MI Applications in Battery Materials Design Series v1.0

AI-Driven Materials Discovery Accelerating Next-Generation Battery Development

Series Overview

This series is a comprehensive 4-chapter educational content designed to teach how to apply Materials Informatics (MI) methods to battery materials design and development. From lithium-ion batteries to all-solid-state batteries, you will acquire practical skills including capacity prediction, cycle life evaluation, and fast charging optimization.

Features:

✅ Battery Materials Focused: Design of cathodes, anodes, electrolytes, and solid electrolytes
✅ Practice-Oriented: 30 executable code examples using real data
✅ Latest Technologies: Next-generation technologies including all-solid-state batteries, Li-S batteries, and Na-ion batteries
✅ Industrial Applications: Implementation cases for EVs, stationary energy storage, and IoT devices

Total Learning Time: 110-130 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 (fundamentals of electrochemistry and inorganic chemistry)

How to Learn

Recommended Learning Sequence

flowchart TD A["Chapter 1: Battery Materials
Fundamentals & MI Role"] --> B["Chapter 2: Battery-Specific
MI Methods"] B --> C["Chapter 3: Python Implementation
Battery 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 Battery Materials Beginners:

Chapter 1 → Chapter 2 → Chapter 3 (basic code only) → Chapter 4
Duration: 90-110 minutes

With Chemistry/Materials Science Background:

Chapter 2 → Chapter 3 → Chapter 4
Duration: 80-100 minutes

Strengthening AI Battery Design Implementation Skills:

Chapter 3 (full code implementation) → Chapter 4
Duration: 70-90 minutes

Chapter Details

Chapter 1: Battery Materials Fundamentals and the Role of Materials Informatics

Difficulty: Beginner

Reading Time: 25-30 minutes

Learning Content

Battery Fundamentals

- Battery operation principles (redox reactions, ionic conduction)

- Key performance indicators: energy density, power density, cycle life, safety

- Lithium-ion battery structure (cathode, anode, electrolyte, separator)

- Battery types: LIB, all-solid-state batteries, Li-S, Li-air, Na-ion

Current Status and Challenges in Battery Materials Development

- Challenge 1: Energy density improvement (300 → 500 Wh/kg)

- Challenge 2: Fast charging (80% charge in 10 minutes)

- Challenge 3: Cycle life extension (500 → 2,000 cycles)

- Challenge 4: Safety improvement (thermal runaway prevention)

- Challenge 5: Cost reduction (Co reduction, Na utilization)

Battery Development Challenges Solved by MI

- Capacity and voltage prediction (machine learning models)

- Cycle degradation prediction (time series analysis)

- New materials discovery (high-throughput screening)

- Charging protocol optimization (reinforcement learning)

Impact on the Battery Industry

- Market size: $50B (2024) → $120B (2030 forecast)

- Main sectors: EVs (70%), stationary energy storage (20%), IoT (10%)

- Contribution to carbon neutrality: key to renewable energy stabilization

Learning Objectives

✅ Explain basic battery concepts (capacity, voltage, energy density)
✅ Compare characteristics of LIB and next-generation batteries
✅ List battery materials development challenges with specific examples
✅ Understand the value MI brings to battery development

Read Chapter 1 →

Chapter 2: MI Methods Specialized for Battery Materials Design

Difficulty: Intermediate

Reading Time: 30-35 minutes

Learning Content

Battery Materials Descriptors

- Structural descriptors: crystal structure, lattice constants, space groups

- Electronic descriptors: band gap, density of states, work function

- Chemical descriptors: ionic radius, electronegativity, oxidation state

- Electrochemical descriptors: potential, ionic conductivity, diffusion coefficient

Capacity and Voltage Prediction Models

- Regression models: Random Forest, XGBoost, Neural Network

- Physics-based models: First-principles calculations + ML

- Graph Neural Network (GNN): direct prediction from crystal structures

- Transfer Learning: transfer learning from similar material systems

Cycle Degradation Prediction

- Time series data analysis (LSTM, GRU)

- Degradation mechanism classification (SEI growth, lithium plating, structural collapse)

