AI-Driven Biohybrid Photocatalysis for CO₂ Conversion & Solar Fuels

This 3-day mentor-led workshop introduces the emerging field of AI-driven biohybrid nanocatalysis, where semiconductor nanomaterials are integrated with microorganisms or enzymes to convert solar energy and CO₂ into valuable chemicals and fuels. Participants will explore semiconductor photocatalysis, charge transfer, biological interfaces, microbial CO₂ fixation, AI-based catalyst screening, performance prediction, and safe-by-design optimisation. The workshop follows a continuous workflow from materials selection to AI-guided biohybrid design and sustainable candidate prioritisation.

To provide practical, research-oriented training in combining nanocatalysis, artificial photosynthesis, microbial biotechnology, and AI/ML for the design and optimisation of biohybrid systems for CO₂ conversion, solar-fuel generation, and sustainable biomanufacturing.

• Understand the principles of semiconductor photocatalysis and artificial photosynthesis.
• Explore band-gap engineering, charge generation, and charge-transfer mechanisms.
• Understand semiconductor–microbe and semiconductor–enzyme interfaces.
• Explore extracellular electron transfer and microbial CO₂ fixation.
• Understand microbial electrosynthesis and solar-to-chemical conversion.
• Evaluate material and biological parameters influencing biohybrid performance.
• Screen semiconductor materials using computational materials-property datasets.
• Develop datasets and molecular/material descriptors for AI modelling.
• Apply Random Forest and XGBoost for biohybrid performance prediction.
• Use SHAP to interpret important material and biological parameters.
• Evaluate ROS generation, oxidative stress, toxicity, and material stability.
• Apply multi-objective optimisation to balance performance, safety, and sustainability.
• Prioritise promising biohybrid systems for further experimental investigation.

📅 Day 1: Biohybrid Nanocatalysis & Artificial Photosynthesis

• Introduction to biohybrid nanocatalysis and semiconductor–biological systems Natural, artificial, and semi-artificial photosynthesis Semiconductor photocatalysis, band-gap engineering, and solar-energy harvesting Charge generation, separation, recombination, and interfacial electron transfer Semiconductor–microbe and semiconductor–enzyme interfaces Extracellular electron transfer and biological utilization of photogenerated electrons Key photocatalytic materials including TiO₂, ZnO, g-C₃N₄, BiVO₄, Fe₂O₃, and CdS Material properties governing photocatalytic activity, stability, and biohybrid performance Materials databases and computational screening of semiconductor photocatalysts
• 🛠️ Hands-on:
• Explore semiconductor materials using Materials Project datasets Analyse band gap, composition, stability, density, and electronic properties Compare and filter candidate photocatalysts based on activity, stability, and toxicity-related criteria Visualise structure–property relationships using Python Prepare a shortlisted semiconductor dataset for biohybrid-system design
• 🧰 Tools Covered:
• Google Colab, Python, Materials Project, mp-api, pymatgen, pandas, NumPy, Matplotlib, and semiconductor datasets

📅 Day 2: AI-Guided CO₂ Conversion & Biohybrid Performance Prediction

• Solar-driven CO₂ conversion, carbon fixation, and microbial electrosynthesis Photobiocatalytic CO₂ reduction and semiconductor–microbe electron-transfer mechanisms Microbial and enzymatic pathways for carbon conversion and reducing-power generation Production of solar fuels and value-added chemicals including hydrogen, formate, acetate, alcohols, and organic acids Relationship between material properties, biological conditions, and conversion performance Development of AI-ready biohybrid nanocatalysis datasets Feature engineering for material, biological, and operating parameters Random Forest and XGBoost models for CO₂-conversion and product-yield prediction Model evaluation using R², MAE, and RMSE Explainable AI and SHAP-based interpretation of structure–property–performance relationships
• 🛠️ Hands-on:
• Integrate semiconductor, biological, and operating-condition data into an AI-ready dataset Prepare features including band gap, catalyst type, pH, temperature, light intensity, and reaction conditions Train and compare Random Forest and XGBoost prediction models Evaluate model performance and interpret influential parameters using SHAP Rank semiconductor–biological combinations based on predicted CO₂-conversion potential
• 🧰 Tools Covered:
• Google Colab, Python, pandas, NumPy, scikit-learn, XGBoost, SHAP, Matplotlib, and biohybrid nanocatalysis datasets

📅 Day 3: Safe-by-Design Biohybrids & Sustainable Optimization

• Reactive oxygen species, oxidative stress, and microbial biocompatibility Heavy-metal leaching, nanomaterial toxicity, and semiconductor–microbe interactions Material degradation, long-term stability, catalyst recyclability, and regeneration Balancing photocatalytic efficiency with biological compatibility and safety Safe-by-design principles for biohybrid nanocatalysts Sustainability, life-cycle considerations, and scale-up of artificial-photosynthesis systems Multi-objective optimization of efficiency, stability, toxicity, cost, and sustainability Pareto optimization and performance trade-offs for candidate selection AI-assisted ranking of biohybrid systems for sustainable biomanufacturing Future directions in autonomous and AI-guided biohybrid-material discovery
• 🛠️ Hands-on:
• Integrate material, AI-predicted performance, safety, and sustainability data from Days 1 and 2 Compare high-efficiency and safe-and-stable photocatalyst candidates Perform multi-criteria and Pareto-based candidate ranking Generate comparative plots and optimization visualisations Identify semiconductor–biological combinations with the best balance of performance, safety, and sustainability Develop a preliminary safe-by-design biohybrid nanocatalyst shortlist for CO₂ conversion and solar biomanufacturing
• 🧰 Tools Covered:
• Google Colab, Python, pandas, scikit-learn, XGBoost, SHAP, Matplotlib, multi-objective optimization workflows, and candidate-ranking templates

• Explain the fundamentals ofbiohybrid nanocatalysis and artificial photosynthesis.
• Understand semiconductor–biological interfaces and charge-transfer mechanisms.
• Evaluate semiconductor candidates using materials-property data.
• Understand microbial CO₂ fixation and solar-to-chemical conversion pathways.
• Construct datasets for AI-assisted biohybrid-system analysis.
• Build ML models to predict biohybrid CO₂-conversion performance.
• ApplyRandom Forest and XGBoostto materials and bioprocess datasets.
• Interpret model predictions usingSHAP and feature-importance analysis.
• Evaluate biohybrid systems forconversion efficiency, stability, toxicity, and sustainability.

Graduate and postgraduate students in Nanotechnology, Biotechnology, Biochemistry, Chemistry, Materials Science, Microbiology, Environmental Science, Bioinformatics, and related fields. PhD scholars and researchers working in photocatalysis, nanocatalysis, artificial photosynthesis, microbial biotechnology, synthetic biology, materials informatics, and sustainable energy. Academicians and faculty members interested in AI-enabled materials research, biohybrid systems, CO₂ conversion, and sustainable biomanufacturing. Industry professionals from clean energy, biotechnology, materials R&D, environmental technology, chemical engineering, and sustainable manufacturing. Computational researchers and data scientists interested in applying machine learning to materials and biological systems.