Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Entropy plays a pivotal role in catalysis, and extensive research efforts have been directed to
understanding the enthalpy-entropy relationship that defines the reaction pathways of …

Chemical reaction networks form the heart of microkinetic models, which are one of the key
tools available for gaining detailed mechanistic insight into heterogeneous catalytic …

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 …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Electrocatalytic CO2 reduction reactions (CO2RR) based on scalable and highly efficient
catalysis provide an attractive strategy for reducing CO2 emissions. In this work, we …

Abstract Lithium-sulfur (Li-S) batteries with high energy density have been considered one
kind of promising next-generation energy storage system. However, the shuttling effect of …

Transition metal (TM) catalyzed cross-coupling reactions are the utmost versatile and
reliable methods for the production of many industrially important fine chemicals. The …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …