🌐 EN | 🇯🇵 JP Last sync: 2025-11-08

🏫 Materials Informatics Dojo

Comprehensive Learning Platform for Data-Driven Materials Development

📚 29 Series | 📖 127 Chapters

🧭 New to Materials Informatics? Start here

📚 Introductory Series (5 series)
📘
Data-Driven Materials Introduction
Fundamentals of data-driven approaches in materials science
Beginner 60-90 min 4 chapters Prereq: Basic Python
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📘
Machine Learning Potentials (MLP) Introduction
Neural network potentials for atomistic simulations
📍 Stage 3 Intermediate 90-120 min 4 chapters Prereq: Python + ML basics
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📗
Bioinformatics Introduction
Protein engineering and biomaterial design with AI
Beginner-Intermediate 100-120 min 5 chapters Prereq: Basic Python
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📘
Materials Informatics Introduction
Opening the future of materials development with data-driven approaches
📍 Stage 1 Beginner 4 chapters Prereq: None
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💎
Nanomaterials Introduction
Introduction to nanomaterials science through Python practice
Beginner-Intermediate 4 chapters Prereq: Basic Python
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🤖 Machine Learning Methods (10 series)
🎯
Active Learning Introduction
Efficient exploration with active learning strategies
📍 Stage 3 Intermediate 80-100 min 4 chapters Prereq: Python + ML basics
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📊
Bayesian Optimization Introduction
Advanced optimization for materials discovery
📍 Stage 2 Intermediate-Advanced 90-120 min 4 chapters Prereq: Python + ML basics
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🧠
Graph Neural Networks (GNN) Introduction
Graph-based deep learning for molecular and crystal structures
Advanced 120-150 min 5 chapters Prereq: Python + ML basics
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🔬
GNN Features Comparison Introduction
Comparative analysis of graph neural network architectures
Advanced 80-100 min 6 chapters Prereq: Python + ML basics
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🎮
Reinforcement Learning Introduction
RL for materials design and process optimization
Advanced 90-120 min 4 chapters Prereq: Python + ML basics
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🔄
Transformer Introduction
Attention mechanisms for materials science applications
Advanced 80-100 min 4 chapters Prereq: Python + ML basics
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🕸️
Graph Neural Networks Introduction
Message passing and graph representations for materials data
📍 Stage 3 Intermediate-Advanced 5 chapters Prereq: Python + ML basics
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🧩
Introduction to Transformers and Foundation Models
Attention mechanisms and foundation models for materials science
Intermediate-Advanced 4 chapters Prereq: Python + ML basics
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🕹️
Introduction to Reinforcement Learning (Materials Science)
Agents and reward design for materials design and process optimization
Intermediate-Advanced 4 chapters Prereq: Python + ML basics
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🌀
Introduction to Quantum Machine Learning
Whether a quantum model beats a classical baseline on materials data, worked out rather than asserted: data encoding, quantum kernel methods and variational quantum circuits built from scratch in NumPy and measured against a tuned classical baseline under one strict protocol — with exponential concentration and dequantization explaining the results
Intermediate-Advanced 220-245 min 5 chapters Prereq: Quantum computing + ML basics
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🔧 Materials Features & Descriptors (2 series)
⚛️
Composition Features Introduction
Chemical composition-based descriptors for materials properties
📍 Stage 2 Intermediate 90-120 min 5 chapters Prereq: Basic Python
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🧪
Chemoinformatics Introduction
Molecular descriptors and QSAR/QSPR modeling
Intermediate 80-100 min 4 chapters Prereq: Basic Python
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💻 Computational Methods (5 series)
⚡
Computational Materials Basics Introduction
DFT, molecular dynamics, and quantum chemistry fundamentals
Intermediate 100-120 min 5 chapters Prereq: Quantum-chemistry basics
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🚀
High-Throughput Computing Introduction
Large-scale materials screening and workflow automation
📍 Stage 3 Advanced 100-120 min 5 chapters Prereq: Quantum-chemistry basics
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🤖
NIMO: Automated Materials Exploration with AI
Closed-loop optimization combining AI and robotic experiments
Beginner 90-120 min 5 chapters Prereq: None
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⚛️
Introduction to Quantum Chemistry
From the Schrödinger equation and DFT to first-principles calculations
Intermediate-Advanced 5 chapters Prereq: Basic Python
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🦾
Robotic Lab Automation Introduction
Automating experiments with robotic laboratory systems
📍 Stage 4 Advanced 5 chapters Prereq: MI basics
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📊 Data & Experimental (3 series)
🗄️
Materials Databases Introduction
Accessing and utilizing materials data repositories
📍 Stage 2 Beginner 70-90 min 4 chapters Prereq: Basic Python
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🔬
Experimental Data Analysis Introduction
Machine learning for experimental characterization data
📍 Stage 2 Intermediate 80-100 min 4 chapters Prereq: Basic Python
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🗺️
Materials Property Mapping
Visualizing high-dimensional materials space with GNN and dimensionality reduction
Beginner-Intermediate 4 chapters Prereq: Python + ML basics
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🎯 Application Domains (5 series)
🔋
Battery MI Application
Machine learning for battery materials discovery
📍 Stage 4 Intermediate 80-100 min 4 chapters Prereq: MI basics
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⚗️
Catalyst MI Application
AI-driven catalyst design and optimization
📍 Stage 4 Intermediate 80-100 min 4 chapters Prereq: MI basics
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💊
Drug Discovery MI Application
Machine learning in pharmaceutical materials development
📍 Stage 4 Intermediate 80-100 min 4 chapters Prereq: MI basics
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🏭
Materials Informatics Practical Applications
Frontiers of drug discovery, battery, and catalyst development
Beginner-Intermediate 4 chapters Prereq: MI basics
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⚙️
Introduction to Process Informatics
Data-driven optimization of chemical processes
Intermediate 4 chapters Prereq: Basic Python
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📋 Overview & Resources (2 series)
📰
MI Journals and Conferences Introduction
Guide to MI journals and conferences, from submission strategy to career path
Beginner-Intermediate 3 chapters Prereq: None
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🌐
Global Materials Informatics Research Projects
Database of national MI initiatives worldwide
Advanced 5 chapters Prereq: None
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