MI Applications to Catalyst Design Series v1.0

From High-Performance Catalyst Discovery to Reaction Mechanism Elucidation - AI-Driven Catalyst Development

Series Overview

This series is a comprehensive 4-chapter educational content for learning how to apply Materials Informatics (MI) methods to catalyst design and development. You will acquire practical skills ranging from catalyst chemistry fundamentals to activity prediction using machine learning and composition exploration using Bayesian optimization.

Features:

✅ Catalyst-Focused: Activity/selectivity prediction, reaction mechanism analysis, catalyst deactivation prediction
✅ Practice-Oriented: 30 executable code examples using real data
✅ Latest Trends: Cutting-edge topics including hydrogen production, CO₂ reduction, ammonia synthesis
✅ Industrial Applications: Implementation patterns usable in real catalyst development projects

Total Learning Time: 100-120 minutes (including code execution and exercises)

Prerequisites:

Completion of the Materials Informatics Introduction Series is recommended
Python basics, fundamental concepts of machine learning
Basic chemistry knowledge (introductory inorganic and physical chemistry)

How to Learn

Recommended Learning Sequence

flowchart TD A["Chapter 1: Catalyst Chemistry and
the Role of MI"] --> B["Chapter 2: Catalyst-Specific
MI Methods"] B --> C["Chapter 3: Python Implementation
Catalyst Data Analysis"] C --> D["Chapter 4: Industrial Applications
Case Studies"] style A fill:#e3f2fd style B fill:#fff3e0 style C fill:#f3e5f5 style D fill:#e8f5e9

For catalyst beginners (first time learning catalyst chemistry):

Chapter 1 → Chapter 2 → Chapter 3 (basic code only) → Chapter 4
Duration: 80-100 minutes

With chemistry/materials science background:

Chapter 2 → Chapter 3 → Chapter 4
Duration: 70-90 minutes

For strengthening AI catalyst design implementation skills:

Chapter 3 (full code implementation) → Chapter 4
Duration: 60-75 minutes

Chapter Details

Chapter 1: Catalyst Chemistry and the Role of Materials Informatics

Difficulty: Introductory

Reading Time: 20-25 minutes

Learning Content

Catalyst Fundamentals

- Definition and role of catalysts (activation energy reduction)

- Homogeneous catalysts vs heterogeneous catalysts

- Three critical metrics for catalysts: activity, selectivity, stability

- Turnover number (TON) and turnover frequency (TOF)

Current State and Challenges in Catalyst Development

- Traditional development process: trial and error (several years to 10+ years)

- Challenge 1: Enormous composition space (combinations of metals, supports, promoters)

- Challenge 2: Complexity of reaction mechanisms

- Challenge 3: Difficulty of in situ measurements

How MI Solves Catalyst Development Challenges

- Activity and selectivity prediction (machine learning models)

- Optimal composition exploration (Bayesian optimization)

- Reaction mechanism analysis (first-principles calculation + ML)

- Deactivation prediction and lifetime evaluation

Impact on Catalyst Industry

- Market size: $40B (2024) → $60B (2030 projection)

- Major fields: petroleum refining, chemical manufacturing, environmental catalysts, energy conversion

- Contribution to carbon neutrality: CO₂ reduction, green hydrogen production

Learning Objectives

✅ Explain basic catalyst concepts (activity, selectivity, stability)
✅ Compare characteristics of homogeneous and heterogeneous catalysts
✅ List limitations of traditional catalyst development with concrete examples
✅ Understand the value MI brings to catalyst development

Read Chapter 1 →

Chapter 2: Catalyst Design-Specific MI Methods

Difficulty: Intermediate

Reading Time: 25-30 minutes

Learning Content

Catalyst Descriptors

- Electronic descriptors: d-orbital occupancy, work function, band center

- Geometric descriptors: crystal structure, surface area, pore size distribution

- Compositional descriptors: elemental composition, atomic radius, electronegativity

Sabatier principle and d-band theory

Activity and Selectivity Prediction Models

- Regression models: Random Forest, Gradient Boosting, Neural Network

- Classification models: active catalyst vs inactive catalyst

- Prediction accuracy improvement through ensemble learning

- Uncertainty quantification

Catalyst Exploration via Bayesian Optimization

- Acquisition functions: EI, UCB, PI

- Surrogate models using Gaussian Process

- Active learning cycle

- Multi-fidelity optimization (computational cost reduction)

