Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms

Aug 4, 2026

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Sunlight-driven CO2 reduction into fuels and platform chemicals is a promising route toward a circular economy, yet conventional optimization methods struggle with multivariable, multimetric photocatalytic systems. They often target a single performance metric at the expense of others, limiting achievable overall performance. A holistic metric is defined to unify multiple figures of merit, and a machine learning-guided workflow efficiently navigates a large experimental parameter space. In a five-component self-assembled photocatalytic micelle system, experiments are optimized to simultaneously improve yield, quantum yield, turnover number, and turnover frequency while preserving high selectivity. Analysis of the dataset identifies buffer concentration as the dominant parameter, nearly four times more influential than catalyst concentration, enabling quantification of each parameter’s impact and clearer identification of performance bottlenecks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Holistic optimization concept and objective functions

Limits of heuristic tuning and brute-force screening

Learning algorithms and Bayesian optimization approach

Role of supramolecular assembly in photocatalysis

This article is licensed under CC-BY 4.0 [pubs.acs.org/JACS](pubs.acs.org/JACS) Article Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms Shannon A. Bonke, Giovanni Trezza, Luca Bergamasco, Hongwei Song, Santiago Rodríguez-Jiménez, Leif Hammarström, Eliodoro Chiavazzo, * and Erwin Reisner* Cite This: J. Am. Chem. Soc. 2024, 146, 15648−15658 Read Online | | | | | | | | --- | --- | --- | --- | --- | --- | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | ABSTRACT: The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter’s effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit. ■ INTRODUCTION Catalysis relies on a complex interplay of interdependent variables that must be optimized to meet a set of performance metrics. The challenge is exemplified by multicomponent photocatalytic systems where the parameter space is increasingly difficult to navigate due to the increasing number of variables required to provide supramolecular control (e.g., concentrations of reagents, additives, experimental variables). The optimization target is also unclear as there is limited standardization and a multitude of metrics to optimize (e.g., yield, quantum yield, selectivity, turnover number and frequency).1,2 Crucially, established and intuitive heuristic/ human optimization approaches can only maximize 1 or 2 performance metrics simultaneously. This situation has led to selective optimizations where some metrics are prioritized in ways that have limited meaning to overall system performance because they use conditions that sacrifice other metrics; such as using a very low catalyst loading to reach a high turnover frequency (TOF) but having negligible product yield.1 The fundamental problem is that optimization to improve all figures of merit (holistic optimization) is not feasible with established protocols. The parameter space is too large to evaluate, the interactions of variables that affect multiple metrics are too complex, and no holistic figure of merit has been defined.1,2 New strategies are required to navigate the large parameter space and extract deeper understanding into how multiple variables interact and affect each metric to control overall system performance.3−5 Holistic optimization first requires all figures of merit to be connected via a mathematical description (an objective function) that evaluat

FAQs

Why are established optimization approaches inadequate for multivariable multimetric photocatalytic systems?

They typically optimize one performance metric while sacrificing others, which restricts overall system performance. The lack of standardization and the presence of many competing metrics make intuitive optimization insufficient for holistic improvement.

How does the methodology enable holistic optimization of multiple performance figures of merit?

It defines a holistic metric as an objective function that combines multiple figures of merit into a single scalar. A machine learning algorithm guides experiments through the large parameter matrix to efficiently maximize this objective.

Which parameter was found to dominate optimal photocatalytic activity, and how does it compare to catalyst concentration?

Buffer concentration emerged as the dominating parameter for optimal photocatalytic activity. Its effect is nearly four times more important than catalyst concentration.

Aug 4, 2026

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