Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review

Abstract

Plasma-based gas conversion has emerged as increasingly prominent sustainable technology for chemical production, offering significant advantages such as mild operating conditions, instantaneous control, and flexibility in scales. However, the inherent complexity of its multidimensional parameter space makes traditional experimental optimization resource-intensive. Machine learning (ML) presents a transformative method to efficiently explore such intricate scientific phenomena, yet its application in the field remains in its infancy. Current efforts are constrained by fragmented, small-scale experimental datasets that lack standardization across different reactor configurations and measurement protocols. Data quality issues, inconsistent reporting of performance metrics, and the absence of critical plasma and catalyst descriptors further hinder model development. Consequently, most ML studies are limited to simple predictive models that interpolate within narrow operational domains, offering little generalizability or mechanistic insight. This critical review provides a comprehensive analysis of ML methodologies applied to plasma-based gas conversion, using CO₂ conversion as a base case. We outline the general ML workflow and key algorithms, discuss their applications with state-of-the-art examples, and critically evaluate current limitations. Finally, we identify emerging challenges and future opportunities to guide the field toward more robust, generalizable, and physically as well as chemically meaningful ML applications.

Graphical abstract

Keywords

plasma-based gas conversion / artificial intelligence / machine learning / CO₂ utilization / active learning

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