The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

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 …

Material representations that are compatible with machine learning models play a key role in
develo** models that exhibit high accuracy for property prediction. Atomic orbital …

Abstract Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training accurate ML …

Develo** amorphous polymers with desirable thermal conductivity has significant
implications, as they are ubiquitous in applications where thermal transport is critical …

Metal-organic frameworks (MOFs) have been considered as promising physical adsorbents
for hydrogen storage due to their high porosity and structural tunability. We selected 7643 …

We apply the machine learning (ML) tool to calculate the Gibbs free energy (Δ G) of reaction
intermediates rapidly and accurately as a guide for designing porphyrin-and graphene …

Autonomous computations that rely on automated reaction network elucidation algorithms
may pave the way to make computational catalysis on a par with experimental research in …

To achieve carbon neutrality, electrocatalysis has the potential to be applied in the
technological upgrading of numerous industries. Therefore, the search for high-performance …