A Materials Acceleration Platform for Organic Laser Discovery
A Materials Acceleration Platform for Organic Laser Discovery
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DOI：
10.1002/adma.202207070
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发表时间：
2022-02
影响因子：
29.4
通讯作者：
Tony C Wu;Andrés Aguilar Granda;K. Hotta;S. Alasvand Yazdani;R. Pollice;Jenya Vestfrid;Han Hao;C. Lavigne;Martin Seifrid;Nicholas H. Angello;F. Bencheikh;J. Hein;M. Burke;C. Adachi;Alán Aspuru-Guzik
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文献类型：
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作者：
Tony C Wu;Andrés Aguilar Granda;K. Hotta;S. Alasvand Yazdani;R. Pollice;Jenya Vestfrid;Han Hao;C. Lavigne;Martin Seifrid;Nicholas H. Angello;F. Bencheikh;J. Hein;M. Burke;C. Adachi;Alán Aspuru-Guzik
传统材料的发现是一个费力而耗时的过程，从最初的材料概念到商业化可能需要几十年的时间。材料加速平台的最新发展有望通过实验自动化和机器学习来加速材料的发现。然而，化学领域的自动化工作大多集中在合成和化合物鉴定上，而对目标性质的综合表征关注较少。在这项工作中，引入了一个自动化平台，用于发现分子作为有机半导体激光器的增益介质，这是传统方法面临的一个挑战。该平台包括自动化乐高合成、产品鉴定和光学表征，可以以完全集成的端到端方式执行。利用该工作流程对有机激光器候选者进行筛选，发现了8种潜在的有机激光器候选者。对薄膜器件中四个分子的激光阈值进行了测试，并找到了两个具有最先进性能的分子。这些有希望的结果显示了自动化合成和筛选加速材料开发的潜力。
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.