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 …
Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021 - ACS Publications
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 …
Machine learning in concrete science: applications, challenges, and best practices
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …
human development. Despite conceptual and methodological progress in concrete science …
Artificial intelligence-powered electronic skin
Skin-interfaced electronics is gradually changing medical practices by enabling continuous
and non-invasive tracking of physiological and biochemical information. With the rise of big …
and non-invasive tracking of physiological and biochemical information. With the rise of big …
Machine-learning interatomic potentials for materials science
Y Mishin - Acta Materialia, 2021 - Elsevier
Large-scale atomistic computer simulations of materials rely on interatomic potentials
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
providing computationally efficient predictions of energy and Newtonian forces. Traditional …
Overview on theoretical simulations of lithium‐ion batteries and their application to battery separators
For the proper design and evaluation of next‐generation lithium‐ion batteries, different
physical‐chemical scales have to be considered. Taking into account the electrochemical …
physical‐chemical scales have to be considered. Taking into account the electrochemical …
Machine learning overcomes human bias in the discovery of self-assembling peptides
Peptide materials have a wide array of functions, from tissue engineering and surface
coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the …
coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the …
Benchmarking graph neural networks for materials chemistry
Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …
of machine learning models remarkably well-suited for materials applications. To date, a …
Deep learning in mechanical metamaterials: from prediction and generation to inverse design
Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …
mechanical properties determined by their microstructures and constituent materials …