Chapter 1: Why Materials Informatics Now?

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Chapter 1: Why Materials Informatics Now?

Learning Objectives

By reading this chapter, you will learn:
- Understand the historical evolution of materials development (from Bronze Age to modern era)
- Explain the limitations and challenges of conventional materials development
- Understand the social and technical background necessitating MI
- Learn from the specific example of lithium-ion battery development about the difficulties and possibilities of materials development

1.1 History of Materials Development: 5000 Years of Trial and Error

Human civilization has always evolved alongside materials. The types and performance of materials we use have characterized each era.

Ancient Times: The Era of Accidental Discovery

Bronze Age (circa 3000 BCE)

Bronze, humanity's first alloy (an alloy of copper and tin), was probably an accidental product. It was smelted in a state where copper ore and tin ore were mixed, and by chance, a metal much harder than pure copper was obtained. This discovery led humanity to transition from the Stone Age to the Metal Age.

Iron Age (circa 1200 BCE)

With the establishment of iron smelting technology, harder materials that exist more abundantly became available. However, establishing techniques to control iron properties (quenching, tempering) took several more hundred years.

Modern Era: Development Based on Empirical Rules (1800-1950s)

Age of Steel (1800s)

During the Industrial Revolution period, Henry Bessemer invented the Bessemer converter process (1856), enabling mass production of steel. However, this invention was also basically a product of trial and error.

Discovery of Stainless Steel (1913)

British metallurgist Harry Brearley was researching iron-chromium alloys aiming to improve the corrosion resistance of gun barrels. During experiments, he accidentally discovered steel that doesn't rust (stainless steel). This discovery was also basically through accident and empirical rules.

Contemporary Era: Beginning of Theory-Based Design (1950s-Present)

Silicon Semiconductors (1950s)

With the establishment of transistor technology (invented in 1947) and silicon semiconductor technology, the foundation of the information society was built. Around this time, materials design based on theoretical foundations such as quantum mechanics began.

Polymer Materials and Composite Materials (1990s onwards)

Composite materials such as carbon fiber reinforced plastic (CFRP) came to be adopted in aircraft and automobiles. These materials are designed utilizing theoretical calculations and simulations. However, experimental validation is still essential in the end.

Challenges Visible from History

Looking back at 5000 years of materials development history, the following challenges become visible:

  1. Dependence on accident: Many important discoveries were products of chance
  2. Time-consuming process: Several years to several decades for one material development
  3. Limited exploration range: Only exploring the range researchers can think of
  4. Dispersed knowledge: Knowledge dispersed among individuals and organizations, making systematic accumulation difficult

Question: What if we could systematically explore materials by computer?

This is the starting point of Materials Informatics (MI), the next generation of materials development methods.

1.2 Limitations of Conventional Materials Development

Modern materials development has become far more advanced than ancient times. However, it still faces major challenges.

Challenge 1: Time-consuming

Typical Materials Development Timeline

Years 1-2: Literature survey and theoretical examination
  ↓
Years 3-5: Synthesis and evaluation of candidate materials (10-50 types)
  ↓
Years 6-10: Optimization and characterization
  ↓
Years 11-15: Establishment of mass synthesis process for practical application
  ↓
Years 16-20: Practical application and commercialization

Result: It takes an average of 15-20 years from new material to practical application[1,2].

Challenge 2: High cost

Cost to evaluate one material

Total: 1-7 million yen per material

A laboratory with an annual budget of 30 million yen can only evaluate 10-30 materials per year.

Challenge 3: Limited exploration range

Material combinations are astronomical numbers

Furthermore, considering variations in composition ratios and crystal structures, there are essentially infinite combinations.

With conventional methods, based on researcher experience and intuition, they select and experiment with tens to hundreds of types. In other words, only a small fraction of the vast possibilities can be explored.

Challenge 4: Dependence on experience and intuition

Tacit knowledge of experienced researchers

Such tacit knowledge is very valuable, but has the following problems:

  1. Difficult to systematize: Hard to share because based on individual experience
  2. Reproducibility issues: Results may differ among researchers even under same conditions
  3. Time for training young researchers: 10+ years of experience needed to become proficient
  4. Existence of bias: May miss innovative discoveries by being bound to existing knowledge

1.3 Case Study: 20 Years of Lithium-Ion Battery Development

The development story of lithium-ion batteries is a typical example showing the difficulty of conventional materials development and the importance of persistent research. Let's look closely at this technology, which was the subject of the 2019 Nobel Prize in Chemistry.

