Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack

Abstract

In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of Patterns, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials.