AI-Powered Photocatalysis, Electrocatalysis & Green Hydrogen

The workshop aims to equip participants with an integrated computational framework for analysing photocatalytic materials, predicting catalytic performance, screening single-atom catalysts, evaluating photoelectrochemical systems, and assessing the technical and preliminary economic feasibility of green-hydrogen production.

By the end of the workshop, participants will be able to:

• Explain the principles of photocatalysis, electrocatalysis, and photoelectrochemical water splitting.
• Interpret structural, optical, surface, and morphological characterization data for photocatalytic materials.
• Calculate crystallite size, optical band gap, degradation efficiency, and kinetic rate constants.
• Build structured datasets using material, pollutant, catalyst, and operating-condition descriptors.
• Develop and validate machine-learning models for photocatalytic-performance prediction.
• Apply SHAP and uncertainty analysis to interpret model predictions responsibly.
• Generate descriptors for single-atom catalyst and adsorbate systems.
• Predict adsorption energies and prioritize catalyst candidates for further validation.
• Analyse HER, OER, and CO₂RR activity, selectivity, stability, and material availability.
• Interpret LSV, IPCE, ABPE, EIS, and Mott–Schottky datasets.
• Estimate photoelectrode performance, charge-transfer behaviour, and carrier properties.
• Develop a simplified photoreactor-performance and hydrogen-production model.
• Perform preliminary techno-economic screening using indicative hydrogen-production costs.

📅 Day 1: Photocatalyst Design, Characterization & Pollutant Degradation

• Fundamentals of semiconductor and heterogeneous photocatalysis
• ZnO nanostructures, doping, heterojunctions and defect engineering
• Chemical and green synthesis approaches
• XRD, UV–Vis/DRS, FTIR, SEM/TEM, BET and photoluminescence analysis
• Crystallite-size and optical-band-gap estimation
• Photocatalytic degradation of dyes, pharmaceuticals and emerging pollutants
• Experimental controls, degradation efficiency and mineralization
• Pseudo-first-order kinetics, rate constants and catalyst recyclability
• Linking material properties with photocatalytic performance

🛠️ Hands-on:

• Analyse ZnO characterization and pollutant-degradation datasets, calculate crystallite size and band gap, fit kinetic models and compare catalyst performance.
• Workflow: Characterization Data → Structural/Optical Analysis → Degradation Profile → Kinetic Modelling → Performance Interpretation

🧰 Tools Covered: Python, pandas, NumPy, SciPy, Matplotlib, WebPlotDigitizer, Google Colab

📌 Deliverable: ZnO characterization and photocatalytic-performance report.

📅 Day 2: Machine Learning for Photocatalytic Prediction & Optimization

• Building structured photocatalysis datasets
• Material, pollutant and experimental descriptors
• Data cleaning, missing-value treatment and exploratory analysis
• Feature engineering, scaling and outlier detection
• Random Forest, Support Vector Regression and XGBoost
• Cross-validation and hyperparameter optimization
• Model evaluation using R², MAE and RMSE
• SHAP-based feature interpretation
• Prediction of degradation efficiency and kinetic rate constants
• Applicability domain, uncertainty and data-leakage prevention
• AI-guided optimization of catalyst and operating conditions

🛠️ Hands-on:

• Build and validate an ML model to predict photocatalytic performance, identify influential parameters and recommend optimized degradation conditions.
• Workflow: Experimental Dataset → Preprocessing → Feature Engineering → ML Modelling → Validation → Explainability → Prediction → Optimization

🧰 Tools Covered: Python, pandas, scikit-learn, XGBoost, SHAP, Optuna, Matplotlib, Google Colab

📌 Deliverable: Validated and explainable photocatalytic-performance prediction model.