- Lifetime prediction models (Remaining Useful Life: RUL)

- Anomaly detection (failure precursor detection)

High-Throughput Materials Screening

- Bayesian optimization for composition search

- Integration with DFT calculations (Multi-fidelity Optimization)

- Database utilization: Materials Project, Battery Data Genome

- Active Learning: efficient experimental planning

Major Databases and Tools

- Materials Project: electrochemical properties of 140,000+ materials

- Battery Data Genome: charge-discharge curve data

- NIST Battery Database: standard datasets

- PyBaMM: Python battery modeling library

Learning Objectives

✅ Understand the 4 types of battery materials descriptors and how to use them
✅ Explain the construction procedure for capacity and voltage prediction models
✅ Understand cycle degradation prediction methods
✅ Grasp integration methods for DFT calculations and ML

Read Chapter 2 →

Chapter 3: Python Implementation of Battery MI - PyBaMM & Machine Learning Practice

Difficulty: Intermediate

Reading Time: 40-50 minutes

Code Examples: 30 (all executable)

Learning Content

Environment Setup

pip install pybamm

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

Battery Data Acquisition and Preprocessing (7 Code Examples)

- Example 1: Retrieve cathode material data from Materials Project

- Example 2: Load and visualize charge-discharge curves

- Example 3: Potential profile calculation

- Example 4: Capacity calculation and coulombic efficiency

- Example 5: Automatic descriptor calculation (matminer)

- Example 6: Data cleaning and outlier removal

- Example 7: Train/Test data splitting

Capacity and Voltage Prediction Models (8 Code Examples)

- Example 8: Random Forest regression (capacity prediction)

- Example 9: XGBoost (voltage prediction)

- Example 10: Neural Network (Keras)

- Example 11: Graph Neural Network (PyTorch Geometric)

- Example 12: Transfer Learning (learning from similar material systems)

- Example 13: Feature importance analysis (SHAP)

- Example 14: Cross-validation and hyperparameter tuning

- Example 15: Parity Plot (prediction vs. measurement)

Cycle Degradation Prediction (7 Code Examples)

- Example 16: Time series data preparation for charge-discharge curves

- Example 17: LSTM (Long Short-Term Memory) model

- Example 18: GRU (Gated Recurrent Unit) model

- Example 19: Lifetime prediction (RUL: Remaining Useful Life)

- Example 20: Degradation rate prediction

- Example 21: Anomaly detection (Isolation Forest)

- Example 22: SOH (State of Health) estimation

Materials Search via Bayesian Optimization (5 Code Examples)

- Example 23: Gaussian Process regression

- Example 24: Bayesian optimization loop (composition optimization)

- Example 25: Multi-objective optimization (capacity & cycle life)

- Example 26: Constrained optimization (safety constraints)

- Example 27: Pareto front visualization

Battery Simulation with PyBaMM (3 Code Examples)

- Example 28: DFN model (Doyle-Fuller-Newman)

- Example 29: Charge-discharge curve simulation

- Example 30: Parameter optimization and fitting

Project Challenge

- Goal: Discovery of high-capacity, long-life cathode materials (Target: capacity > 200 mAh/g, 2,000 cycles)

- Steps:

1. Retrieve cathode material data from Materials Project

2. Descriptor calculation and feature engineering

3. Train XGBoost model (capacity prediction)

4. Search optimal composition with Bayesian optimization

5. Simulate cycle performance with PyBaMM

Learning Objectives

✅ Build and simulate battery models with PyBaMM
✅ Implement and evaluate capacity and voltage prediction models
✅ Build cycle degradation prediction models (LSTM/GRU)
✅ Search for optimal materials with Bayesian optimization
✅ Execute actual battery development projects

