Chapter 2: MI Fundamentals - Concepts, Methods & Ecosystem

Learning Objectives

By reading this chapter, you will be able to:
- Explain the definition of MI and its differences from related fields (computational materials science, cheminformatics, etc.)
- Understand the characteristics and use cases of major materials databases (Materials Project, AFLOW, OQMD, JARVIS)
- Explain the 5-step MI workflow in detail (from problem formulation to validation)
- Understand the types and importance of materials descriptors (composition-based, structure-based, property-based)
- Correctly use 20 specialized terms frequently used in the MI field

2.1 What is MI: Definition and Related Fields

2.1.1 Etymology and History of Materials Informatics

The term Materials Informatics (MI) began to be used in the early 2000s. It gained worldwide attention particularly with the launch of the U.S. Materials Genome Initiative (MGI) in 2011 [1].

MGI Goals:
- Reduce new materials development time to half of traditional approaches
- Significantly reduce development costs
- Accelerate through integration of computation, experiment, and data

This initiative was expected to have the potential to fundamentally transform materials science, just as the Human Genome Project transformed biology.

2.1.2 Definition

Materials Informatics (MI) is an academic field that combines materials science with data science. It is a methodology that accelerates the discovery of new materials and prediction of material properties by leveraging large amounts of materials data and information science technologies such as machine learning.

Concise Definition:

"Science that speeds up materials development through the power of data and AI"

Core Elements:
1. Data: Experimental data, computational data, knowledge from literature
2. Computation: First-principles calculations, molecular dynamics simulations
3. Machine Learning: Predictive models, optimization algorithms
4. Experimental Validation: Verification of predictions and data addition

2.1.3 Comparison with Related Fields

MI is related to multiple fields, but each has a different focus.

Uniqueness of MI:
- Inverse Design Approach: Design materials from target properties (traditional approach calculates properties from materials)
- Diverse Material Types: Covers a wide range including metals, ceramics, semiconductors, polymers
- Strong Experimental Collaboration: Emphasizes experimental validation, not just computation

2.1.4 Forward Design vs Inverse Design

Traditional Materials Development (Forward Design):

Material composition → Calculate/measure structure/properties → Evaluate results

• Researchers propose candidate materials
• Repeated trial and error
• Time-consuming

MI Approach (Inverse Design):

Target properties → Predict candidate materials with ML → Experiment top candidates

• AI proposes optimal materials
• Efficiently screens large numbers of candidates
• Significant time reduction

Concrete Example of Inverse Design:
"I want a semiconductor material with a bandgap of 2.0 eV"
→ MI system automatically generates candidate material list
→ Researchers experimentally validate only the top 10

2.2 MI Glossary: Essential 20 Terms

Here is a compilation of specialized terms frequently used in learning MI. For beginners, correctly understanding these terms is the first step.

Data & Model Related (1-7)

Computational Methods Related (8-13)

Materials Science Related (14-20)

Key Points for Learning Terminology:
- First prioritize understanding 1-7 (Data & Model related)
- Learn 8-13 (Computational methods) in detail at intermediate level
- Review 14-20 (Materials science) together with basic materials science knowledge

2.3 Overview of Materials Databases

Let's compare the four major materials databases that form the foundation of MI in detail.

2.3.1 Detailed Comparison of Major Databases

2.3.2 How to Use Different Databases

1. Materials Project
- When to use: Exploration of battery materials, semiconductors, general inorganic materials
- Strengths:
- Intuitive Web UI, beginner-friendly
- Rich Python library (pymatgen)
- Active community with abundant information
- Weaknesses: Lower coverage for some structure types

2. AFLOW
- When to use: Exploration of novel crystal structures, structural similarity search
- Strengths:
- Most crystal structures (3.5 million types)
- Standardized crystal structure description (AFLOW prototype)
- Fast structural similarity search
- Weaknesses: Fewer types of property data than Materials Project

3. OQMD
- When to use: Phase diagram calculations for alloys, detailed phase stability evaluation
- Strengths:
- Specialized in phase diagram calculations
- Rich multi-component alloy data
- Temperature-dependent evaluation possible
- Weaknesses: Less user-friendly Web UI

4. JARVIS
- When to use: Optical materials, topological materials, using ML models
- Strengths:
- Integrated machine learning models
- Rich optical properties (permittivity, refractive index)
- Topological property calculations
- Weaknesses: Fewer materials than others

