Fu Ziyang, Liu Weiyi, Huang Chen, Mei Tao
School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China.
Hubei Software Engineering Technology Research Center, Wuhan 430062, China.
Nanomaterials (Basel). 2022 Aug 26;12(17):2957. doi: 10.3390/nano12172957.
With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.
随着许多领域需求的不断增加，材料也在不断发展。然而，它们仍然存在众多实际限制。新材料的合理设计与发现能够产生巨大的技术和社会影响。然而，这种合理设计与发现需要一个全面的、多阶段的设计过程，包括材料成分设计、材料结构设计、材料性能设计以及工艺设计与工程。使用传统科学方法进行如此复杂的探索不仅盲目，而且是对时间和资源的巨大浪费。机器学习（ML）通过处理数据来寻找材料性能之间的相关性并理解材料的化学性质，正被视为探索材料领域的一种新方法。本文综述了机器学习在材料性能预测领域的一些近期主要进展和应用，并讨论了这一交叉领域的关键挑战与机遇。