Read Chapter 3 →

Chapter 4: Latest Battery Development Cases and Industrial Applications

Difficulty: Intermediate to Advanced

Reading Time: 25-30 minutes

Learning Content

5 Detailed Case Studies

Case Study 1: All-Solid-State Batteries - Solid Electrolyte Materials Discovery

- Challenge: High ionic conductivity (>10⁻³ S/cm) and chemical stability

- Approach: Graph Neural Network + Bayesian optimization

- Achievement: Discovery of new compositions in Li₇P₃S₁₁ system

- Companies: Toyota Motor Corporation, Murata Manufacturing

Case Study 2: Li-S Batteries - Sulfur Cathode Degradation Suppression

- Technology: Optimal design of carbon host materials

- ML methods: Transfer Learning + Molecular Dynamics

- Achievement: Capacity retention 70% → 90% (500 cycles)

- Impact: Energy density 500 Wh/kg achieved

Case Study 3: Fast Charging Optimization - 10-Minute Charging Protocol

- Current status: 30-60 minutes for 80% charge

- New technology: Charging curve optimization via reinforcement learning (RL)

- Achievement: 80% charge in 10 minutes, degradation rate < 1%/1000 cycles

Nature Energy

Case Study 4: Co-Reduced Cathode Materials - Ni Ratio Optimization

- Challenge: High Co cost ($40,000/ton) and supply risk

- Strategy: Increase Ni ratio (NCM811, NCM9½½)

- ML technology: Multi-fidelity Optimization

- Achievement: 90% reduction in Co usage, equivalent capacity

Case Study 5: Na-ion Batteries - Li-Free Materials Development

- Approach: Transfer learning from Li-analogous structures

- AI methods: Graph Convolutional Network

- Achievement: Capacity 150 mAh/g, 40% cost reduction

- Companies: CATL (China), Natron Energy (USA)

Battery AI Strategies of Major Companies

Battery Manufacturers:

Tesla: Charging optimization AI, lifetime prediction
Panasonic: Materials screening, manufacturing process optimization
CATL: Na-ion battery development
Samsung SDI: All-solid-state battery materials discovery

Automotive Manufacturers:

Toyota: All-solid-state battery commercialization (2027 target)
GM: Electrolyte development via Bayesian optimization
BMW: Cycle life prediction AI

Research Institutions:

NREL (USA): Battery Data Genome construction
AIST (Japan): All-solid-state battery materials database
MIT: Materials prediction via Graph Neural Networks
Best Practices for Battery AI

Keys to Success:

✅ Securing high-quality data (charge-discharge curves, DFT calculations)
✅ Leveraging domain knowledge (electrochemistry, solid-state physics)
✅ Iteration with experiments (Active Learning cycles)
✅ Integration of safety evaluation (thermal runaway risk)

Common Pitfalls:

❌ Inconsistent experimental conditions (temperature, current density)
❌ Overfitting (complex models with limited data)
❌ Ignoring scale-up challenges
❌ Insufficient consideration of supply chain risks
Carbon Neutrality and Batteries

- EV adoption: 30 million units annually by 2030 (30% of total)

- Renewable energy stabilization: Stationary energy storage systems (100 GWh scale)

- Life cycle assessment: CO2 reduction across manufacturing, usage, and recycling

- Circular economy: Battery recycling rate > 90%

Career Paths in Battery Research

Academia:

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

Industry:

Positions: Battery Scientist, Material Engineer
Salary: ¥6-18M/year (Japan), $80-250K (USA)
Companies: Panasonic, Toyota, CATL, Tesla, Samsung

Startups:

Examples: QuantumScape (all-solid-state batteries), SES (Li-Metal batteries)
Risk/Return: High risk, high impact
Required skills: Technology + Business + Fundraising

Learning Objectives

✅ Explain 5 successful battery AI application cases
✅ Compare and evaluate strategies of major companies
✅ Understand the role of batteries in carbon neutrality
✅ Plan career paths in battery research

Read Chapter 4 →

Overall Learning Outcomes

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

Knowledge Level

✅ Explain basic battery principles (capacity, voltage, cycle life)
✅ Understand the relationship between battery materials descriptors and performance
✅ Grasp industry trends in AI battery development
✅ Describe 5 or more latest case studies in detail

Practical Skills

✅ Build and simulate battery models with PyBaMM
✅ Implement capacity and voltage prediction models
✅ Build cycle degradation prediction models (LSTM)
✅ Search for optimal materials with Bayesian optimization

