Conspectus Finding catalytic materials with optimal properties for sustainable chemical and
energy transformations is one of the pressing challenges facing our society today …

Discovering and understanding new materials with desired properties are at the heart of
materials science research, and machine learning (ML) has recently offered special …

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

Despite recent advances of data acquisition and algorithms development, machine learning
(ML) faces tremendous challenges to being adopted in practical catalyst design, largely due …

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and
energy conversion processes, and its revolution necessitates the hunt for new materials with …

Catalysis is crucial for clean energy, green chemistry, and environmental remediation, but
traditional methods rely on expensive and scarce precious metals. This review addresses …

Conspectus Machine learning has become a common and powerful tool in materials
research. As more data become available, with the use of high-performance computing and …

Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …

Machine learning has been successfully applied in recent years to screen materials for a
variety of applications. However, despite recent advances, most screening-based machine …

A priori catalyst design guidelines from first principles simulations and reliable data-driven
models are essential for cost efficient catalyst discovery. Nonetheless, acquiring all …