Machine Learning-Assisted Nanomaterial Design and Preparation

Xiaoyang Wang; Yifang Zhao; Chenyi Liu; Leyan Fan; Dejun Xue; Guolei Xiang

doi:10.7536/PC20250704

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Prog Chem ›› 2026, Vol. 38 ›› Issue (2) : 181-193. DOI: 10.7536/PC20250704

Review

Machine Learning-Assisted Nanomaterial Design and Preparation

Xiaoyang Wang ¹ ,
Yifang Zhao ² ,
Chenyi Liu ¹ ,
Leyan Fan ³ ,
Dejun Xue ²^,^* ,
Guolei Xiang ¹^,^*

Author information +

History +

Abstract

Recent advances in machine learning （ML） have demonstrated remarkable potential in revolutionizing the design，property prediction，and synthesis optimization of nanomaterials，facilitating a paradigm shift from traditional empirical approaches to data-driven methodologies in nanoscience. This review examines the research frameworks and cutting-edge developments in ML-assisted nanomaterial design and fabrication，with a focus on representative material systems，including zero-dimensional quantum dots，one-dimensional nanotubes，two-dimensional materials，and metal-organic frameworks （MOFs）. Key technical aspects such as data acquisition and feature engineering，supervised and unsupervised modeling，generative algorithms，and automated experimental platforms are critically discussed. Furthermore，we highlight emerging challenges and future directions，emphasizing the need for standardized databases，physics-informed ML models，and closed-loop experimental systems to enable intelligent and efficient nanomaterial development. This work provides a comprehensive methodological reference for the integration of ML in next-generation nanomaterial research.

Contents

1 Introduction

2 Machine learning application framework

2.1 Acquisition and standardized preprocessing of high-quality data

2.2 Representation methods and feature engineering for material structures

2.3 Model construction and training

2.4 Validation and generalization assessment

2.5 Performance prediction and material screening

2.6 Inverse design and generative structural optimization

3 Representative research progress

3.1 Zero-dimensional nanomaterials

3.2 One-dimensional nanomaterials

3.3 Two-dimensional nanomaterials

3.4 Metal-organic frameworks

4 Conclusion and outlook

Key words

artificial intelligence / machine learning / nanomaterial synthesis / feature engineering / intelligent synthesis

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Xiaoyang Wang , Yifang Zhao , Chenyi Liu , et al . Machine Learning-Assisted Nanomaterial Design and Preparation[J]. Progress in Chemistry. 2026, 38(2): 181-193 https://doi.org/10.7536/PC20250704

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Funding

Advanced Materials-National Science and Technology Major Project(2025ZD0619900)

National Natural Science Foundation of China(22575013)

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