With the rapid developments in the field of information technology, the material research society is looking for an alternate scientific route to the traditional methods of trial and error in material research and process development. Machine learning emerges as a new research paradigm to accelerate the application‐oriented material discovery. Quantum dots are expanded as functional nanomaterials to enhance cutting‐edge photonic technology. However, they suffer from uncertainty in industrial fabrication and application. Here, we discuss how machine learning accelerates the development of quantum dots. The basic principles and operation procedures of machine learning are described with a few representative examples of quantum dots. We emphasize how machine learning contributes to the optimization of synthesis and the analysis of material characterizations. To conclude, we give a short perspective discussing the problems of combining machine learning and quantum dots.

中文翻译：

---

机器学习如何加速量子点的发展？†

随着信息技术领域的飞速发展，材料研究学会正在寻找替代材料研究和过程开发中传统试验和错误方法的科学途径。机器学习作为一种新的研究范式应运而生，以加速面向应用程序的材料发现。量子点作为功能纳米材料得到了扩展，以增强尖端的光子技术。但是，它们在工业制造和应用中存在不确定性。在这里，我们讨论机器学习如何加速量子点的发展。机器学习的基本原理和操作过程以量子点的一些代表性示例进行了描述。我们强调机器学习如何有助于优化合成和材料表征分析。总而言之，我们简要讨论了将机器学习与量子点相结合的问题。