Organic photoredox catalysts for CO2 reduction: Driving discovery with genetic algorithms

Kron, Kareesa J.; Rodriguez-Katakura, Andres; Regu, Pranesh; Reed, Maria N; Elhessen, Rachelle; Sharada, Shaama Mallikarjun

doi:10.1063/5.0088353

Cited by 9 publications

(10 citation statements)

References 62 publications

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“…The probability of a mutation occurring is set by the mutation rate, one of the hyperparameters of the GA. Although a wide variety of mutation rates have been reported in the literature 10,29,34,35 , ranging from 1% to 50%, there have been few reports on optimizing this parameter for chemical applications 36 . In this work, we examine mutation rates ranging from 10% to 90% with 10% increments.…”

Section: Chemical Representation Crossover and Mutationmentioning

confidence: 99%

Best Practices for Using Genetic Algorithms in Molecular Discovery

Greenstein

Elsey

Hutchison

2023

Preprint

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Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this work, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman's rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, 50% elitism rate, 3- way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

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Section: Chemical Representation Crossover and Mutationmentioning

confidence: 99%

Best Practices for Using Genetic Algorithms in Molecular Discovery

Greenstein

Elsey

Hutchison

2023

Preprint

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“…Another option is to use search algorithms (GAs), such as genetic algorithms, rather than evaluating the entire library. [13][14][15][16] The efficiency of these search algorithms also allows for the use of QM, rather than ML, for reactivity. [13,16].…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15][16] The efficiency of these search algorithms also allows for the use of QM, rather than ML, for reactivity. [13,16]. However, all studies so far have focused on screening user-defined libraries of catalysts.…”

Section: Introductionmentioning

confidence: 99%

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

Seumer

Jensen

2022

Preprint

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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 performed five independent GA searches that resulted in 448 unique molecules, for which we were able to locate 435 true transitions states at semiempirical level of theory. The predicted activation energies of all 435 molecules where all lower than that of DABCO, which is a popular catalyst of the MBH reaction. Virtually all the molecules contain an aziridine 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 redisovered as the search progresses. Many of the GA searches also introduce 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. The molecule with the lowest barrier has higher barriers for the other steps compared to DABCO, but none of the barriers are competitive with the rate-determining barrier and is predicted to outperform DABCO. This demonstrates the power of free exploration of chemical a space compared to more constrained fragment-based approaches.

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Section: Introductionmentioning

confidence: 99%

“…[13][14][15][16] The efficiency of these search algorithms also allows for the use of QM, rather than ML, for reactivity. [13,16] However, all studies so far have focused on screening user-defined libraries of catalysts. While experimental verification of catalysts predicted using these computational approaches are rare, Das et al [17] have recently successfully identified a frustrated Lewis pair catalyst for direct hydrogenation of CO 2 .…”

Section: Introductionmentioning

confidence: 99%

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

Seumer

Hansen

Nielsen

et al. 2022

Preprint

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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.

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Organic photoredox catalysts for CO2 reduction: Driving discovery with genetic algorithms

Cited by 9 publications

References 62 publications

Best Practices for Using Genetic Algorithms in Molecular Discovery

Best Practices for Using Genetic Algorithms in Molecular Discovery

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

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

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