Phase 1: Basic Research (1970s)

Background: Energy Crisis

The oil shock of the 1970s heightened interest in energy storage technology not dependent on petroleum.

Dr. John Goodenough's Challenge

Dr. Goodenough at Oxford University believed that oxide materials containing lithium were promising for energy storage.

Material candidates explored: About 100+ types

Discovery (1980): LiCoO₂ (Lithium cobalt oxide)

However, there were still many challenges to put this material into practical use.

Phase 2: Development of Negative Electrode Material (1980s)

Challenge: Danger of metallic lithium

Initially, metallic lithium was used for the negative electrode, but there were the following problems:

  1. Dendrite formation: With repeated charging and discharging, needle-like lithium crystals (dendrites) grow
  2. Short circuit risk: When dendrites reach the positive electrode, they short circuit and risk ignition
  3. Cycle life: Degradation after tens of charge-discharge cycles

Dr. Akira Yoshino's Solution (1985)

Dr. Yoshino at Asahi Kasei conceived the idea of using carbon material (graphite) as a lithium-ion storage material.

Material candidates explored: About 50+ types

Results:
- Graphite safely stores Li ions between layers
- No dendrite formation
- Cycle life improved to hundreds of times

Phase 3: Electrolyte Optimization (Late 1980s)

Challenge: Electrolyte stability

An electrolyte that operates stably between the positive electrode (4V) and negative electrode (0V vs Li/Li⁺) was needed.

Material candidates explored: About 100+ types

Discovery of optimal solution:
- Ethylene carbonate (EC) + Diethyl carbonate (DEC)
- Lithium salt: LiPF₆
- This combination achieved stable charging and discharging

Phase 4: Practical Application (1991)

Commercialization by Sony

In 1991, Sony commercialized the world's first lithium-ion battery as a battery for video cameras.

Specifications (1991 first model):
- Energy density: About 200 Wh/kg (about twice that of nickel-metal hydride batteries)
- Cycle life: 500+ times
- Operating voltage: 3.7V

Time and Cost for Development

Time: About 20 years from basic research start (1970s) to commercialization (1991)

Researchers: Led by Dr. Goodenough, Dr. Whittingham, and Dr. Yoshino, several hundred researchers worldwide were involved

Total number of materials explored: Estimated 500+ types

Failed experiments: Thousands or more

Question: What if MI existed?

If modern MI technology existed in the 1970s:

  1. Narrowing down material candidates: Machine learning predicts 100 promising types in a few days
  2. Electrolyte optimization: Bayesian optimization discovers optimal composition in about 20 experiments
  3. Development period: Estimated 5-7 years (less than 1/3) reduction possible

This is not science fiction but what is actually possible with modern MI technology.

1.4 Conventional Method vs MI: Workflow Comparison

As seen in the example of lithium-ion batteries, conventional materials development is time-consuming and costly. Here, let's visually compare the workflows of conventional methods and MI methods.

Workflow Comparison Diagram

graph TD
    subgraph "Conventional Method (Trial and Error Type)"
        A1[Literature Survey] -->|1-2 months| A2[Select Candidate Material 1]
        A2 -->|2 weeks| A3[Synthesize Material 1]
        A3 -->|2 weeks| A4[Measure Properties]
        A4 -->|1 week| A5{Goal Achieved?}
        A5 -->|No 95%| A2
        A5 -->|Yes 5%| A6[Consider Practical Application]

        style A1 fill:#ffcccc
        style A2 fill:#ffcccc
        style A3 fill:#ffcccc
        style A4 fill:#ffcccc
        style A5 fill:#ffcccc
        style A6 fill:#ccffcc
    end

