Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal
nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

The development of reliable water oxidation catalysts (WOCs) is essential for implementing
artificial photosynthesis on a large technological scale. WOC research has evolved into two …

Designing single-species selective membranes for high-precision separations requires a
fundamental understanding of the molecular interactions governing solute transport. Here …

The accelerated discovery of materials for real world applications requires the achievement
of multiple design objectives. The multidimensional nature of the search necessitates …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered
that can selectively oxidize methane to methanol. We exploit active learning to …