Green Hydrogen 2.0: AI-Driven Electrocatalyst Discovery, Materials Informatics & Techno-Economic Optimization for Hydrogen Production
Accelerating the Future of Clean Energy Through Artificial Intelligence, Intelligent Catalyst Discovery & Next-Generation Hydrogen Technologies
Skills you will gain:
About Program:
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.
Aim: 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.
Program Objectives:
- 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
What you will learn?
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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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
Career Supporting Skills
Program Outcomes
- 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