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

This mentor-based workshop explores how Artificial Intelligence (AI), Machine Learning (ML), and Materials Informatics are revolutionizing green hydrogen research. Participants will learn AI-driven approaches for electrocatalyst discovery, materials screening, water electrolysis optimization, and techno-economic evaluation of hydrogen production systems. Through research-oriented concepts and hands-on computational workflows, participants will gain insights into modern strategies used for accelerating hydrogen technology development, from AI-based catalyst prediction to hydrogen cost optimization and future intelligent energy systems.

The aim of this workshop is to provide participants with an understanding of how Artificial Intelligence, Machine Learning, and Materials Informatics are transforming green hydrogen research — from AI-driven electrocatalyst discovery and computational screening to hydrogen production optimization and techno-economic analysis.

• Fundamentals of green hydrogen production and hydrogen economy
• Water electrolysis technologies (PEM, AEM, and Alkaline systems)
• Principles of HER/OER reactions and electrocatalyst performance evaluation
• AI and Machine Learning approaches for catalyst discovery
• Materials informatics workflows and descriptor-based prediction
• Computational screening of hydrogen electrocatalysts
• DFT-guided catalyst evaluation approaches
• Hydrogen production cost analysis and LCOH estimation
• AI-based optimization strategies for future hydrogen systems

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

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

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

• Understand modern green hydrogen technologies and research challenges
• Analyze electrocatalyst properties and performance parameters
• Apply AI/ML workflows for catalyst prediction and screening
• Explore materials databases for hydrogen research applications
• Understand computational approaches for accelerated materials discovery
• Perform basic techno-economic evaluation of hydrogen production systems
• Interpret AI-driven insights for sustainable hydrogen development

PhD Scholars and Researchers Faculty Members and Academicians Postgraduate Students (M.Tech/M.Sc.) Materials Science and Nanotechnology Researchers Electrochemistry and Catalysis Researchers Renewable Energy Professionals Chemical and Process Engineering Professionals AI/ML Researchers interested in Energy Applications Industry R&D Professionals working in hydrogen technologies