A Materials Acceleration Platform for Organic Laser Discovery. Issue 6 (14th December 2022) - Wu, Tony C; Aguilar‐Granda, Andrés; Hotta, Kazuhiro; Yazdani, Sahar Alasvand; Pollice, Robert; Vestfrid, Jenya; Hao, Han; Lavigne, Cyrille; Seifrid, Martin; Angello, Nicholas; Bencheikh, Fatima; Hein, Jason E.; et al.
Abstract: 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. Abstract : The accelerated discovery of two molecules with a state‐of‐the‐art organic laser performance from screening 40 candidates is made possible by the introduction of an automated platform encompassing automated synthesis, product identification, and optical characterization that can be run fully end‐to‐end. These promising results show the potential of automated synthesis and accelerated discovery of materials.