    subgraph "MI Method (Data-Driven Type)"
        B1[Data Collection] -->|1 week| B2[Build Machine Learning Model]
        B2 -->|1 day| B3[Predict 10,000 types]
        B3 -->|1 day| B4[Narrow down to top 100 types]
        B4 -->|1 day| B5[Select top 10 types]
        B5 -->|2 weeks| B6[Experimental validation of 10 types]
        B6 -->|1 week| B7{Goal Achieved?}
        B7 -->|No 50%| B8[Add Data]
        B8 -->|Continuous Learning| B2
        B7 -->|Yes 50%| B9[Consider Practical Application]

        style B1 fill:#ccddff
        style B2 fill:#ccddff
        style B3 fill:#ccddff
        style B4 fill:#ccddff
        style B5 fill:#ccddff
        style B6 fill:#ffffcc
        style B7 fill:#ccddff
        style B8 fill:#ccddff
        style B9 fill:#ccffcc
    end

    A1 -.comparison.- B1

Quantitative Comparison

Metric Conventional Method MI Method Improvement Rate
Annual materials explored 10-30 types 100-200 types (experimental)
10,000+ types (computational)		 10-1000x
Time per material 4-8 weeks 1-2 weeks (experiment only)
seconds (prediction)		 75-99% reduction
Cost per material 1-7 million yen 100,000-1 million yen (experiment)
almost free (computation)		 90-99% reduction
Success rate 5-10% (empirical rules) 30-50% (prediction accuracy) 3-5x improvement
Development period (to practical use) 15-20 years 3-7 years (target) 60-80% reduction

Timeline Comparison Example

When evaluating 100 materials with conventional method:
- 1 material × 4 weeks = 100 materials × 4 weeks = 400 weeks = about 8 years

When evaluating 100 materials with MI method:
- Data collection & model building: 2 weeks
- Predict 10,000 types: 1 day
- Experiment on top 100 types: 100 materials × 2 weeks = 200 weeks = about 4 years
- However, with parallel experiments and robot automation: 6 months-1 year

Time reduction: 8 years → 6 months~1 year = 87-93% reduction

1.5 Column: A Day in the Life of a Materials Scientist

Let's see how the materials development field has changed through a specific story.

1985: Era of Conventional Methods

A Day of Professor Tanaka (45 years old)

9:00 - Arrive at laboratory
The sample prepared yesterday has completed synthesis. Remove from furnace and start cooling.

10:00 - Characterize sample
Go to experimental room for X-ray diffraction measurement. Measurement takes 3 hours. Read papers in the meantime.

14:00 - Data analysis
Manually analyze X-ray diffraction patterns. Takes 2 hours to identify crystal structure.

16:00 - Plan next experiment
Looking at today's results, think about the composition of the next sample. Based on experience, decide to try a material with slightly changed composition.

17:00 - Prepare sample
Prepare new sample for tomorrow. Weigh reagents, mix, set in furnace.

18:00 - Record in laboratory notebook
Record today's results in detail in handwritten laboratory notebook.

19:00 - Leave work

Daily achievement: Evaluated 1 type of material, prepared next 1 type

Monthly achievement (20 days): About 20 types of materials evaluated

Annual achievement: About 200 types of materials evaluated (actually about 150 types due to equipment troubles and holidays)

2025: MI Era

A Day of Associate Professor Sato (38 years old)

9:00 - Arrive at laboratory
First, check the results of 10 types of samples that the automated experimental equipment executed overnight. Data is automatically saved in cloud database.

9:30 - AI data analysis
Machine learning model automatically identifies crystal structures and predicts properties. Completes analysis of 10 types of data in 10 minutes.

10:00 - Predict next experiment candidates
Bayesian optimization algorithm proposes 20 promising types to try next from a database of 100,000 types of materials. Prediction takes 5 minutes.

10:30 - Examine top candidates
Check the proposed 20 types with human eyes. Utilizing materials science knowledge, select 10 particularly promising types.

11:00 - Set experimental conditions
Input synthesis conditions for the selected 10 types into automated experimental equipment.

11:30 - Research meeting
Discuss this week's progress with students. Review validity of AI prediction results and consider next research directions.

13:00 - Write papers
With overnight experiments available, more time can be devoted to paper writing.

15:00 - Maintain automated experimental equipment
Check equipment operation and replace consumables.

16:00 - Train new model
Add new data obtained this week and retrain machine learning model. Prediction accuracy improves further.

