AI-Driven Catalyst Discovery: Machine Learning, Explainable AI & Reaction Optimization

To equip researchers and professionals with practical skills in AI-driven catalyst screening, machine learning-based performance prediction, explainable AI and reaction optimization, enabling data-driven catalyst selection and more informed experimental research.

• Explore AI-Driven Catalyst Discovery: Understand how catalyst informatics and machine learning support catalyst screening, performance analysis and research decision-making.
• Develop Catalyst Data Analysis Skills: Learn to preprocess experimental datasets, engineer relevant descriptors and analyze catalyst properties using Python.
• Perform Data-Driven Catalyst Screening: Apply virtual high-throughput screening and performance-based ranking to identify promising catalyst candidates.
• Build Machine Learning Prediction Models: Develop Random Forest and XGBoost models to predict catalytic activity and reaction selectivity.
• Interpret AI Predictions: Apply SHAP-based explainable AI to identify influential catalyst descriptors and understand model behavior.
• Optimize Catalytic Reaction Conditions: Use predictive models and numerical optimization techniques to explore reaction conditions and activity–selectivity trade-offs.
• Support Sustainable Catalyst Research: Incorporate relevant operating-condition and energy-related indicators into catalyst evaluation and prioritization.

📅 Day 1: Catalyst Informatics, Descriptor Engineering & High-Throughput Screening

• Introduction to AI-driven heterogeneous catalyst discovery and catalyst informatics.
• Experimental catalyst datasets and catalytic performance indicators: conversion, yield, activity, and selectivity.
• Catalyst descriptors: chemical composition, metal loading, surface area, particle size, and physicochemical properties.
• Structure–activity and structure–selectivity relationships in catalytic systems.
• Data preprocessing, missing-value handling, normalization, and descriptor engineering.
• Exploratory data analysis, descriptor correlation, and catalyst performance visualization.
• Virtual high-throughput catalyst screening, performance-based ranking, and candidate shortlisting.

🛠️ Hands-On Lab: Data-Driven Catalyst Screening & Ranking

• Clean and analyze experimental catalyst datasets, explore key catalyst descriptors, generate correlation plots, visualize catalytic performance, and rank promising catalyst candidates through data-driven screening.

🧰 Tools Covered: Google Colab | Python | Pandas | Seaborn

📊 Deliverable: Cleaned catalyst dataset, descriptor correlation plots, performance visualizations, and ranked catalyst shortlist.

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

• Introduction to machine learning workflows for catalytic activity and selectivity prediction.
• Feature selection, descriptor preparation, and training/testing dataset development.
• Catalytic activity prediction using Random Forest regression.
• Reaction selectivity prediction using XGBoost regression.
• Model training, cross-validation, hyperparameter tuning, and overfitting prevention.
• Predictive performance evaluation using R², MAE, and RMSE.
• Predicted-versus-experimental performance visualization, model comparison, and candidate prediction.

🛠️ Hands-On Lab: ML-Based Catalyst Performance Prediction

• Train Random Forest and XGBoost models using experimental catalyst datasets, evaluate predictive accuracy, compare predicted and experimental catalytic performance, and generate predictions for catalyst candidates.

🧰 Tools Covered: Google Colab | Python | Scikit-learn | XGBoost

📊 Deliverable: Trained predictive models, evaluation metrics, predicted-versus-experimental plots, and catalyst performance predictions.

🗓️ Day 3: Explainable AI, Multi-Objective Optimization & Sustainable Catalyst Design

• Introduction to explainable AI for interpreting catalyst performance prediction models.
• SHAP-based feature importance analysis and catalyst descriptor interpretation.
• Identification of influential composition, structural, and reaction-condition parameters affecting activity and selectivity.
• AI-assisted optimization of reaction temperature and operating conditions using trained predictive models.
• Multi-objective optimization of catalytic activity and selectivity using SciPy.
• Pareto-based catalyst selection and sustainability-aware screening using energy-related operating proxies.
• AI-guided catalyst prioritization, prediction reliability assessment, and experimental validation planning.

🛠️ Hands-On Lab: SHAP-Guided Catalyst Interpretation & Reaction Optimization

• Interpret trained machine learning models using SHAP, identify influential catalyst descriptors, optimize reaction conditions within defined constraints, analyze activity–selectivity trade-offs, and prioritize promising catalyst candidates for experimental validation.

🧰 Tools Covered: Google Colab | Python | SHAP | XGBoost | SciPy

📊 Deliverable: SHAP feature importance plots, optimized reaction parameters, activity–selectivity trade-off visualization, and prioritized catalyst recommendations.

• PhD Scholars & Postdoctoral Researchers: Working in heterogeneous catalysis, catalyst development, reaction engineering and computational chemistry.
• Chemistry & Chemical Engineering Researchers: Interested in catalytic activity prediction, reaction selectivity and data-driven process optimization.
• Materials Science & Nanotechnology Researchers: Studying nanocatalysts, functional materials, metal-based catalysts and catalyst structure–property relationships.
• Green Chemistry & Sustainability Researchers: Exploring energy-efficient catalytic processes and sustainable chemical technologies.
• Computational Chemistry & AI Researchers: Interested in machine learning, catalyst informatics, predictive modeling and explainable AI.
• Industrial R&D Professionals: Working in chemical manufacturing, catalyst development, process engineering and performance optimization.
• M.Tech, M.Sc. & Advanced Undergraduate Students: From chemistry, chemical engineering, materials science, nanotechnology and related disciplines seeking practical exposure to AI-assisted catalyst research.

• Analyze Catalyst Datasets: Clean, preprocess and visualize experimental catalyst data using Python and Pandas.
• Identify Key Catalyst Descriptors: Examine relationships between catalyst composition, physicochemical properties and catalytic performance.
• Screen Promising Catalysts: Apply data-driven filtering and performance-based ranking to shortlist catalyst candidates.
• Predict Catalytic Performance: Train and evaluate Random Forest and XGBoost models for catalytic activity and selectivity prediction.
• Apply Explainable AI: Generate SHAP plots to interpret model predictions and identify influential catalyst parameters.
• Optimize Reaction Conditions: Explore reaction temperature and operating parameters through model-based numerical optimization.
• Develop a Research-Ready Workflow: Integrate catalyst screening, predictive modeling, explainable AI and reaction optimization into a reproducible computational pipeline.