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 …
Lithium batteries and the solid electrolyte interphase (SEI)—progress and outlook
Interfacial dynamics within chemical systems such as electron and ion transport processes
have relevance in the rational optimization of electrochemical energy storage materials and …
have relevance in the rational optimization of electrochemical energy storage materials and …
Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Machine learning for electrocatalyst and photocatalyst design and discovery
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …
reducing the impact of global warming, and providing solutions to environmental pollution …
Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
Rechargeable batteries have become indispensable implements in our daily life and are
considered a promising technology to construct sustainable energy systems in the future …
considered a promising technology to construct sustainable energy systems in the future …
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 …
The stiffness of living tissues and its implications for tissue engineering
The past 20 years have witnessed ever-growing evidence that the mechanical properties of
biological tissues, from nanoscale to macroscale dimensions, are fundamental for cellular …
biological tissues, from nanoscale to macroscale dimensions, are fundamental for cellular …
Mechanical properties and peculiarities of molecular crystals
In the last century, molecular crystals functioned predominantly as a means for determining
the molecular structures via X-ray diffraction, albeit as the century came to a close the …
the molecular structures via X-ray diffraction, albeit as the century came to a close the …
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Abstract Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials science. Most of …
simulations in many fields, from chemistry via molecular biology to materials science. Most of …