) model for atomistic simulations of carbon,
constructed using the Gaussian approximation potential (GAP) methodology. The potential …
constructed using the Gaussian approximation potential (GAP) methodology. The potential …
Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future
Heterogeneous catalysis, for its industrial importance and great complexity in structure, has
long been the testing ground of new characterization techniques. Machine learning (ML) as …
long been the testing ground of new characterization techniques. Machine learning (ML) as …