📅 Day 3: AI-Driven Single-Atom Catalyst Discovery & Virtual Screening

• Fundamentals of Single-Atom Catalysts and metal–support interactions
• M–N₄ sites, coordination environments and defect-engineered supports
• SAC applications in CO₂RR, HER and OER
• Atomic structures, adsorbates and materials descriptors
• Adsorption-energy prediction using ML surrogate models
• Key intermediates: *H, *COOH, *CO, *OH and *OOH
• Graph-based and equivariant AI for energy and force prediction
• AI-guided geometry optimization of catalyst–adsorbate systems
• CO₂RR–HER activity and selectivity analysis
• Stability, uncertainty, cost and elemental-availability filters
• Multi-objective catalyst ranking for further DFT or experimental validation

🛠️ Hands-on:

• Generate SAC descriptors, predict adsorption energies, analyse representative catalyst–adsorbate structures and screen a virtual SAC library.
• Workflow: SAC Library → Atomic Descriptors → Adsorption Prediction → Structure Evaluation → Activity/Selectivity Analysis → Stability Filter → Candidate Ranking

🧰 Tools Covered: ASE, Matminer, Catalysis-Hub datasets, FAIR-Chem/UMA, Scikit-learn, XGBoost, SHAP, Py3Dmol, Google Colab

📌 Deliverable: Ranked shortlist of promising single-atom catalysts with activity, selectivity, stability and confidence scores.

📅 Day 4: Photoelectrochemical Characterization, Reactor Design & Green-Hydrogen Assessment

• Fundamentals of photoelectrochemical water splitting
• Semiconductor photoelectrodes: BiVO₄, α-Fe₂O₃, TiO₂ and Cu₂O
• Band-gap and band-edge engineering
• Type-II, S-scheme and Z-scheme heterojunctions
• HER, OER and solar-to-hydrogen efficiency concepts
• LSV, photocurrent density, IPCE and ABPE analysis
• EIS fitting and charge-transfer resistance
• Mott–Schottky analysis, flat-band potential and carrier density
• Faradaic efficiency, stability and photocorrosion
• Photoreactor architectures, photon transport and Beer–Lambert attenuation
• Mass transport, flow, bubble coverage and gas separation
• Scale-up considerations, Techno-Economic Analysis and indicative LCOH

🛠️ Hands-on:

• Analyse PEC datasets, fit EIS and Mott–Schottky data, simulate simplified photoreactor performance and estimate hydrogen-production economics.
• Workflow: Semiconductor Screening → PEC Data Analysis → Electrochemical Fitting → Reactor Simulation → H₂ Production → Indicative LCOH

🧰 Tools Covered: Python, mp-api, pymatgen, impedance.py, NumPy, SciPy, Matplotlib, Plotly, Google Colab

📌 Deliverable: Integrated photoelectrode, reactor-performance and green-hydrogen feasibility report.

This workshop is suitable for:

• Undergraduate and postgraduate students in chemistry, physics, biotechnology, chemical engineering, environmental science, materials science, nanotechnology, and energy engineering.
• PhD scholars and researchers working in photocatalysis, electrocatalysis, nanomaterials, environmental remediation, CO₂ conversion, water splitting, or green hydrogen.
• Faculty members seeking to integrate AI, data science, and computational modelling into catalysis or clean-energy research.
• Materials scientists and electrochemists working on semiconductor photocatalysts, photoelectrodes, or single-atom catalysts.
• Environmental researchers studying dyes, pharmaceuticals, wastewater contaminants, and emerging pollutants.
• Chemical and process engineers interested in photoreactor design, reaction engineering, hydrogen production, and process scale-up.
• Data scientists and computational researchers interested in materials informatics and AI-assisted catalyst screening.
• R&D professionals working in advanced materials, renewable energy, hydrogen technology, environmental technologies, and sustainable chemical processes.

After completing the workshop, participants will be able to:

• Connect catalyst composition, structure, optical properties, and surface characteristics with photocatalytic performance.
• Analyse pollutant-degradation experiments using kinetic and comparative performance metrics.
• Construct reproducible ML pipelines for predicting degradation efficiency and reaction-rate constants.
• Detect data leakage, overfitting, extrapolation, and unreliable catalyst predictions.
• Interpret influential material and operating parameters using explainable AI.
• Use AI surrogate models to accelerate the screening of single-atom catalysts.
• Rank catalyst candidates using activity, selectivity, stability, uncertainty, cost, and availability criteria.
• Extract meaningful electrochemical and semiconductor parameters from PEC datasets.
• Relate photoelectrode behaviour to reactor-level hydrogen-production performance.
• Evaluate how photon transport, mass transfer, bubble coverage, stability, and gas separation influence scale-up.
• Estimate preliminary hydrogen yield and indicative LCOH under defined assumptions.
• Identify candidates and operating conditions requiring further DFT calculations or experimental validation.