2.3.3 Practical Examples of Database Utilization

Scenario 1: Finding novel cathode materials for lithium-ion batteries

1. Search in Materials Project:
   - Conditions: Contains Li, voltage 3.5-4.5V, stable
   - Candidates: 100 types found

2. Select top 10 types:
   - Evaluate based on balance of capacity, voltage, stability

3. Confirm phase stability in OQMD:
   - Check possibility of decomposition with temperature changes

4. Experimental validation:
   - Actually synthesize top 3 types

Scenario 2: Finding transparent conductive materials (for solar cells)

1. Search in JARVIS:
   - Conditions: Bandgap > 3.0 eV (transparent), high electrical conductivity
   - Candidates: 50 types

2. Additional information from Materials Project:
   - Confirm formation energy, thermal stability

3. Search for similar structures in AFLOW:
   - Find structures similar to promising materials found

4. Experimental validation

2.3.4 Example of Database Access

Materials Project API (Python) usage example:

from pymatgen.ext.matproj import MPRester

# Get API key: https://materialsproject.org
with MPRester("YOUR_API_KEY") as mpr:
    # Get LiCoO2 information
    data = mpr.get_data("mp-1234")  # material_id

    print(f"Chemical formula: {data[0]['pretty_formula']}")
    print(f"Bandgap: {data[0]['band_gap']} eV")
    print(f"Formation energy: {data[0]['formation_energy_per_atom']} eV/atom")

Important Points:
- Each database has a complementary relationship
- Using multiple databases together is recommended, not just one
- Important to compare results across different databases to ensure data reliability

2.4 MI Ecosystem: Data Flow

MI is not a standalone technology but an ecosystem where multiple elements collaborate. The following diagram shows the data flow in MI.

graph TB
    subgraph "Data Generation"
        A[Experimental Data] --> D[Materials Database]
        B[First-principles Calculation<br>DFT] --> D
        C[Papers/Patents] --> D
    end

    subgraph "Data Processing"
        D --> E[Data Cleaning<br>Standardization]
        E --> F[Descriptor Generation<br>Feature Engineering]
    end

    subgraph "Machine Learning"
        F --> G[Model Training<br>Regression/Classification]
        G --> H[Prediction/Screening<br>Thousands to tens of thousands]
    end

    subgraph "Experimental Validation"
        H --> I[Candidate Selection<br>Top 10-100]
        I --> J[Experimental Synthesis/Measurement]
        J --> K{Prediction<br>Accurate?}
    end

    subgraph "Continuous Improvement"
        K -->|Yes| L[Add as New Data]
        K -->|No| M[Model Improvement/Retraining]
        L --> D
        M --> G
    end

    style D fill:#e3f2fd
    style F fill:#fff3e0
    style G fill:#f3e5f5
    style J fill:#e8f5e9
    style L fill:#fce4ec

How to Read the Diagram:
1. Data Generation: Collect data from experiments, calculations, literature
2. Data Processing: Convert raw data into formats suitable for machine learning
3. Machine Learning: Train models and predict large numbers of candidates
4. Experimental Validation: Experimentally verify promising candidates
5. Continuous Improvement: Add results to data and improve models

Importance of Feedback Loop:
- Accurate prediction → Add data to further improve model
- Inaccurate prediction → Review model, change descriptors or learning methods
- Model accuracy improves by repeating this cycle

2.5 Basic MI Workflow: Detailed Version

Chapter 1 introduced a 4-step workflow, but here we expand to a more practical 5-step approach.

2.5.1 Overview

graph LR
    A[Step 0:<br>Problem Formulation] --> B[Step 1:<br>Data Collection]
    B --> C[Step 2:<br>Model Building]
    C --> D[Step 3:<br>Prediction/Screening]
    D --> E[Step 4:<br>Experimental Validation]
    E --> F[Step 5:<br>Data Addition/Improvement]
    F -.Continuous Improvement.-> B

    style A fill:#ffebee
    style B fill:#e3f2fd
    style C fill:#fff3e0
    style D fill:#f3e5f5
    style E fill:#e8f5e9
    style F fill:#fce4ec

2.5.2 Step 0: Problem Formulation (Most Important, Often Overlooked)

What to do:
- Clearly define the problem to solve
- Specify target properties and constraints
- Set success criteria

Concrete Example: Battery Material Development

Poor problem formulation:

"I want to find good battery materials"

Good problem formulation:

"Discover materials with the following properties as cathode materials for lithium-ion batteries:
- Theoretical capacity: ≥200 mAh/g
- Operating voltage: 3.5-4.5 V vs. Li/Li+
- Cycle life: Capacity retention ≥80% after 500 cycles
- Cost: ≤$50/kg (raw materials basis)
- Safety: Thermal runaway temperature ≥200°C
- Environmental constraint: Minimize Co usage (ideally Co-free)"

Problem Formulation Checklist:
- [ ] Are target properties quantitatively defined?
- [ ] Are constraints (cost, environment, safety) clear?
- [ ] Are success criteria measurable?
- [ ] Is it experimentally verifiable?

Time estimate: 1-2 weeks (including literature review and expert discussions)

Common Failures:
- Vague goals that change repeatedly later
- Ignore constraints and end up searching for infeasible materials
- No success criteria, so exploration never ends

2.5.3 Step 1: Data Collection

What to do:
- Collect material information from existing experimental data and literature
- Download relevant data from materials databases
- Add data through first-principles calculations if necessary

Priority of Data Sources:
1. Existing Databases (Most efficient)
- Materials Project, AFLOW, OQMD
- High reliability, immediately usable

2. Papers/Patents (Manual work required)
   - Google Scholar, Web of Science
   - May contain experimental data

3. Self-calculate/measure (Time-consuming)
   - Generate new material data through DFT calculations
   - Laboratory measurements

Concrete Example: Lithium-ion Battery Cathode Materials

from pymatgen.ext.matproj import MPRester
import pandas as pd

# Search for Li-containing oxides from Materials Project
with MPRester("YOUR_API_KEY") as mpr:
    # Search criteria
    criteria = {
        "elements": {"$all": ["Li", "O"]},  # Must contain Li and O
        "nelements": {"$gte": 2, "$lte": 4},  # 2-4 elements
        "e_above_hull": {"$lte": 0.05}  # Stable or metastable
    }

    # Properties to retrieve
    properties = [
        "material_id",
        "pretty_formula",
        "formation_energy_per_atom",
        "energy_above_hull",
        "band_gap",
        "density"
    ]

    # Get data
    results = mpr.query(criteria, properties)

    # Convert to DataFrame
    df = pd.DataFrame(results)

    print(f"Number of materials retrieved: {len(df)}")
    print(df.head())

Expected Results:
- Data for hundreds to thousands of candidate materials
- Basic properties of each material (composition, formation energy, bandgap, etc.)

Time Estimate:
- Database use: Several hours to days
- Literature review: 1-2 weeks
- DFT calculations: Several weeks to months (depends on number of materials)

Common Problems:
- Missing data (specific properties available only for some materials)
- Data inconsistency (values differ between different databases)
- Data bias (biased toward specific material systems)

Solutions:
- Compare multiple databases to verify reliability
- Consider imputation methods for missing values (mean, ML estimation, etc.)
- Recognize data bias and clarify model applicability range

2.5.4 Step 2: Model Building

What to do:
- Train machine learning models using collected data
- Select appropriate descriptors (features)
- Evaluate and optimize model performance

Sub-steps:

2.1 Descriptor Design

Need to convert materials into numerical vectors.

Types of Descriptors:

Descriptor Example: Numerical Representation of LiCoO2

# Simple example: Composition-based descriptors
material = "LiCoO2"

# Fraction of each element
Li_fraction = 0.25  # 1/(1+1+2)
Co_fraction = 0.25
O_fraction = 0.50

# Element properties (from periodic table)
electronegativity_Li = 0.98
electronegativity_Co = 1.88
electronegativity_O = 3.44

# Weighted average
avg_electronegativity = (
    Li_fraction * electronegativity_Li +
    Co_fraction * electronegativity_Co +
    O_fraction * electronegativity_O
)  # = 2.38

# Vector representation
descriptor_vector = [
    Li_fraction, Co_fraction, O_fraction,  # Composition
    avg_electronegativity,  # Electronegativity
    # ... Add other properties
]

In actual projects, use the matminer library:

from matminer.featurizers.composition import ElementProperty

# Automatically generate numerous descriptors
featurizer = ElementProperty.from_preset("magpie")
features = featurizer.featurize_dataframe(df, col_id="composition")

