Green Hydrogen 2.0: AI-Guided Electrocatalyst Discovery, Water Electrolysis & Techno-Economic Optimization
Accelerating the Future of Clean Energy Through Artificial Intelligence, Intelligent Catalyst Discovery & Next-Generation Hydrogen Technologies
About This Course
The global energy transition is accelerating the adoption of green hydrogen as a clean fuel for future industries. However, improving hydrogen production efficiency, reducing costs, and discovering high-performance catalysts remain major scientific and engineering challenges.
Aim
The aim of this international mentor-based workshop is to provide participants with a comprehensive understanding of how Artificial Intelligence, Machine Learning, and Materials Informatics are transforming green hydrogen research and development — from accelerated electrocatalyst discovery and water electrolysis optimization to cost-effective hydrogen production strategies.
Workshop Objectives
- Understand the fundamentals of green hydrogen production and hydrogen economy
- Explore different water electrolysis technologies including PEM, alkaline, and AEM systems
- Understand the role of AI and Machine Learning in accelerating catalyst discovery
- Learn computational approaches for identifying high-performance electrocatalyst materials
- Explore materials databases and AI-based prediction workflows
- Analyze catalyst activity, efficiency, and optimization parameters
- Understand techno-economic factors influencing hydrogen production cost
- Develop awareness of emerging AI-driven solutions for scalable hydrogen technologies
Workshop Structure
Day 1: Green Hydrogen Fundamentals & AI-Driven Catalyst Discovery
- Global hydrogen economy and sustainable energy transition
- Green hydrogen production pathways
- Alkaline, PEM, and AEM water electrolysis technologies
- Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER)
- Role of AI and Machine Learning in accelerating catalyst discovery
- Materials descriptors and data-driven catalyst prediction
Hands-On Session : AI-Based Electrocatalyst Discovery Workflow
Participants will explore catalyst datasets, generate material features, and develop an AI-based workflow to identify promising hydrogen production catalysts.
Tools: Python | Google Colab | Materials Databases | Machine Learning Models
Day 2: AI-Powered Catalyst Screening & Water Electrolysis Optimization
- Advanced electrocatalyst materials for hydrogen generation
- Transition metal-based catalysts and sustainable alternatives
- Computational approaches for catalyst performance prediction
- Introduction to DFT-based catalyst evaluation
- Catalyst activity descriptors and optimization strategies
- AI-assisted materials ranking and screening approaches
Hands-On Session : Virtual Screening of Hydrogen Electrocatalysts
Participants will perform computational screening and rank potential catalyst candidates using materials data and AI-based analysis workflows.
Tools: Materials Project | Matminer | Python | Scikit-learn
Day 3: Techno-Economic Optimization of Green Hydrogen Systems
- Integration of renewable energy with hydrogen production
- Electrolyzer efficiency and system optimization
- Hydrogen production economics and scalability challenges
- Capital and operational cost analysis
- Levelized Cost of Hydrogen (LCOH) estimation
- Future trends in AI-powered hydrogen systems and digital energy solutions
Hands-On Session : Techno-Economic Modeling of Green Hydrogen Production
Participants will analyze hydrogen production parameters, evaluate cost factors, and optimize hydrogen production strategies.
Tools: Python | Data Analysis Frameworks | Hydrogen Cost Models
Who Should Enrol?
- Chemical Engineering Students & Researchers
- Materials Science & Nanotechnology Researchers
- Electrochemistry Scientists
- Renewable Energy Professionals
- AI/ML Researchers working in Energy Applications
- Battery, Fuel Cell & Hydrogen Technology Researchers
- Faculty Members and Industry R&D Professionals
Important Dates
Registration Ends
September 28, 2026
IST 4:30 PM
Workshop Dates
September 28, 2026 – September 30, 2026
IST 5:00 PM
Workshop Outcomes
- Explain the fundamental principles, technologies, and applications of green hydrogen production
- Understand AI-driven approaches for materials discovery and electrocatalyst optimization
- Perform basic computational screening of hydrogen catalyst materials using data-driven workflows
- Apply machine learning concepts for predicting material properties and catalyst performance
- Analyze key factors influencing electrolyzer efficiency and hydrogen production performance
- Understand hydrogen production economics, cost drivers, and optimization strategies
- Identify opportunities for integrating AI into clean energy research and industrial applications
- Develop foundational skills for future research and innovation in AI-enabled hydrogen technologies
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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