A meta-learning approach for selectivity prediction in asymmetric catalysis

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 15, 2025

Abstract Transition metal-catalyzed asymmetric reactions are of high contemporary importance in organic synthesis. Recently, machine learning (ML) has shown promise accelerating the development newer catalytic protocols. However, need for large amount experimental data can present a bottleneck implementing ML models. Here, we propose meta-learning workflow that harness literature-derived to extract shared reaction features and requires only few examples predict outcome new reactions. Prototypical networks used as method enantioselectivity hydrogenation olefins. This model consistently provides significant performance improvement over other popular methods such random forests graph neural networks. The our meta-model is analyzed with varying sizes training demonstrate its utility even limited data. A good on an out-of-sample test set further indicates general applicability approach. We believe this work will provide leap forward identifying promising early phases when minimal available.

Language: Английский

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Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

Chemical Science, Journal Year: 2024, Volume and Issue: 15(34), P. 13618 - 13630

Published: Jan. 1, 2024

Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and selecting a catalyst induces the desired reactivity or selectivity trivial task. Nonetheless, ligand engineering selection any new prochiral olefin remains an empirical trial-error exercise. In study, we investigated whether machine learning techniques could be used to accelerate identification most efficient ligand. For purpose, high throughput experimentation build large dataset consisting results Rh-catalyzed asymmetric hydrogenation, specially designed applications in learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, computational framework automated reproducible quantum-chemistry based featurization structures was created. Together less computationally demanding representations, these descriptors were fed into our pipeline both out-of-domain in-domain prediction tasks reactivity. purposes, models provided limited efficacy. It found even expensive do not impart significant meaning model predictions. The application, partly successful predictions conversion, emphasizes need evaluating cost-benefit ratio intensive tailored descriptor design. Challenges persist predicting enantioselectivity, calling caution interpreting from small datasets. Our insights underscore importance diversity broad substrate inclusion suggest mechanistic considerations improve accuracy statistical models.

Language: Английский

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Machine Learning Assisted Selection of Catalyst for γ-Valerolactone Hydrogenation from Levulinic Acid

ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(44), P. 16340 - 16353

Published: Oct. 24, 2024

Language: Английский

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Genetic Algorithms for the Discovery of Homogeneous Catalysts

CHIMIA International Journal for Chemistry, Journal Year: 2023, Volume and Issue: 77(1/2), P. 39 - 39

Published: Feb. 22, 2023

In this account, we discuss the use of genetic algorithms in inverse design process homogeneous catalysts for chemical transformations. We describe main components evolutionary experiments, specifically nature fitness function to optimize, library molecular fragments from which potential are assembled, and settings algorithm itself. While not exhaustive, review summarizes key challenges characteristics our own (i.e., NaviCatGA) other GAs discovery new catalysts.

Language: Английский

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Machine-learning-guided prediction of Cu-based electrocatalysts towards ethylene production in CO2 reduction

Molecular Catalysis, Journal Year: 2023, Volume and Issue: 547, P. 113366 - 113366

Published: July 15, 2023

Language: Английский

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