Heterogeneous electrocatalysis lies at the center of various technologies that could help
enable a sustainable future. However, its complexity makes it challenging to accurately and …

During the last decades, X-ray absorption spectroscopy (XAS) has become an
indispensable method for probing the structure and composition of heterogeneous catalysts …

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

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic
and electronic structures around the absorbing atom. However, the quantitative analysis of …

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature.
Because hierarchy gives rise to unique properties and functions, many have sought …

The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …