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Turbocharged molecular discovery of OLED emitters: From high-throughput quantum simulation to highly efficient TADF devices

机译:OLED发射器的涡轮增压分子发现:从高通量量子模拟到高效的TADF器件

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Discovering new OLED emitters requires many experiments to synthesize candidates and test performance in devices. Large scale computer simulation can greatly speed this search process but the problem remains challenging enough that brute force application of massive computing power is not enough to successfully identify novel structures. We report a successful High Throughput Virtual Screening study that leveraged a range of methods to optimize the search process. The generation of candidate structures was constrained to contain combinatorial explosion. Simulations were tuned to the specific problem and calibrated with experimental results. Experimentalists and theorists actively collaborated such that experimental feedback was regularly utilized to update and shape the computational search. Supervised machine learning methods prioritized candidate structures prior to quantum chemistry simulation to prevent wasting compute on likely poor performers. With this combination of techniques, each multiplying the strength of the search, this effort managed to navigate an area of molecular space and identify hundreds of promising OLED candidate structures. An experimentally validated selection of this set shows emitters with external quantum efficiencies as high as 22%.
机译:发现新的OLED发射器需要许多实验来合成设备中的候选物和测试性能。大规模计算机仿真可以大大加速此搜索过程,但问题仍然足够具有挑战性,使得大规模计算能力的蛮力应用不足以成功识别新颖结构。我们报告了一个成功的高吞吐量虚拟筛选研究,利用了一系列方法来优化搜索过程。候选结构的产生被约束以含有组合爆炸。模拟被调整到特定问题并用实验结果进行校准。实验主义者和理论家积极合作,使实验反馈定期用于更新和塑造计算搜索。监督机器学习方法在量子化学模拟之前优先考虑候选结构,以防止浪费对可能的较差的表现者的计算。利用这种技术的组合,每个乘以搜索的强度,这项努力管理导航分子空间的区域并识别数百个有前途的OLED候选结构。该集合的实验验证选择显示了外部量子效率高达22%的发射器。

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