Catalysis is key to address today’s most pressing environmental and public health challenges; from pollutant degradation and clean water generation to targeted biosensing and the mitigation of xenobiotic toxicity. With the rapid emergence of machine learning, generative models, and autonomous laboratories, we are entering a digital era in which artificial intelligence (AI) accelerates catalyst discovery, design, and optimisation. These tools have shown potential to not only shorten the time between computational prediction and experimental realisation, but also to design and discover fundamentally new catalytic materials with optimized biocompatibility and minimal off-target toxicity.

This Research Topic will focus on AI-driven approaches, with a special emphasis in environmental catalysis, highlighting how data science, modelling, and closed-loop experimentation can expand the materials space, deepen mechanistic understanding, and ultimately enable practical environmental applications. The scope spans enzymes, and nanostructured materials that can be used as photocatalysts, electrocatalysts, single-atom catalysts, and porous frameworks (i.e. MOFs/COFs), among others. Key areas of interest include, but are not limited to:

- Predictive AI Toxicology and Biocompatibility: Utilising machine learning to predict the environmental persistence and biological toxicity of novel catalysts. We encourage studies using AI to develop for "safe-by-design" materials, assessing oxidative stress induction, cytotoxicity, and the long-term impact of catalyst byproducts on human health and ecosystems.

- Sensing: catalytic materials used in electrochemical, optical, and photoelectrochemical sensors for the detection of pollutants, toxins, greenhouse gases, and environmental biomarkers.

- Sustainable synthesis: AI-guided discovery of catalysts that reduce energy input, minimize byproducts, and enable greener reaction pathways.
AI-Guided Enzyme and Nanozyme Design: Generative models that tailor biocatalysts for medical applications, such as enzyme replacement therapies, reactive oxygen species (ROS) scavenging, or accelerated metabolic detoxification.

- Simplifying complex reactions: catalyst development that lowers the reliance on harsh conditions or elaborate chemistries, creating new sustainable materials or streamlining established synthetic routes.

- Mechanistic insights and structure–activity relationships: we invite studies that elucidate how catalytic mechanisms can be captured, predicted, and guided by AI. Contributions that link active site chemistry, defect engineering, or catalytic pathways to performance are encouraged.

- Explainable AI for Bio-Mechanistic Insights: Contributions that link active site chemistry to biological performance, using explainable AI to bridge the gap between material structure and its behaviour within complex biological or clinical matrices.

Manuscripts should show contributions across computational, theoretical, and experimental catalysis. We particularly encourage submissions that bridge the gap between in silico prediction and real-world performance, and explore mechanism-informed descriptors for explainable AI.

Topic editor Dr. Antonio Ruiz-Gonzalez receives funding from the Academy of Medical Sciences Cross-Sector Award. Topic Editor Dr. Layla Hosseini-Gerami is co-founder of, and owns shares in Ignota Labs Ltd. The other Topic Editors report no competing interests related to this Research Topic.

Impact

Frontiers in Catalysis

Modelling, Theory and Computational Catalysis