| Autor: |
Tony C Wu, Andrés Aguilar‐Granda, Kazuhiro Hotta, Sahar Alasvand Yazdani, Robert Pollice, Jenya Vestfrid, Han Hao, Cyrille Lavigne, Martin Seifrid, Nicholas Angello, Fatima Bencheikh, Jason E. Hein, Martin Burke, Chihaya Adachi, Alán Aspuru‐Guzik |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Advanced materials (Deerfield Beach, Fla.). |
| ISSN: |
1521-4095 |
| Popis: |
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, we introduce an automated platform for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. Our 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, we have discovered 8 potential candidates for organic lasers. We test the lasing threshold of 4 molecules in thin-film devices and find 2 molecules with state-of-the-art performance. These promising results show the potential of automated synthesis and screening for accelerated materials development. This article is protected by copyright. All rights reserved. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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