Machine learning based approaches to material discovery are reviewed with the aim of providing a perspective on the current state of the art and its potential. Various models used to represent molecules and crystals are introduced and such representations can be used within the neural networks to generate materials that satisfy specified physical features and properties. For problems where large database for structure-property map cannot be created, the active learning approaches based on Bayesian optimization to maximize the efficiency of a search are reviewed. Successful applications of these machine learning based material discovery approaches are beginning to appear and some of the notable ones are reviewed.

回顾了基于机器学习的材料发现方法，目的是提供对当前技术状态及其潜力的看法。引入了用于表示分子和晶体的各种模型，并且可以在神经网络中使用此类表示来生成满足特定物理特征和特性的材料。对于无法创建结构-性质图的大型数据库的问题，回顾了基于贝叶斯优化以最大化搜索效率的主动学习方法。这些基于机器学习的材料发现方法的成功应用开始出现，并审查了一些值得注意的方法。