New Year Offer End Date: 30th April 2024
Program
AI-Driven Single-Atom Catalyst Discovery for CO₂ Reduction & Green Hydrogen: Machine Learning, Adsorption Energy & High-Throughput Screening
From Atomic Active Sites to AI-Prioritised Electrocatalysts for Carbon Conversion and Clean Hydrogen
Skills you will gain:
About Program:
Single-atom catalysts (SACs) are promising materials for sustainable energy conversion due to their high atomic efficiency and tunable active sites. This three-day hands-on workshop introduces an AI-accelerated workflow for discovering SACs for CO₂ reduction (CO₂RR) and hydrogen evolution (HER). Participants will learn to represent catalyst structures, predict adsorption energies, perform AI-assisted atomic relaxation and screen catalyst libraries. The workshop also explores how metal centres, supports and coordination environments influence catalytic activity and selectivity. By the end, participants will generate a prioritised shortlist of high-potential catalyst candidates for further validation.
Aim: To provide participants with practical training in combining single-atom catalysis, materials informatics, machine learning, equivariant atomistic AI and virtual high-throughput screening for accelerated discovery of electrocatalysts for CO₂ conversion and green-hydrogen production.
Program Objectives:
- Understand single-atom catalyst structures, active sites and metal–support interactions.
- Represent SAC structures as machine-readable materials descriptors.
- Identify adsorption descriptors governing CO₂RR and HER performance.
- Build ML models for adsorption-energy prediction.
- Interpret catalyst predictions using explainable AI.
- Apply pretrained atomistic AI models for energy and force prediction.
- Perform AI-assisted catalyst–adsorbate structure relaxation.
- Compare CO₂RR intermediates against competing hydrogen adsorption.
- Construct a virtual high-throughput catalyst-screening workflow.
- Rank SAC candidates using activity, selectivity, stability and feasibility criteria.
What you will learn?
🗓️ Day 1: Single-Atom Catalyst Representation & Machine Learning Activity Prediction
- Understanding the fundamentals of Single-Atom Catalysts (SACs), isolated active sites, metal–support interactions, and coordination environments such as M–N₄ and defect-engineered supports.
- Representing SAC structures, catalytic surfaces, active metal centres, and adsorbates as machine-readable materials descriptors.
- Computing coordination number, electronegativity, elemental, structural, and adsorption-related features for catalyst modelling.
- Building machine-learning surrogate models to predict adsorption and binding energies relevant to CO₂ reduction and hydrogen evolution.
- Evaluating model reliability using MAE, R², cross-validation, feature importance, and explainable-AI methods.
🧰 Tools
- Matminer: Materials descriptors and feature engineering.
- ASE: Atomic structure and catalyst-surface representation.
- Catalysis-Hub / Public Catalyst Datasets: Adsorption-energy and catalyst-property data.
- Scikit-learn / XGBoost: Machine-learning surrogate modelling.
- SHAP: Interpretation of catalyst descriptors and ML predictions.
🧪 Hands-on Lab (Google Colab)
- Notebook: Predicting Adsorption Energies of Single-Atom Catalysts for CO₂ Reduction Using Machine Learning.
- Task: Generate SAC descriptors, train an adsorption-energy prediction model, evaluate its accuracy, and identify the structural and chemical features governing catalytic activity.
- Deliverable: Validated SAC adsorption-energy model with MAE, R², cross-validation results, SHAP feature analysis, and an initial shortlist of promising single-atom catalysts.
🗓️ Day 2: Equivariant AI, Atomic Relaxation & Catalyst–Adsorbate Interactions
- Understanding graph-based and 3D equivariant representations of atomic structures for catalyst modelling.
- Representing SAC active sites, supports, and adsorbates as atomic graphs for energy and force prediction.
- Applying pretrained AI interatomic potentials to accelerate catalyst–adsorbate energy calculations and structural relaxation.
- Performing AI-guided geometry optimisation to identify stable catalyst–adsorbate configurations and local-energy minima.
- Comparing adsorption behaviour of key intermediates involved in CO₂RR, HER, and OER.
- Interpreting metal–adsorbate distances, coordination changes, predicted forces, and adsorption energies.