2.2 Model Selection

Beginner-friendly Models:
- Linear Regression: Simple, interpretable
- Decision Tree: Visualizable, captures non-linear relationships

Intermediate Models:
- Random Forest: High accuracy, robust to overfitting
- Gradient Boosting (XGBoost, LightGBM): Highest accuracy

Advanced Models:
- Neural Networks: Learn complex non-linear relationships
- Graph Neural Networks (GNN): Directly learn crystal structures

2.3 Training and Evaluation

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, r2_score

# Data split
X = features  # Descriptors
y = df['target_property']  # Example: voltage

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Model training
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Prediction
y_pred = model.predict(X_test)

# Evaluation
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"MAE: {mae:.3f}")
print(f"R²: {r2:.3f}")

# Cross-validation (more reliable evaluation)
cv_scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_absolute_error')
print(f"CV MAE: {-cv_scores.mean():.3f} ± {cv_scores.std():.3f}")

Performance Guidelines:
- R² > 0.8: Good
- R² > 0.9: Excellent
- R² < 0.5: Model needs revision

Time Estimate:
- Descriptor design: Several days to 1 week
- Model training and optimization: 1-2 weeks

Common Problems:
- Overfitting (high accuracy on training data but low on test data)
- Poor descriptor selection (missing important features)

Solutions:
- Verify generalization performance with cross-validation
- Analyze feature importance and remove unnecessary descriptors
- Introduce regularization (L1/L2)

2.5.5 Step 3: Prediction/Screening

What to do:
- Predict properties of unknown materials using trained model
- Evaluate large numbers of candidate materials (thousands to tens of thousands) in a short time
- Select promising top candidates

Screening Flow:

Candidate materials: 10,000 types (generated by calculations)
  ↓ (Predict with ML: minutes)
Rank by predicted values
  ↓
Select top 1,000 (close to target properties)
  ↓ (Detailed calculation/evaluation: hours to days)
Narrow down to top 100
  ↓
Materials to experiment: Top 10 (most promising candidates)

Concrete Code Example:

import numpy as np

# Generate candidate material list (example: create candidates by varying composition)
# Actually use more systematic methods
candidate_compositions = [...]  # 10,000 items

# Calculate descriptors for each candidate
candidate_features = compute_descriptors(candidate_compositions)

# Predict with model
predicted_properties = model.predict(candidate_features)

# Rank (example: by highest voltage)
ranked_indices = np.argsort(predicted_properties)[::-1]

# Select top 100
top_100 = [candidate_compositions[i] for i in ranked_indices[:100]]

print("Top 10 candidates:")
for i, comp in enumerate(top_100[:10]):
    pred_val = predicted_properties[ranked_indices[i]]
    print(f"{i+1}. {comp}: Predicted value = {pred_val:.2f}")

Efficiency Example:
- Traditional: Evaluate 10,000 types experimentally → About 30 years (1 per day)
- MI: Evaluate 10 types experimentally → About 2 weeks
- Time reduction rate: 99.9%

Time Estimate:
- Prediction calculation: Minutes to hours (depends on number of candidates)
- Result analysis: Several days

Cautions:
- Predictions are just predictions. Must validate experimentally
- Prediction accuracy is low for materials outside model applicability range (materials very different from training data)
- More reliable with uncertainty evaluation (Bayesian methods)

2.5.6 Step 4: Experimental Validation

What to do:
- Actually synthesize materials narrowed down by prediction
- Measure properties and verify if predictions were correct
- Analyze discrepancies between predictions and measurements

Experimental Priority:
1. Materials with highest predicted values (Best case)
2. Materials with moderate predicted values but low uncertainty (Safe choice)
3. Materials with high predicted values but also high uncertainty (High risk, high return)

Validation Checklist:
- [ ] Are synthesis conditions established?
- [ ] Are measurement instruments available?
- [ ] Does measurement accuracy meet target property requirements?
- [ ] Reproducibility confirmation (multiple measurements)

Time Estimate:
- Synthesis: Several days to weeks (depends on material)
- Measurement: Several days to 1 week
- Total: 2-3 months for top 10

Judging Success and Failure:

Important Points:
- Failures are also valuable data. Always add to database
- Analyzing causes of prediction-measurement discrepancies improves models

2.5.7 Step 5: Data Addition/Model Improvement

What to do:
- Add experimental results (both success and failure) to database
- Retrain model with new data
- Verify improved prediction accuracy

Continuous Improvement Cycle:

Initial model (R² = 0.75)
  ↓
Add 10 experimental results
  ↓
Model retraining (R² = 0.82)
  ↓
10 more experiments
  ↓
Model retraining (R² = 0.88)
  ↓
Finally discover optimal material

Active Learning Application:

In typical MI, materials with high predicted values are experimented on, but in active learning, the model suggests "materials with high uncertainty".

