AI-Driven Catalyst Discovery: High-Throughput Screening, Selectivity Prediction & Sustainable Process Optimization

Catalyst discovery is evolving with the integration of artificial intelligence, high-throughput data screening, and predictive modeling. These approaches enable researchers to analyze complex catalyst properties, predict catalytic activity and reaction selectivity, and identify promising materials and operating conditions more efficiently. This 3-day live, mentor-led, hands-on workshop introduces participants to data-driven catalyst discovery using experimental datasets, machine learning, and explainable AI. Participants will explore catalyst descriptors, develop predictive models using Random Forest and XGBoost, interpret model predictions through SHAP, and apply computational optimization to identify promising reaction conditions. Through practical exercises in Google Colab using freely available Python libraries, participants will develop research-relevant skills in catalyst informatics, predictive catalysis, and sustainability-aware reaction optimization.

To equip researchers and industry professionals with practical skills in AI-driven catalyst screening, catalytic performance prediction, and explainable machine learning for efficient catalyst selection and sustainable reaction optimization.

• Understand AI-driven catalyst discovery and high-throughput computational screening.
• Analyze catalyst descriptors and structure–performance relationships.
• Process and visualize experimental catalytic performance datasets.
• Develop machine learning models for catalytic activity and selectivity prediction.
• Evaluate predictive models using statistical performance metrics.
• Interpret influential catalyst properties and reaction parameters using SHAP.
• Apply constrained optimization to identify promising reaction conditions.

📅 Day 1: Catalyst Informatics & High-Throughput Screening

• AI-driven approaches to heterogeneous catalyst discovery. Experimental catalyst datasets and catalytic performance indicators. Catalyst descriptors: composition, metal loading and surface properties. Structure–activity and structure–selectivity relationships. Data preprocessing, normalization and descriptor engineering. High-throughput catalyst filtering and performance-based ranking. Catalyst shortlist generation and comparative visualization.

📅 Day 2: Machine Learning for Catalytic Activity & Selectivity Prediction

• Machine learning workflows for catalytic performance prediction. Feature selection for catalyst descriptors and reaction parameters. Catalytic activity prediction using Random Forest regression. Reaction selectivity prediction using XGBoost. Model training, cross-validation and hyperparameter tuning. Predictive performance evaluation using R², MAE and RMSE. Predicted-versus-experimental performance visualization.

📅 Day 3: Explainable AI & Sustainable Reaction Optimization

• Explainable AI for interpreting catalytic performance models. SHAP-based feature importance and catalyst descriptor analysis. Identification of key activity- and selectivity-driving parameters. Reaction temperature and operating-condition optimization. Multi-objective optimization of activity and selectivity. Sustainability-aware screening using energy-related operating proxies. AI-guided catalyst selection and experimental validation strategies.

• Screen Catalyst Candidates:Prepare experimental datasets and identify promising catalysts through descriptor-based analysis.
• Build Predictive Models:Develop Random Forest and XGBoost models for catalytic activity and reaction selectivity.
• Evaluate Model Performance:Assess prediction accuracy using R², MAE, RMSE, and visualization techniques.
• Interpret AI Predictions:Apply SHAP to identify influential catalyst descriptors and operating parameters.
• Optimize Reaction Conditions:Use computational optimization to recommend reaction conditions under defined performance and operating constraints.
• Support Research Decisions:Integrate screening, predictive modeling, and explainable AI into catalyst research workflows.

This workshop is designed for researchers, academicians, postgraduate students, and industry professionals working in: Catalysis & Chemical Sciences: Heterogeneous catalysis, catalyst design, reaction kinetics, and catalytic reaction engineering. Materials Science & Nanotechnology: Functional materials, nanocatalysts, surface engineering, and catalyst characterization. Chemical & Process Engineering: Chemical process optimization, industrial catalysis, and reaction engineering. Green Chemistry & Sustainability: Sustainable catalysis, resource-efficient chemical processes, and cleaner production technologies. Artificial Intelligence & Computational Research: Machine learning, materials informatics, predictive modeling, and data-driven catalyst discovery. Industrial R&D: Catalyst development, chemical manufacturing, process improvement, and sustainable technology development. Recommended Background: Basic understanding of chemistry, catalysis, or chemical engineering. Prior Python or machine learning experience is helpful but not mandatory.