New materials can bring about tremendous progress in technology and applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. Now, a newly proposed idea of using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine learning algorithms and cross-validation procedures. Furthermore, we propose to assist traditional DFT calculations with machine learning for material discovery. Many experiments and literature reports have shown the great effects and prospects of this idea. It is currently showing its potential and advantages in property prediction, material discovery, inverse design, corrosion detection and many other aspects of life.

中文翻译：

---

机器学习驱动的新材料发现

新材料可以带来技术和应用的巨大进步。然而，常用的试错法无法满足当前对新材料的需求。现在，一种新提出的使用机器学习探索新材料的想法正变得流行起来。在本文中，我们回顾了将机器学习应用于材料发现的研究范式，包括数据预处理、特征工程、机器学习算法和交叉验证程序。此外，我们建议通过机器学习来辅助传统的 DFT 计算以进行材料发现。许多实验和文献报道都表明了这一想法的巨大效果和前景。它目前在性能预测、材料发现、逆向设计、