Application Capability

✅ Design new battery development projects
✅ Evaluate industry cases and apply to your own research
✅ Contemplate contributions to carbon neutrality realization

Recommended Learning Patterns

Pattern 1: Complete Mastery (For Battery Materials Beginners)

Target: Those learning about batteries for the first time

Duration: 2-3 weeks

Week 1:
Day 1-2: Chapter 1 (Battery Fundamentals)
Day 3-5: Chapter 2 (MI Methods)
Day 6-7: Chapter 2 exercises, terminology review

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

Week 3:
Day 1-2: Chapter 3 (Bayesian optimization, Examples 23-27)
Day 3-4: Chapter 3 (PyBaMM, Examples 28-30)
Day 5-6: Project Challenge
Day 7: Chapter 4 (Case Studies)

Pattern 2: Intensive Study (With Chemistry Background)

Target: Those with chemistry and materials science fundamentals

Duration: 1-2 weeks

Day 1-3: Chapter 2 (Battery descriptors and MI methods)
Day 4-7: Chapter 3 (Full code implementation)
Day 8: Project Challenge
Day 9-10: Chapter 4 (Industrial applications)

Pattern 3: Implementation Skills Enhancement (For ML Practitioners)

Target: Those with machine learning experience

Duration: 4-6 days

Day 1: Chapter 2 (Battery descriptors)
Day 2-4: Chapter 3 (Full code implementation)
Day 5: Project Challenge
Day 6: Chapter 4 (Industrial cases)

FAQ

Q1: Can I understand without electrochemistry knowledge?

A: Chapter 1 explains basic battery principles, but knowledge of high school chemistry (redox reactions) facilitates understanding. Chapter 3 code implementation is executable with programming skills, as the PyBaMM library handles electrochemical calculations.

Q2: PyBaMM installation is difficult.

A: We recommend PyBaMM installation via pip:

pip install pybamm

It is also available on Google Colab. Please refer to the official documentation (https://pybamm.org/) for details.

Q3: Is DFT calculation knowledge essential?

A: Chapters 1-2 and Examples 1-27 in Chapter 3 do not require DFT knowledge. We use pre-calculated data retrieved from Materials Project. If you wish to learn details of first-principles calculations, we recommend studying specialized DFT textbooks separately.

Q4: Isn't LSTM difficult?

A: We learn it step-by-step in Chapter 3:

Time series data fundamentals
Start with simple RNN
Understand LSTM/GRU structures
Implementation in Keras

Using TensorFlow/Keras, implementation is possible even without mathematical background.

Q5: Will I become a battery development expert with this series alone?

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

Build foundation with this series (2-4 weeks)
Intensive paper reading (Nature Energy, Advanced Energy Materials) (3-6 months)
Execute independent projects (6-12 months)
Conference presentations and paper writing (1-2 years)

Next Steps

Recommended Actions After Series Completion

Immediate (1-2 weeks):

✅ Create GitHub portfolio
✅ Publish Project Challenge results
✅ Add "Computational Battery Science" skill to LinkedIn

Short-term (1-3 months):

✅ Independent project with Materials Project data
✅ Intensive reading of 3 papers from Chapter 4 references
✅ Join PyBaMM community

Medium-term (3-6 months):

✅ Intensive reading of 10 papers (J. Electrochem. Soc., ACS Energy Lett.)
✅ Contribute to open source projects
✅ Present at domestic conferences (Electrochemical Society, Applied Physics Society)

Long-term (1+ years):

✅ Present at international conferences (ECS Meeting, MRS)
✅ Submit peer-reviewed papers
✅ Pursue battery-related work (battery manufacturers or EV companies)

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
✅ Use for educational purposes
✅ Modifications and derivative works

Conditions:

📌 Author attribution
📌 Indicate modifications when made
📌 Contact in advance for commercial use

Let's Begin!

Chapter 1: Battery Materials Fundamentals and the Role of Materials Informatics →

Update History

2025-10-19: v1.0 Initial release

The journey to realize a sustainable energy society through AI-driven battery development begins here!

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