🧰 Tools
- FAIR-Chem / UMA: Pretrained universal models for atomic energy and force prediction.
- ASE: Construction, manipulation, and relaxation of catalyst–adsorbate structures.
- PyTorch / Graph-Based AI Concepts: Atomic graph representation and model interpretation.
- Py3Dmol / ASE Visualizer: Interactive visualization of relaxed SAC structures.
🧪 Hands-on Lab (Google Colab)
- Notebook: AI-Accelerated Atomic Relaxation of Single-Atom Electrocatalysts for CO₂RR and Green Hydrogen.
- Task: Construct a representative SAC–adsorbate system, perform AI-guided atomic relaxation, calculate adsorption energies, and compare competing catalytic intermediates.
- Deliverable: Optimised 3D SAC–adsorbate structures with predicted energies, forces, relaxation trajectories, and adsorption-energy comparison.
🗓️ Day 3: Virtual High-Throughput Screening, Selectivity & Multi-Objective Catalyst Ranking
- Designing Virtual High-Throughput Screening (VHTS) pipelines for large libraries of single-atom catalysts.
- Predicting and comparing activity descriptors for CO₂ Reduction Reaction (CO₂RR), Hydrogen Evolution Reaction (HER), and Oxygen Evolution Reaction (OER).
- Analysing competition between CO₂RR and HER to identify selective catalysts for carbon conversion.
- Generating adsorption-energy relationships, activity maps, and volcano-type plots for catalyst interpretation.
- Applying multi-objective optimisation using catalytic activity, selectivity, structural stability, metal cost, and elemental availability.
- Using explainability and uncertainty-aware filtering to distinguish robust catalyst candidates from unreliable ML predictions.
- Ranking SAC candidates and identifying high-priority structures for subsequent DFT calculation or experimental validation.
🧰 Tools
- FAIR-Chem / UMA: AI-based catalyst property and energy prediction.
- Catalysis-Hub: Catalyst and adsorption-energy datasets.
- XGBoost / Scikit-learn: Activity and selectivity prediction.
- SHAP: Explainable-AI analysis.
- Optuna: Multi-objective catalyst optimisation.
- Matplotlib / Pandas: Screening, ranking, and research-ready visualization.
🧪 Hands-on Lab (Google Colab)
- Notebook: Building an AI-Powered Virtual Screening Engine for Single-Atom Catalysts in CO₂ Reduction and Green Hydrogen.
- Task: Screen a virtual SAC library, predict adsorption/activity descriptors, analyse CO₂RR–HER selectivity, apply multi-objective filters, and rank candidates for further validation.
- Deliverable: Ranked shortlist of high-potential single-atom catalysts with activity/selectivity maps, explainable-AI results, optimisation scores, and publication-ready visualizations.
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Undergraduate and Postgraduate Students in Nanotechnology, Materials Science, Chemistry, Chemical Engineering, Physics, Energy Science and related disciplines.
- Ph.D. Scholars and Researchers working in catalysis, CO₂ reduction, hydrogen evolution, electrochemistry, computational chemistry and materials informatics.
- Faculty and Academicians interested in AI/ML-assisted catalyst design, atomistic modelling and high-throughput materials screening.
- Materials Scientists and Computational Chemists exploring adsorption-energy prediction, catalyst–adsorbate interactions and structure–activity relationships.
- Electrochemistry and Energy Researchers working on CO₂RR, HER, green hydrogen and sustainable energy conversion.
- Industry and R&D Professionals from catalysis, chemicals, hydrogen, CO₂ utilization, clean energy and advanced-materials sectors.
Career Supporting Skills
Program Outcomes
- Build machine-learning models to predict single-atom catalyst adsorption energies and activity.
- Analyse how metal centres, supports and coordination environments influence CO₂RR and HER performance.
- Perform AI-assisted catalyst–adsorbate relaxation and interpret predicted energies and atomic interactions.
- Evaluate CO₂RR–HER selectivity using adsorption-energy descriptors and activity maps.
- Apply explainable AI and multi-objective screening to compare catalyst candidates.
- Generate a ranked shortlist of high-potential single-atom catalysts for further DFT or experimental validation.