Green Hydrogen 2.0: AI-Driven Electrocatalyst Discovery, Materials Informatics & Techno-Economic Optimization for Hydrogen Production

Aim

Workshop Objectives

Workshop Structure

Day 1: Green Hydrogen Fundamentals & AI-Driven Electrocatalyst Discovery

• Hydrogen economy and sustainable energy transition
• Green hydrogen production pathways and water electrolysis technologies (PEM, AEM, Alkaline)
• Fundamentals of HER/OER reactions and electrocatalyst performance parameters
• Catalyst design principles and materials descriptors
• Role of AI, Machine Learning, and Materials Informatics in catalyst discovery
• AI-driven workflows for catalyst property prediction and optimization

🧪 Hands-on: AI-Based Electrocatalyst Discovery Workflow

• Explore catalyst datasets
• Generate material descriptors
• Build AI-based prediction workflows
• Identify promising catalyst candidates

Tools: Python | Google Colab | Materials Databases | pymatgen | Matminer | Scikit-learn

Day 2: AI-Powered Catalyst Screening & Computational Materials Design

• Advanced electrocatalyst materials for hydrogen generation
• Transition metal catalysts, single atom catalysts, and emerging materials
• Catalyst optimization through doping, defects, and surface engineering
• DFT-guided catalyst evaluation and activity descriptors
• Materials database exploration and high-throughput screening
• Machine Learning models for catalyst prediction
• Explainable AI approaches for materials discovery

🧪 Hands-on: Virtual Screening of Hydrogen Electrocatalysts

• Access materials databases
• Extract catalyst properties
• Apply ML-based screening approaches
• Rank potential hydrogen catalysts

Tools: Materials Project | pymatgen | Matminer | Python | Scikit-learn | XGBoost

Day 3: Techno-Economic Optimization & AI-Powered Hydrogen Systems

• Renewable energy integration with hydrogen production
• Electrolyzer efficiency and system optimization
• Hydrogen production cost analysis
• CAPEX, OPEX, and Levelized Cost of Hydrogen (LCOH) estimation
• Sustainability assessment and Life Cycle Analysis (LCA)
• AI-enabled digital twins and future hydrogen technologies

🧪 Hands-on: Techno-Economic Modeling of Green Hydrogen Systems

• Analyze hydrogen production parameters
• Estimate production cost and LCOH
• Perform sensitivity analysis
• Optimize hydrogen production strategies

Tools: Python | Pandas | NumPy | Plotly | Hydrogen Cost Models

Who Should Enrol?

Important Dates

Workshop Outcomes