Accelerated Exploration of Heterogeneous CO2 Hydrogenation Catalysts by Bayesian Optimized High-throughput and Automated Experimentation.

07 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Automated high-throughput platforms and Artificial Intelligence (AI) are already accelerating discovery and optimization in various fields of chemistry and chemical engineering. However, despite some promising solutions, little to no attempts have targeted the full heterogeneous catalyst discovery workflow, with most chemistry laboratories continuing to perform research with a traditional one-at-a-time experiment approach and limited digitization. In this work, we present a closed-loop data-driven approach targeting the optimization of catalysts’ composition for the direct transformation of carbon dioxide (CO2) into methanol, by combining Bayesian Optimization (BO) algorithm, automated synthesis by incipient wetness impregnation and high-throughput catalytic performance evaluation in fixed bed mode. The BO algorithm optimized a four-objective function simultaneously (high CO2 conversion, high methanol selectivity, low methane selectivity, and low metal cost) with a total of 11 parameters (4 supports, 6 metals salts, and one promoter). In 6 weeks, 144 catalysts were synthesized and tested, with limited manual laboratory activity. The results show a significant improvement in the objectives at the end of each iteration. Between the first and fifth catalyst generation, the average CO2 conversion and methanol formation rates have been multiplied by 5.7 and 12.6 respectively, while simultaneously reducing the methane production rate by 3.2 and dividing the metal cost by 6.3 times. Notably, through the exploration process, the BO algorithm rapidly focuses on copper-based catalysts supported on zirconia doped with Zinc and/or Cerium, with the best catalysts, according to the model, showing an optimized composition of 1.85wt% Cu, 0.69wt% Zn, and 0.05wt% Ce supported on ZrO2. When changing the objective, i.e. removing the metal cost as a constrain, the BO algorithm suggests compositions centered on Indium-based catalysts, highlighting an alternative family of catalysts, testifying of the algorithm adaptability and the reusability of the data when targeting different objectives. In only 30 days, the BO, coupled with automated synthesis and high-throughput testing, has been able to replicate the major development stages in the field of heterogeneous catalysts research for CO2 conversion to methanol, made over of the last 100 years with a conventional experimental approach. This data-driven approach proves to be very efficient in exploring and optimizing catalyst composition from the vast multi-parameter space towards multiple performance objectives simultaneously and could be easily extrapolated to different parameter spaces, objectives, and be transposed to other applications.


Artificial Intelligence
Machine Learning
Bayesian Optimization
High-Throughput Experimentation
Heterogeneous Catalysis
Self-driving Labs

Supplementary materials

Condensed set of data for the 144 catalysts presented in this study
The file provide the main synthesis and performances of the 144 catalysts presented in this study.
Supplemental graphs presenting additional performances information for the 144 catalysts
Figure S1 : Picture of the 120 impregnation vials used to prepare the metal-supported catalysts of the first 5 generations of the study (gen 1 to 5 from left to right). Figure S2 : Graph presenting the average CO2 to methanol conversion, selectivities and yield for each of the 6 generations. Figure S3 : Plot presenting the performances of the 144 catalysts prepared in this study in terms of CO2 conversion and methanol. Figure S4 : Plot presenting the performances of the 144 catalysts prepared in this study in terms of methane selectivity and cost.

Supplementary weblinks


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