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

Aug 4, 2026

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Sunlight-driven CO2 reduction into fuels and platform chemicals supports a circular economy, yet conventional optimization methods struggle with multi-variable, multi-metric photocatalytic systems. This work introduces a holistic performance metric that integrates multiple figures of merit and applies machine learning to efficiently steer experiments across a large parameter space. A self-assembled, five-component photocatalytic micelle platform is experimentally optimized to raise yield, quantum yield, turnover number, and frequency while preserving high selectivity. The dataset enables quantifying each parameter’s contribution to overall performance, revealing buffer concentration as the dominant factor for optimal results and improving insight into performance bottlenecks, comparability, and cross-domain catalysis optimization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages27

Reading Time1 h 8 min

AccessFree to download

Introduction

Holistic optimization needs a unified objective function

Why learning-based optimization reduces experimental burden

Dataset-driven interpretation of parameter effects

Results and discussion

Multi-metric optimization for self-assembled photocatalytic micelles

Feature importance and dominant parameters

Implications for standardization and comparability

Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms Shannon A. Bonke,a Giovanni Trezza,b Luca Bergamasco,b Hongwei Song,c Santiago RodríguezJiménez,a Leif Hammarström,c Eliodoro Chiavazzob* and Erwin Reisnera * a Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. b Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy. c Department of Chemistry, Ångström Laboratory, Uppsala University, Box 523, 75120 Uppsala, Sweden. * Correspondence to: Prof E. Reisner ([reisner@ch.cam.ac.uk](reisner@ch.cam.ac.uk)) or Prof E. Chiavazzo (eliodoro.chiavazzo@polito. it) . Keywords: Machine Learning, Photocatalysis, CO2 Reduction, Self-Assembly, Micelles, Shapley Additive Explanations (SHAP) , Holistic Optimization Table of Contents Figure Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms Page 1 / 27 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 multi-variable multi-metric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multi-metric challenge by defining a metric for holistic system performance that takes all 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 fivecomponent 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 dataset 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 performance, 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. Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance using Machine Learning Algorithms Page 2 / 27 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 mer

FAQs

Why are established optimization approaches inadequate for multi-variable, multi-metric photocatalytic systems?

They typically optimize one performance metric while sacrificing others, and they lack standardization and a single holistic objective function. This prevents true improvement across all figures of merit simultaneously.

How does the proposed method make holistic optimization accessible?

It defines a holistic metric that combines multiple figures of merit into a single scalar objective, then uses a machine learning algorithm to guide experiments through the large parameter matrix efficiently.

Which parameter is found to dominate optimal performance in the optimized CO2-to-CO system?

Buffer concentration is unexpectedly revealed as the dominating parameter, nearly four times more important than catalyst concentration.

Aug 4, 2026

Upload by Eliana