This “white paper” is a concise perspective of the potential of machine learning in the
process systems engineering (PSE) domain, based on a session during FIPSE 5, held in …

Crystal structure prediction (CSP) is the problem of determining the most stable crystalline
arrangements of materials given their chemical compositions. In general, CSP …

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards
the rational design of novel catalysts, understanding reactions over surfaces is the most …

Develo** high-performance single-atom platinum (Pt) catalysts for acidic hydrogen
evolution reaction (HER) is of significance. However, their HER kinetics are limited due to …

While quantitative structure–property relations (QSPRs) have been developed successfully
in multiple fields, catalyst synthesis affects structure and in turn performance, making simple …

Determining the energetically most favorable structure of nanoparticles is a fundamentally
important task towards understanding their stability. In the case of bimetallic nanoclusters …

Novel materials are being enabled by advances in synthesis techniques that achieve ever
better control over the atomic-scale structure of materials. The pace of materials …

We mine from the literature experimental data on the CO2 electrochemical reduction
selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning …

Structure enumeration has played an essential role in performing crystal structure prediction
and in understanding crystal structures. In general, structure enumeration requires a policy …

Computational materials discovery involves a complex interplay between physics-based
modeling, data-driven machine learning, and optimization. Despite fast-growing …