Catalysis and Artificial Intelligence (AI) for CO₂ Mitigation
Catalysis Meets AI: Transforming CO₂ into Tomorrow’s Fuel.
Skills you will gain:
About Program:
The rapid rise in atmospheric CO₂ has led to severe climate change impacts worldwide, demanding urgent and innovative solutions. Catalysis and AI present a unique convergence—where advanced materials science meets intelligent data-driven optimization—to accelerate breakthroughs in CO₂ mitigation.
This 3-day mentor-led program introduces participants to state-of-the-art techniques for CO₂ capture and conversion, covering sorbents like graphene, MOFs, COFs, and zeolites, alongside catalytic reduction methods ranging from borohydrides to electrocatalysis, photocatalysis, and biocatalysis. AI’s role in predictive catalyst design, process optimization, and simulation will also be discussed, preparing participants to tackle both academic and industrial challenges in carbon management.
Aim: This workshop aims to bridge the fields of catalysis, chemistry, and artificial intelligence in tackling CO₂ emissions. Participants will gain knowledge of carbon capture, catalytic conversion, and AI-driven solutions for mitigating climate change. The program emphasizes both theoretical insights and industrially adaptable applications.
Program Objectives:
- To understand the role of CO₂ in climate change and the greenhouse effect.
- To explore advanced sorbents and adsorbents for CO₂ capture.
- To analyze catalytic and AI-driven pathways for CO₂ conversion into fuels and chemicals.
- To develop skills in applying AI/ML for catalyst design and process optimization.
- To prepare participants for industrial and research roles in carbon mitigation technologies.
What you will learn?
Day 1 – CO₂ Emissions, Capture & Sorbents
- Climate science basics: GHGs, radiative forcing, impacts
- CO₂ capture & utilization (CCS/CCU): absorption, adsorption, membranes
- Sorbents: Graphene, Activated Carbon — pore structure, isotherms, selectivity
- AI intro for capture: datasets, features, baseline ML for adsorption prediction
Day 2 – Catalytic Conversion & AI Design
- Materials: MOFs, COFs, Zeolites, POMs — structure, uptake, stability
- CO₂ → fuels/chemicals: formate, methanol, syngas; borohydride route
- Electrocatalysis, photocatalysis, biocatalysis (cyanobacteria) overview
- AI for catalysts: QSAR/graph ML, model interpretability, rapid candidate ranking
Day 3 – Practical (Hands-On)
- Data prep: clean/structure adsorption & catalyst datasets
- Build models (Python): train/test, metrics (R², MAE), feature importance/SHAP
- Optimization: Cu nanoparticle electrocatalysis; Bayesian/active learning
- Mini project: end-to-end pipeline → shortlist best CO₂-reduction candidates
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Computational Biology, Environmental Science, Chemistry, Chemical Engineering, Nanotechnology, or related fields.
- Professionals in energy, pharma, chemicals, diagnostics, food safety, or environmental sectors.
- Data scientists and AI/ML engineers interested in applying their skills to sustainable chemistry and climate technology.
- Individuals with a keen interest in the convergence of life sciences, chemistry, catalysis, and artificial intelligence.
Career Supporting Skills
Program Outcomes
- Comprehensive understanding of CO₂ capture mechanisms and adsorbents.
- Knowledge of catalytic, electrocatalytic, photocatalytic, and biocatalytic CO₂ conversion methods.
- Ability to integrate AI/ML in catalyst discovery and optimization.
- Exposure to industrial applications and sustainability-driven innovations.
- Readiness to contribute to research, industry, and policy on CO₂ mitigation.