Discovery of molybdenum based nitrogen fixation catalysts with genetic algorithms.

Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity of the chemical reactions involved and the lack of understanding of natures enzyme catalysts raises the need for intricate computational models. In this study, we use a dataset of 91 experimentally verified ligands as starting population for a Genetic Algorithm (GA) and use this to discover molybdenum based nitrogen fixation catalyst in trigonal bipyramidal and octahedral configurations.

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita-Baylis-Hillman Reactions.

Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization.

Computational Evolution Of New Catalysts For The Morita-Baylis-Hillman Reaction.

We present a de novo discovery of an efficient catalyst of the Morita-Baylis-Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate-determining step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We identify 435 candidates, virtually all of which contain an azetidine N as the catalytically active site, which is discovered by the GA. Two molecules are selected for further study based on their predicted synthetic accessibility and have predicted rate-determining barriers that are lower than that of a known catalyst.