Computational evolution of new catalysts for the Morita–Baylis–Hillman reaction

Abstract

We present a de novo discovery of an efficient catalyst of the methanol-mediated Morita– Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the es- timated barrier of the rate-determining step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We performed five independent GA searches that resulted in 448 unique molecules, for which we were able to locate 435 true transition states at semiem- pirical level of theory. The predicted activation energies of all 435 molecules were all lower than that of 1,4-diazabicyclo[2.2.2]octane (DABCO), which is a popular catalyst of the MBH reaction. Virtually all the molecules contain an azetidine N as the catalytically active site, which is discovered by the GA since it is either not found in the initial population or discarded early only to be rediscovered as the search progresses. Many of the GA searches also intro- duce a substituent with a hydrogen bond donor that helps to stabilize the transition state and thus lower the barrier. Two molecules are selected for further study based on their synthetic accessibility as predicted by the retrosynthesis package Manifold. For these two molecules, we compute the entire free energy reaction profile at the DFT level and show that their rate- determining barriers are 1.7 and 2.4 kcal/mol lower than that of DABCO. Azetidines have not been used as catalysts for the MBH reaction so we experimentally verified our predictions. One of the molecules was successfully synthesized and is indeed more active than DABCO with an eight-fold increase in the rate-constant corresponding to a roughly 1 kcal/mol lower barrier, in good agreement with the predictions. We believe this is the first experimentally verified de novo discovery of an efficient catalyst using a generative model.

Self-Driving Laboratories for Chemistry and Materials Science

“…On a purely computational level, this bottleneck can be circumvented, which has led to impressive and experimentally validated examples of closed-loop catalyst design, for example in organocatalysis. 465 In heterogeneous catalysis, on the other hand, synthesis requires intricate thermal treatment and annealing steps, which possess inherent automation constraints, and can often lead to structurally ill-defined materials, adding further complexity to the data-driven prediction problem. As a consequence, the last decade has produced rare examples of true SDLs for catalyst discovery, which remains a grand challenge for autonomous discovery, both from the software and the hardware standpoint.…”