Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Electrocatalysis as the nexus for sustainable renewable energy: the gordian knot of activity, stability, and selectivity
J Masa, C Andronescu… - Angewandte Chemie …, 2020 - Wiley Online Library
The use of renewable energy by means of electrochemical techniques by converting H2O,
CO2 and N2 into chemical energy sources and raw materials, is the basis for securing a …
CO2 and N2 into chemical energy sources and raw materials, is the basis for securing a …
Open catalyst 2020 (OC20) dataset and community challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
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 …
Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning
The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach
for mitigating CO2 emissions and generating value-added products. Consequently …
for mitigating CO2 emissions and generating value-added products. Consequently …
How machine learning can accelerate electrocatalysis discovery and optimization
Advances in machine learning (ML) provide the means to bypass bottlenecks in the
discovery of new electrocatalysts using traditional approaches. In this review, we highlight …
discovery of new electrocatalysts using traditional approaches. In this review, we highlight …
[HTML][HTML] Unlocking the potential: Machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
Adsorption enthalpies for catalysis modeling through machine-learned descriptors
M Andersen, K Reuter - Accounts of Chemical Research, 2021 - ACS Publications
Conspectus Heterogeneous catalysts are rather complex materials that come in many
classes (eg, metals, oxides, carbides) and shapes. At the same time, the interaction of the …
classes (eg, metals, oxides, carbides) and shapes. At the same time, the interaction of the …
Machine learning boosts the design and discovery of nanomaterials
Y Jia, X Hou, Z Wang, X Hu - ACS Sustainable Chemistry & …, 2021 - ACS Publications
Nanomaterials (NMs) have developed quickly and cover various fields, but research on
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
nanotechnology and NMs largely relies on costly experiments or complex calculations (eg …
Crystal twins: self-supervised learning for crystalline material property prediction
Abstract Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training accurate ML …
material properties. However, large labeled datasets required for training accurate ML …