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 …
Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
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 …
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
electrolyte batteries, thanks to its highly conductive phases, cheap components, and large …
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
Challenges, opportunities, and prospects in metal halide perovskites from theoretical and machine learning perspectives
Metal halide perovskite (MHP) is a promising next generation energy material for various
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs …
Structure and pore size distribution in nanoporous carbon
We study the structural and mechanical properties of nanoporous (NP) carbon materials by
extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To …
extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To …
Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards
the rational design of novel catalysts, understanding reactions over surfaces is the most …
the rational design of novel catalysts, understanding reactions over surfaces is the most …
Modeling high-entropy transition metal alloys with alchemical compression
Alloys composed of several elements in roughly equimolar composition, often referred to as
high-entropy alloys, have long been of interest for their thermodynamics and peculiar …
high-entropy alloys, have long been of interest for their thermodynamics and peculiar …