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The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent
Ke Wu1, Zefan Sang1, Guangxun Zhang1,2
1School of Chemistry and Materials, Yangzhou Key Laboratory of Smart Materials and Clean Energy, Yangzhou University, Yangzhou, Jiangsu, China.
Small (Weinheim an Der Bergstrasse, Germany)
|August 10, 2026
Summary
Machine learning accelerates micro-nano material design by predicting structure-property relationships, reducing experiments. This review explores its impact on material discovery, performance, and future directions in materials science.
Area of Science:
- Materials Science
- Computational Chemistry
- Nanotechnology
Background:
- Micro-nano materials (e.g., MOFs, 2D materials, nanoparticles) exhibit unique properties due to high surface area and size effects.
- Traditional experimental methods for micro-nano material discovery are time-consuming and labor-intensive.
- Machine learning (ML) offers a powerful approach to overcome these limitations.
Purpose of the Study:
- To review the transformative impact of ML on micro-nano material research.
- To highlight ML applications in performance prediction, geometric generation, and intelligent design.
- To discuss current challenges and future prospects of ML in this field.
Main Methods:
- Literature review of machine learning applications in micro-nano materials.
- Analysis of ML's role in structure-property prediction.
- Exploration of ML's use in material design and generation.
Main Results:
- ML enables precise prediction of micro-nano material structure and properties.
- Accelerated rational material discovery and optimization, minimizing experimental efforts.
- Successful applications demonstrated in catalysis, energy storage, and battery technologies.
Conclusions:
- Machine learning is revolutionizing micro-nano material science, shifting from empirical to intelligent design.
- Addressing current limitations and exploring emerging directions will further enhance ML's capabilities.
- This review provides guidance for future research in ML-driven materials discovery.