# Estimate uncertainty with Random Forest
predictions = []
for tree in model.estimators_:
    pred = tree.predict(candidate_features)
    predictions.append(pred)

predictions = np.array(predictions)
uncertainty = predictions.std(axis=0)  # High std = high uncertainty

# Prioritize materials with high uncertainty for experiment
high_uncertainty_indices = np.argsort(uncertainty)[::-1]
next_experiment = candidate_compositions[high_uncertainty_indices[0]]

Time Estimate: 1-2 weeks per cycle

Termination Conditions:
- Found materials satisfying target properties
- Prediction accuracy sufficiently high (R² > 0.9)
- Budget/time constraints

2.6 Details of Materials Descriptors

2.6.1 Types of Descriptors and Concrete Examples

1. Composition-based Descriptors

Characteristics:
- Calculable from chemical formula alone
- Usable even when crystal structure is unknown
- Low computational cost

Concrete Examples:

2. Structure-based Descriptors

Characteristics:
- Utilize crystal structure information
- Capture structure-property relationships
- Require crystal structure data

Concrete Examples:

3. Property-based Descriptors

Characteristics:
- Predict unknown properties from known properties
- Utilize property correlations
- Difficult to apply to unknown materials

Concrete Examples:

2.6.2 Automatic Descriptor Generation (Using Matminer)

from matminer.featurizers.composition import ElementProperty, Stoichiometry
from matminer.featurizers.structure import DensityFeatures
from pymatgen.core import Composition

# Automatic generation of composition-based descriptors
comp = Composition("LiCoO2")

# Example 1: Element property-based descriptors (73 types)
element_featurizer = ElementProperty.from_preset("magpie")
element_features = element_featurizer.featurize(comp)

print(f"Number of generated descriptors: {len(element_features)}")
print(f"Example descriptors: {element_features[:5]}")

# Example 2: Composition ratio-based descriptors
stoich_featurizer = Stoichiometry()
stoich_features = stoich_featurizer.featurize(comp)

print(f"Composition ratio descriptors: {stoich_features}")

Descriptors Generated by Matminer (partial list):
- Average electronegativity, atomic radius, atomic weight
- Element position on periodic table (group, period)
- Electronic configuration (s-orbital electrons, p-orbital electrons, etc.)
- Average and variance of oxidation states
- Number of valence electrons

2.6.3 Descriptor Selection and Feature Engineering

Not all descriptors are useful:
- Irrelevant descriptors → Become noise and degrade model performance
- Redundant descriptors → Waste of computational cost

Descriptor Selection Methods:

1. Feature Importance

import matplotlib.pyplot as plt
import pandas as pd

# Random Forest feature importance
importances = model.feature_importances_
feature_names = X.columns

# Sort by importance
indices = np.argsort(importances)[::-1]

# Visualize top 20
plt.figure(figsize=(10, 6))
plt.bar(range(20), importances[indices[:20]])
plt.xticks(range(20), [feature_names[i] for i in indices[:20]], rotation=90)
plt.xlabel("Feature")
plt.ylabel("Importance")
plt.title("Feature Importance Top 20")
plt.tight_layout()
plt.show()

2. Correlation Analysis

# Correlation matrix between features
correlation_matrix = X.corr()

# Remove feature pairs with high correlation (>0.9)
high_corr_pairs = []
for i in range(len(correlation_matrix.columns)):
    for j in range(i+1, len(correlation_matrix.columns)):
        if abs(correlation_matrix.iloc[i, j]) > 0.9:
            high_corr_pairs.append((correlation_matrix.columns[i],
                                   correlation_matrix.columns[j]))

print(f"High correlation pairs: {len(high_corr_pairs)}")