Reaction Mechanism Analysis

- Integration with DFT calculations

- Automation of transition state search

- Generation of reaction energy diagrams

- Microkinetic modeling

Major Databases and Tools

- Catalysis-Hub: Catalytic reaction energy database

- Materials Project: Crystal structure and property data

- NIST Kinetics Database: Reaction rate constants

- ASE (Atomic Simulation Environment): Python catalyst calculation tool

Learning Objectives

✅ Understand four types of catalyst descriptors and their appropriate use
✅ Explain the construction process for activity prediction models
✅ Understand the principles and application methods of Bayesian optimization
✅ Comprehend integration methods of DFT calculations and ML

Read Chapter 2 →

Chapter 3: Python Implementation of Catalyst MI - ASE & Machine Learning Practice

Difficulty: Intermediate

Reading Time: 35-45 minutes

Code Examples: 30 (all executable)

Learning Content

Environment Setup

conda install -c conda-forge ase

- Other libraries: pandas, scikit-learn, scikit-optimize, matminer

Catalyst Data Acquisition and Preprocessing (7 code examples)

- Example 1: Data acquisition from Catalysis-Hub

- Example 2: Crystal structure loading and visualization (ASE)

- Example 3: Adsorption energy calculation

- Example 4: Surface energy calculation

- Example 5: Automatic descriptor calculation (matminer)

- Example 6: Data cleaning and outlier removal

- Example 7: Train/test data splitting

Activity Prediction Models (8 code examples)

- Example 8: Random Forest regression (activity prediction)

- Example 9: Gradient Boosting (XGBoost)

- Example 10: Neural Network (Keras)

- Example 11: Ensemble model (Voting Regressor)

- Example 12: Feature importance analysis (SHAP)

- Example 13: Cross-validation and hyperparameter tuning

- Example 14: Parity plot (predicted vs measured)

- Example 15: Uncertainty quantification (prediction intervals)

Composition Exploration via Bayesian Optimization (7 code examples)

- Example 16: Gaussian Process regression

- Example 17: Comparison of acquisition functions (EI, UCB, PI)

- Example 18: Bayesian optimization loop (1D optimization)

- Example 19: Multi-objective Bayesian optimization (activity & selectivity)

- Example 20: Integration with design of experiments (DOE)

- Example 21: Pareto front visualization

- Example 22: Optimal composition proposal

Integration with DFT Calculations (5 code examples)

- Example 23: Structure optimization with ASE

- Example 24: Adsorption site exploration

- Example 25: Transition state search (NEB method)

- Example 26: Vibrational analysis and zero-point energy

- Example 27: Reaction energy diagram

Reaction Kinetics Analysis (3 code examples)

- Example 28: Arrhenius plot and activation energy

- Example 29: Microkinetic model

- Example 30: Catalyst deactivation prediction (time series analysis)

Project Challenge

- Goal: Discover optimal composition for CO₂ reduction catalyst (target: Faradaic efficiency > 80%)

- Steps:

1. Acquire CO₂ reduction data from Catalysis-Hub

2. Descriptor calculation and feature engineering

3. Train Random Forest model

4. Explore optimal composition with Bayesian optimization

5. Validate prediction results

Learning Objectives

✅ Manipulate and visualize catalyst structures using ASE
✅ Implement catalyst activity prediction models and evaluate performance
✅ Explore optimal compositions using Bayesian optimization
✅ Integrate DFT calculation results with ML
✅ Execute actual catalyst development projects

Read Chapter 3 →

Chapter 4: Recent Case Studies and Industrial Applications in Catalyst Development

Difficulty: Intermediate to Advanced

Reading Time: 20-25 minutes

Learning Content

5 Detailed Case Studies

Case Study 1: Green Hydrogen Production - Water Electrolysis Catalyst Optimization

- Challenge: High-efficiency, low-cost oxygen evolution reaction (OER) catalyst

- Approach: Bayesian optimization + high-throughput experimentation

- Achievement: 50% reduction in IrO₂ usage while maintaining activity

- Companies: Toyota Motor Corporation, Panasonic

Case Study 2: CO₂ Reduction Catalyst - Carbon Recycling

- Technology: Cu-based catalyst for CO₂ → C₂H₄ (ethylene)

