Workshop Registration End Date :14 Oct 2026
Virtual Workshop
AI-Driven Semiconductor–Biohybrid Systems for CO₂ Conversion & Solar Fuel Production
From Solar Energy to Sustainable Biomanufacturing — Use AI to Design Biohybrid Nanocatalysts for CO₂ Conversion and Solar-Fuel Production.
Skills you will gain:
About Workshop:
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.
Aim: 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.
Workshop Objectives:
- 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
StudentINR 2499/- OR USD 59
Ph.D. Scholar / ResearcherINR 3999/- OR USD 79
Academician / FacultyINR 5299/- OR USD 99
Industry ProfessionalINR 6499/- OR USD 109
Important Dates
Registration Ends
14 Oct 2026 Indian Standard Timing 4: 30 PM
14 Oct 2026 Indian Standard Timing 4: 30 PM
Workshop Dates
14 Oct 2026 to 16 Oct 2026 Indian Standard Timing 5:30 PM
14 Oct 2026 to 16 Oct 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Workshop Outcomes
- Explain the fundamentals of biohybrid 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.
- Apply Random Forest and XGBoost to materials and bioprocess datasets.
- Interpret model predictions using SHAP and feature-importance analysis.
- Evaluate biohybrid systems for conversion efficiency, stability, toxicity, and sustainability.