Series Overview
This series provides a comprehensive introduction to NIMO (formerly NIMS-OS), a Python library that orchestrates closed-loop automated materials exploration systems. NIMO combines robotic experiments with AI optimization algorithms to enable efficient materials discovery without human intervention.
Developed by the National Institute for Materials Science (NIMS) in Japan, NIMO represents a paradigm shift in how we approach materials research. Instead of manually selecting and executing experiments one by one, NIMO creates an intelligent loop where AI algorithms propose experiments, robots execute them, and results automatically inform the next iteration.
Why Learn NIMO?
The Challenge: Traditional materials discovery is slow and expensive. Researchers must manually decide which experiments to run, wait for results, and then plan the next steps. This process can take years for a single material optimization.
The Solution: NIMO automates this entire workflow. With 11 built-in AI optimization algorithms (including Bayesian Optimization), 3 robot system interfaces, and powerful visualization tools, NIMO can reduce the number of required experiments by up to 90% while discovering optimal materials faster.
Content of All 5 Chapters
Target Audience
- Undergraduate and graduate students interested in automated materials research
- Researchers who want to accelerate their experimental workflows
- Engineers setting up autonomous laboratory systems
- Data scientists exploring applications in materials science
Prerequisite Knowledge
Required:
- Python basics (variables, functions, lists, dictionaries)
- Basic understanding of materials science concepts
Recommended:
- Familiarity with Bayesian Optimization concepts (see our Bayesian Optimization series)
- Experience with pandas DataFrames and CSV files
Key Tools
- NIMO: The main orchestration library (
pip install nimo) - PHYSBO: Backend for Bayesian Optimization
- NumPy/Pandas: Data manipulation
- Matplotlib: Visualization
Learning Path
For Complete Beginners:
Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
Time required: 90-120 minutes
For Those Familiar with Bayesian Optimization:
Chapter 1 (skim) → Chapter 2 → Chapter 4 → Chapter 5
Time required: 60-80 minutes
Quick Practical Start:
Chapter 1 → Chapter 4
Time required: 40-50 minutes
Next Steps
After completing this series, we recommend:
- Bayesian Optimization Introduction: Deeper understanding of the optimization theory
- Active Learning Introduction: Advanced experiment selection strategies
- High-Throughput Computing: Scaling up computational workflows
Let's Get Started!
Ready to revolutionize your materials research? Start with Chapter 1 and learn how NIMO can transform your experimental workflow!