- ML method: Graph Neural Network

- Achievement: Faradaic efficiency 72% → 89%

- Impact: Contribution to carbon neutrality realization

Case Study 3: Ammonia Synthesis - Next-Generation Haber-Bosch Catalyst

- Current state: Fe-based catalyst (unchanged for 100 years)

- New catalyst: Ru/CeO₂ (MI-designed)

- Achievement: Reaction temperature 400°C → 300°C, pressure reduction

Nature Communications

Case Study 4: Automotive Catalyst - Noble Metal Reduction

- Challenge: High cost of PtPdRh catalysts (>$50/g)

- Strategy: Single-atom catalyst (SAC) design

- ML technology: Transfer learning (utilizing existing catalyst data)

- Achievement: 90% reduction in Pt usage, equivalent activity

Case Study 5: Pharmaceutical Intermediate Synthesis - Asymmetric Catalyst

- Approach: Automated design of chiral ligands

- AI method: Molecular Transformer + RL

- Achievement: Enantiomeric excess (ee) 95% → 99.5%

- Companies: Merck, Novartis

Catalyst AI Strategies of Major Companies

Chemical/Energy Companies:

BASF: Building data-driven catalyst development platform
Shell: AI design of CO₂ reduction catalysts
Sabic: Plastic recycling catalyst optimization
Air Liquide: Machine learning prediction for hydrogen production catalysts

Research Institutions:

NREL (USA): Catalyst database construction and ML applications
AIST (Japan): AI catalyst screening
Fraunhofer (Germany): Industrial catalyst optimization
Best Practices for Catalyst AI

Keys to Success:

✅ Securing high-quality data (experiments + DFT calculations)
✅ Utilizing domain knowledge (Sabatier principle, d-band theory)
✅ Iteration with experiments (Active Learning cycle)
✅ Scalability (integration with high-throughput experimentation)

Common Pitfalls:

❌ Descriptor selection errors (features without physical meaning)
❌ Overfitting (complex models with limited data)
❌ Ignoring applicability domain
❌ Delayed experimental validation (overemphasis on computation)
Carbon Neutrality and Catalysts

- Green Hydrogen: Efficiency improvement of water electrolysis catalysts

- CO₂ Utilization: CO₂ → chemicals/fuels (CCU technology)

- Biomass Conversion: Cellulose → chemicals

- Methanation: CO₂ + H₂ → CH₄ (city gas conversion)

Career Paths in Catalyst Research

Academia:

Positions: Postdoc, Assistant Professor, Associate Professor
Salary: ¥5-12M/year (Japan), $60-120K (USA)
Institutions: University of Tokyo, Tohoku University, Hokkaido University, MIT, Stanford

Industry:

Positions: Catalyst Scientist, Process Engineer
Salary: ¥6-15M/year (Japan), $70-200K (USA)
Companies: BASF, Shell, Toyota, Mitsubishi Chemical

Startups:

Examples: Solugen (biocatalysts), Twelve (CO₂ reduction)
Risk/Return: High risk, high impact
Required skills: Technical + Business

Learning Objectives

✅ Explain 5 success cases of catalyst AI applications
✅ Compare and evaluate strategies of major companies
✅ Understand the role of catalysts in carbon neutrality
✅ Plan career paths in catalyst research

Read Chapter 4 →

Overall Learning Outcomes

Upon completion of this series, you will acquire the following skills and knowledge:

Knowledge Level

✅ Explain fundamental catalyst principles (activity, selectivity, stability)
✅ Understand relationships between catalyst descriptors and properties
✅ Comprehend industrial trends in AI catalyst development
✅ Detail 5 or more recent case studies

Practical Skills

✅ Manipulate and visualize catalyst structures using ASE
✅ Implement catalyst activity prediction models
✅ Explore optimal compositions using Bayesian optimization
✅ Integrate DFT calculation results with ML

Application Capabilities

✅ Design new catalyst development projects
✅ Evaluate industrial case studies and apply them to your own research
✅ Consider contributions to carbon neutrality realization

Recommended Learning Patterns

Pattern 1: Complete Mastery (For Catalyst Beginners)

