Nature Communications· 2026Q1
Closed-loop photoreactor design enabled by machine learning and digital twins
- 2citations
- Q1SCImago
- 2026year
Short summary
A machine learning-driven workflow using digital twins and Bayesian optimization designs photoreactors with maximized photon capture and uniform irradiation, validated experimentally.
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Key points
- Introduced a closed-loop workflow combining ray-tracing digital twins and Bayesian optimization for photoreactor design.
- The digital model was calibrated with experimental data and validated using potassium ferrioxalate actinometry.
- Optimized designs achieved maximized incident photon flux and maintained irradiation homogeneity across 16 vials.
- Experimental validation showed reaction yields correlated with simulated photon flux in a photocatalytic Suzuki–Miyaura cross-coupling.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Designing efficient photoreactors remains challenging due to the complex interplay of reflection, scattering, and absorption. Here, we introduce a closed-loop workflow integrating ray-tracing digital twins with multi-objective Bayesian optimization to design photoreactors with high photon capture and uniform irradiation. A digital 3D reactor model is calibrated using measured emission characteristics, reflectivity data, and geometrical properties and validated against potassium ferrioxalate actinometry. The validated model explores reflector shape and vial positioning, identifying geometries that maximize incident photon flux while maintaining homogeneity across 16 vials. Experimental validation using a photocatalytic Suzuki–Miyaura cross-coupling shows that reaction yields track simulated photon flux. The optimized reactor is further applied to quantum-yield screening, enabling mechanistic insights across diverse conditions in a single experiment. This work establishes an experimentally validated virtual co-design framework in which machine learning and digital twins accelerate photoreactor development and support quantitative understanding of photochemical processes.
The authors' abstract, as published at the source. Nature Communications, 2026 · DOI ↗
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Field: Organic Chemistry
Organic ChemistryChemistry