17:00 - Leave work

Daily achievement: Evaluated 10 types of materials, set next 10 types in automated experiments

Monthly achievement (20 days): About 200 types of materials evaluated

Annual achievement: About 2,000 types of materials evaluated (operating weekends with automation)

Points of Change

Item 1985 2025 Change
Daily evaluation count 1 type 10 types 10x
Annual evaluation count 150 types 2,000 types 13x
Data analysis time 2-3 hours/sample 1 minute/sample (automated) 99% reduction
Laboratory notebook Handwritten Digitized (auto-saved) Efficiency
Candidate material selection Experience and intuition AI proposal + human judgment Combination
Paper writing time Little Much (time secured by experiment automation) Improved research quality

Important point: MI is not replacing researchers but supporting them. Both Professor Tanaka's experience and Associate Professor Sato's judgment are indispensable, but Associate Professor Sato can explore more materials, more efficiently with AI support.

1.6 Why "Now" for MI: Three Tailwinds

The concept of MI itself has existed since the 1990s, but it was not until the 2010s and beyond that it was fully put into practical use. Why "now"? There are three major factors.

Tailwind 1: Dramatic improvement in computational performance

Benefits of Moore's Law

Spread of cloud computing

Utilization of GPUs (Graphics Processing Units)

Tailwind 2: Enhancement of materials databases

Materials Project (started in 2011)

Other major databases

Database Start Year Material Count Features
AFLOW 2010 3.5+ million types Crystal structure database
OQMD 2013 1+ million types Thermodynamic data
NOMAD 2014 10+ million entries Computational data repository
Citrine 2013 Undisclosed Experimental data (for enterprises)

Trend of open science

Tailwind 3: Heightened social urgency

US Materials Genome Initiative (2011)

A national project started by the Obama administration. Accelerated public-private research investment with the goal of reducing materials development period by half.

Goals:
- Materials development period: 20 years → 10 years or less
- Integration of computation, experimentation, and data
- Annual budget: About $100 million (about 10 billion yen)

Response to climate change

Spread of electric vehicles (EVs)

Global competition

Conclusion: MI is a technology that is needed now precisely when technological maturity and social necessity are simultaneously satisfied.

1.7 Summary of This Chapter

What We Learned

  1. History of materials development
    - From Bronze Age to modern times, materials have supported civilization development
    - Evolved from ancient accidents, to modern trial and error, to contemporary theory-based design
    - However, development still takes 10-20 years

  2. Limitations of conventional methods
    - Time: 4-8 weeks per material, 15-20 years to practical application
    - Cost: 1-7 million yen per material
    - Exploration range: Only 10-100 types per year (just a fraction of possibilities)
    - Experience-dependent: Dependent on experienced researchers' tacit knowledge

  3. Lessons from lithium-ion batteries
    - 20 years from basic research (1970s) to commercialization (1991)
    - Trial and error with 500+ types of materials
    - Thousands of failed experiments
    - With MI, development period could be reduced to 1/3

  4. Advantages of MI
    - Annual exploration: 10-30 types → 100-2000 types (10-100x)
    - Development period: 15-20 years → 3-7 years (60-80% reduction)
    - Cost reduction: 90-99% reduction (utilizing computational prediction)

  5. Why MI is needed "now"
    - Improved computational performance (Moore's Law, GPUs, cloud)
    - Enhancement of materials databases (Materials Project etc. 140,000+ types)
    - Social urgency (climate change, EV adoption, international competition)

Important Points

To the Next Chapter

In Chapter 2, we will learn in detail about the basic workflow of MI:
- Data collection methods
- Building machine learning models
- Prediction and screening
- Experimental validation and data cycle

Additionally, we will practice simple materials prediction using Python.

Exercises

Problem 1 (Difficulty: easy)

In the history of materials development, explain how development methods evolved across three eras: Bronze Age, Iron Age, and modern era.

Hint Think about the flow from accident → trial and error → theory-based design.
Sample Answer **Bronze Age (circa 3000 BCE)**: - Development method: Complete accident - Alloy accidentally formed when copper ore and tin ore were mixed - Several hundred years to discover optimal composition **Iron Age (circa 1200 BCE)**: - Development method: Trial and error and empirical rules - Experimentally discovered heat treatments like quenching and tempering - Knowledge accumulated as empirical rules **Modern Era (1950s onwards)**: - Development method: Theory-based design + experimental validation - Utilizing theories like quantum mechanics and thermodynamics - Combination of simulation and experimentation - However, still takes 10-20 years for development

Problem 2 (Difficulty: easy)

Calculate how long it takes to evaluate 100 materials per year with conventional materials development methods. Assume it takes 4 weeks per material.

Hint 1 material × 4 weeks = 100 materials × ? weeks
Sample Answer **Calculation**: - 1 material × 4 weeks = 100 materials × 4 weeks = 400 weeks - 1 year = 52 weeks - 400 weeks ÷ 52 weeks/year = **about 7.7 years** **Conclusion**: It takes about 8 years to evaluate 100 materials with conventional methods. This is one reason why conventional methods can only explore a limited number of materials.

Problem 3 (Difficulty: medium)

In the development of lithium-ion batteries, if MI technology had existed in the 1970s, how would the development process have changed? Explain specifically.

Hint Think about the exploration processes for the three components: positive electrode material, negative electrode material, and electrolyte.
Sample Answer **Exploration of positive electrode material (discovery of LiCoO₂)**: Conventional method (actual history): - Trial and error with 100+ candidates over 10 years - Discovered LiCoO₂ in 1980 MI method (hypothetical scenario): - Analyze existing oxide data (thousands of types) with machine learning - Predict electrochemical stability and ion conductivity - Experimentally validate top 10 promising types in 2-3 years - Early discovery of multiple candidates including LiCoO₂ **Exploration of negative electrode material (graphite)**: Conventional method: - Trial and error with 50+ types of carbon materials - Graphite found promising in 1985 MI method: - Predict Li insertion energy with first-principles calculations - Screen layered structure materials - Identify promising candidates including graphite within 1 year **Electrolyte optimization**: Conventional method: - Trial and error with 100+ combinations of solvents and salts - Several years to discover optimal composition (EC/DEC + LiPF₆) MI method: - Efficiently narrow down search space with Bayesian optimization - Identify optimal composition in 20-30 experiments - Develop practical electrolyte within 1 year **Overall development period**: - Conventional method: About 20 years (1970s-1991) - MI method: Estimated 5-7 years (60-70% reduction) **Additional benefits**: - Could have discovered other promising positive electrode materials (LiMn₂O₄, LiFePO₄, etc.) early - Further improvement in battery performance - Selection of safer materials

References

  1. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). "Machine learning in materials informatics: recent applications and prospects." npj Computational Materials, 3(1), 54.
    DOI: 10.1038/s41524-017-0056-5

  2. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). "Machine learning for molecular and materials science." Nature, 559(7715), 547-555.
    DOI: 10.1038/s41586-018-0337-2

  3. Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., et al. (2013). "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation." APL Materials, 1(1), 011002.
    DOI: 10.1063/1.4812323
    Materials Project: https://materialsproject.org

  4. Goodenough, J. B., & Park, K. S. (2013). "The Li-ion rechargeable battery: a perspective." Journal of the American Chemical Society, 135(4), 1167-1176.
    DOI: 10.1021/ja3091438

  5. National Science and Technology Council (2011). "Materials Genome Initiative for Global Competitiveness." Executive Office of the President, USA.
    URL: https://www.mgi.gov/

Author Information

Created by: MI Knowledge Hub Content Team
Supervised by: Dr. Yusuke Hashimoto (Tohoku University)
Created: 2025-10-16
Version: 3.0 (Chapter 1 standalone version)
Template: content_agent_prompts.py v1.0

Update History:
- 2025-10-16: v3.0 Chapter 1 standalone version created
- Expanded to 3,000-4,000 words based on v2.1 Section 1 (58 lines)
- Added materials development history section (Bronze Age to modern era)
- Detailed Li-ion battery case study (20 years of development process)
- Added workflow comparison Mermaid diagram
- Added "A Day in the Life of a Materials Scientist" column (1985 vs 2025)
- Added "Why MI Now" section (three tailwinds)
- Added 3 exercise problems

License: Creative Commons BY-NC-SA 4.0

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