3. Recursive Feature Elimination (RFE)

from sklearn.feature_selection import RFE

# Select best 50 features
selector = RFE(model, n_features_to_select=50, step=1)
selector.fit(X_train, y_train)

selected_features = X.columns[selector.support_]
print(f"Selected features: {list(selected_features)}")

2.7 Summary

What We Learned in This Chapter

1. Definition and Positioning of MI
   - Fusion of materials science and data science
   - Efficiency through inverse design approach
   - Differences from computational materials science and cheminformatics

2. 20 MI Terms
   - Data/model related (descriptors, feature engineering, overfitting, etc.)
   - Computational methods related (DFT, Bayesian optimization, GNN, etc.)
   - Materials science related (crystal structure, bandgap, phase diagram, etc.)

3. Materials Databases
   - Materials Project: Largest scale, beginner-friendly
   - AFLOW: Most crystal structure data
   - OQMD: Strong in phase diagram calculations
   - JARVIS: Integrated machine learning models
   - Using multiple databases together is recommended

4. MI Ecosystem
   - Cycle of data generation → processing → machine learning → experimental validation → improvement
   - Feedback loop is important

5. MI 5-Step Workflow
   - Step 0: Problem formulation (most important)
   - Step 1: Data collection (databases, literature, computation)
   - Step 2: Model building (descriptor design, training, evaluation)
   - Step 3: Prediction/screening (efficiently evaluate large candidates)
   - Step 4: Experimental validation (synthesis/measurement of top candidates)
   - Step 5: Data addition/improvement (continuous improvement cycle)

6. Details of Materials Descriptors
   - Three types: composition-based, structure-based, property-based
   - Automatic generation with Matminer
   - Importance of feature selection

To the Next Chapter

In Chapter 3, we'll put this knowledge into practice. Using actual Python code, you'll experience the entire flow from data acquisition from materials databases, descriptor generation, machine learning model building, to prediction.

Exercise Problems

Problem 1 (Difficulty: easy)

Select 5 terms from the MI glossary and explain them in your own words.

Problem 2 (Difficulty: medium)

Regarding the use of Materials Project vs AFLOW, answer which to use for the following scenarios with reasons.

Scenario A: Want to search for novel lithium-ion battery cathode materials. Need bandgap and formation energy data.

Scenario B: Want to find novel materials with similar crystal structures to existing materials. Need structural similarity search.

Problem 3 (Difficulty: medium)

Explain why Step 0 (problem formulation) in the MI workflow is most important, with concrete examples.

Problem 4 (Difficulty: hard)

There are three types of material descriptors: composition-based, structure-based, and property-based. List the advantages and disadvantages of each, and explain in what situations each should be used.

References

1. Materials Genome Initiative (MGI) - White House Office of Science and Technology Policy (2011)
   URL: https://www.mgi.gov
   U.S. materials development acceleration project launched in 2011. Triggered worldwide spread of MI.

2. 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

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. Curtarolo, S., Setyawan, W., Hart, G. L., Jahnatek, M., Chepulskii, R. V., et al. (2012). "AFLOW: An automatic framework for high-throughput materials discovery." Computational Materials Science, 58, 218-226.
   DOI: 10.1016/j.commatsci.2012.02.005
   AFLOW: http://www.aflowlib.org

5. Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013). "Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)." JOM, 65(11), 1501-1509.
   DOI: 10.1007/s11837-013-0755-4
   OQMD: http://oqmd.org

6. Choudhary, K., Garrity, K. F., Reid, A. C. E., DeCost, B., Biacchi, A. J., et al. (2020). "The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design." npj Computational Materials, 6(1), 173.
   DOI: 10.1038/s41524-020-00440-1
   JARVIS: https://jarvis.nist.gov

Author Information

This article was created as part of the MI Knowledge Hub project under Dr. Yusuke Hashimoto at Tohoku University.

Update History
- 2025-10-16: v3.0 Initial version created
- Expanded Section 2 from v2.1 (approximately 2,000 words) to 4,000-5,000 words
- Added 20-term glossary
- Added detailed materials database comparison table
- Added MI ecosystem diagram (Mermaid)
- Added detailed explanation of 5-step workflow
- Added deep-dive section on materials descriptors
- Added 4 exercise problems (difficulty: 1 easy, 2 medium, 1 hard)