Department of Chemistry, University of Toronto, 80 St George St, Toronto, ON, M5S 3H6, Canada.
Center for Organic Photonics and Electronics Research (OPERA), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
Adv Mater. 2023 Feb;35(6):e2207070. doi: 10.1002/adma.202207070. Epub 2022 Dec 14.
Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development.
传统的材料发现是一个费力且耗时的过程，从材料的最初构思到商业化可能需要几十年的时间。最近材料加速平台的发展有望通过自动化实验和机器学习来加速材料发现。然而，化学领域的大多数自动化工作都集中在合成和化合物识别上，而集成的目标特性表征则受到较少关注。在这项工作中，引入了一种自动化平台，用于发现有机半导体激光器的增益介质分子，这是传统方法面临的挑战。该平台包括自动化乐高式合成、产品识别和光学特性，可以以完全集成的端到端方式执行。使用这种工作流程筛选有机激光候选物，发现了八种潜在的有机激光候选物。在薄膜器件中测试了四种分子的激光阈值，并发现了两种具有最先进性能的分子。这些有希望的结果表明，自动化合成和筛选在加速材料开发方面具有潜力。
