ArticleFrontiers in chemistry2021
Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations.
Article in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.ACS omega · 2026Review
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning.JACS Au · 2024Article
- A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices.Scientific reports · 2024Article
- Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.Life (Basel, Switzerland) · 2024Review
- Generative organic electronic molecular design informed by quantum chemistry.Chemical science · 2023Article
- Virtual Screening for Organic Solar Cells and Light Emitting Diodes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics.Frontiers in chemistry · 2021Article
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Authors and funding
8 authors.
Funding
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Abstract
In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals.
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