Greenaway Rebecca L, Jelfs Kim E
Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, W12 0BZ, UK.
Adv Mater. 2021 Mar;33(11):e2004831. doi: 10.1002/adma.202004831. Epub 2021 Feb 9.
Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.
有机材料在一系列领域都有应用，包括光电子学、传感、封装、分子分离和光催化。然而，材料的发现过程极其缓慢，尤其是与基于几乎无限的有机分子前体选择所构成的巨大化学可能性空间相比时。预测任何分子的材料组装及其相应性能的困难是靶向材料设计的另一个重大障碍。在开发用于筛选大量材料的结构和性能的计算方法方面已经取得了重大进展，这有助于指导合成研究人员找到有前景的材料。特别是，人工智能技术有可能在发现过程的许多环节产生重大影响。与此同时，自动化和机器人技术正在提高材料合成能够实现的规模和速度。在此，重点是展示整合计算和实验材料发现计划的力量，包括对方法可以结合的关键情况的总结以及一系列展示近期成功的案例研究。
