Moosavi Seyed Mohamad, Jablonka Kevin Maik, Smit Berend
Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland.
J Am Chem Soc. 2020 Nov 10;142(48):20273-87. doi: 10.1021/jacs.0c09105.
Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.
开发用于材料合理设计与发现的算法方法，能够使我们系统地找到新型材料，这些材料可能会产生巨大的技术和社会影响。然而，这种合理设计需要对整个多阶段设计过程有一个全面的视角，这涉及到探索巨大的材料空间、它们的属性、工艺设计与工程以及技术经济评估。使用传统科学方法探索所有这些选项的复杂性似乎难以解决。相反，机器学习领域的新型工具可能会在合理材料设计的道路上解决我们面临的一些挑战。在这里，我们回顾这些方法的一些主要进展及其在合理材料设计中的应用，随后讨论我们目前共同面临的一些主要挑战和机遇，以及我们对合理材料设计与发现未来的展望。