Target: First-time learners of catalysts

Duration: 2-3 weeks

Week 1:
Day 1-2: Chapter 1 (Catalyst fundamentals)
Day 3-4: Chapter 2 (MI methods)
Day 5-7: Chapter 2 exercises, terminology review

Week 2:
Day 1-3: Chapter 3 (Data acquisition/preprocessing, Examples 1-7)
Day 4-6: Chapter 3 (Activity prediction, Examples 8-15)
Day 7: Chapter 3 (Bayesian optimization, Examples 16-22)

Week 3:
Day 1-3: Chapter 3 (DFT integration/kinetics, Examples 23-30)
Day 4-5: Project challenge
Day 6-7: Chapter 4 (Case studies)

Pattern 2: Fast Track (With Chemistry Background)

Target: Those with fundamentals in chemistry/materials science

Duration: 1-2 weeks

Day 1-2: Chapter 2 (Catalyst descriptors and MI methods)
Day 3-5: Chapter 3 (Full code implementation)
Day 6: Project challenge
Day 7-8: Chapter 4 (Industrial applications)

Pattern 3: Implementation Skills Enhancement (For ML Practitioners)

Target: Machine learning practitioners

Duration: 3-5 days

Day 1: Chapter 2 (Catalyst descriptors)
Day 2-3: Chapter 3 (Full code implementation)
Day 4: Project challenge
Day 5: Chapter 4 (Industrial case studies)

FAQ

Q1: Can I understand this without chemistry knowledge?

A: Chapters 1 and 2 are easier to understand with basic inorganic and physical chemistry knowledge, but we explain important concepts as they arise. For Chapter 3's code implementation, the ASE library handles chemical calculations, so it's executable with programming skills alone.

Q2: ASE installation is difficult.

A: We recommend installing ASE via conda:

conda create -n catalyst_env python=3.9
conda activate catalyst_env
conda install -c conda-forge ase

It's also usable in Google Colab (!pip install ase).

Q3: Is DFT calculation knowledge required?

A: Chapters 1-2 and Examples 1-22 of Chapter 3 require no DFT knowledge. Examples 23-27 handle DFT calculations but explain basic concepts, so beginners can understand them. For deeper learning, we recommend studying specialized DFT textbooks separately.

Q4: Isn't Bayesian optimization difficult?

A: Chapter 3 teaches progressively:

Gaussian Process regression basics
Understanding acquisition functions
Optimization in 1D
Extension to multi-dimensional/multi-objective

scikit-optimize

Q5: Can I become a catalyst development expert with just this series?

A: This series targets "introductory to intermediate" level. To reach expert level:

Build foundations with this series (2-4 weeks)
Read papers in-depth (ACS Catalysis, Nature Catalysis) (3-6 months)
Execute independent projects (6-12 months)
Present at conferences and write papers (1-2 years)

Next Steps

Recommended Actions After Series Completion

Immediate (1-2 weeks):

✅ Create portfolio on GitHub
✅ Publish project challenge results
✅ Add "Computational Catalysis" skill to LinkedIn

Short-term (1-3 months):

✅ Independent project with Catalysis-Hub data
✅ Read 3 papers in-depth from Chapter 4 references
✅ Join ASE Users community

Medium-term (3-6 months):

✅ Read 10 papers in-depth (ACS Catalysis, J. Catal.)
✅ Contribute to open-source projects
✅ Present at domestic conferences (Catalysis Society, Chemical Engineering Society)

Long-term (1+ years):

✅ Present at international conferences (ICAT, NAM)
✅ Submit peer-reviewed papers
✅ Work in catalyst-related positions (chemical companies or research institutions)

Feedback and Support

Created: October 19, 2025

Version: 1.0

Author: Dr. Yusuke Hashimoto, Tohoku University

Please send feedback and questions to:

Email: yusuke.hashimoto.b8@tohoku.ac.jp

License

This content is published under the CC BY 4.0 license.

Permitted:

✅ Free viewing and downloading
✅ Educational use
✅ Modification and derivative works

Conditions:

📌 Display author credit
📌 Indicate modifications when made
📌 Contact before commercial use

Let's Get Started!

Chapter 1: Catalyst Chemistry and the Role of Materials Informatics →

Update History

2025-10-19: v1.0 Initial release

Your journey to create a sustainable future with AI-driven catalyst development starts here!

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