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Combining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties
Hole-transporting materials (HTM) are a critical class of organic semiconductors, required for the fabrication of a variety of state-of-the-art display and semiconductor devices. In this work, we apply the technique of using Recurrent Neural Networks (RNN) as a generative model on SMILES representation of molecules, which has demonstrated success in drug discovery, to design HTMs targeting specific properties. A set of training compounds were selected from OLED material chemical space expanded from commercial catalogs to run optoelectronic properties calculations using Quantum Mechanics (QM) tools from Schrӧdinger’s Materials Science Suite. Reinforcement learning was used to fine-tune pre-trained RNN based on both the prior likelihood and a scoring function defined by the user, thus allowing optimization of multiple properties at once, which has been a huge challenge in material design. We demonstrated that by carefully defining the applicable chemical space, and providing accurate physics-based property calculation on a large number of compounds, our data-driven approach, with the aid of advanced machine learning techniques, can be expanded to many different domains to systematically discover novel materials with targeted properties.


Apr 7, 2021 09:00 AM in Beijing, Shanghai

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