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RESEARCH PAPER · Frontiers in Chemistry · 2022

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations

H. Shaun Kwak, Yuling An, David J. Giesen, Thomas F. Hughes, Christopher T. Brown, Karl Leswing, Hadi Abroshan, Mathew David Halls

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42 direct citing works

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular SimulationsOriginalUtilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite0Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics26Virtual Screening for Organic Solar Cells and Light Emitting Diodes2366‐3: Active Learning for the Design of Novel OLED Materials7Machine Learning for the Design of Novel OLED Materials10Definition and exploration of realistic chemical spaces using the connectivity and cyclic features of ChEMBL and ZINC10Generative organic electronic molecular design informed by quantum chemistry21Seven-member-ring-based electron-transporting materials for high-efficiency OLEDs7Synthesis, Crystal Structure, Hirshfeld Surface, and Computational Analysis of Three Novel Imidazolium Oxalate as Charge Transport Material0The Problem of Scientific Teleology in Goal-Directed Materials Optimization0Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization8733‐4: Supramolecules in Thin Films and OLED Efficiencies157‐1: Accelerating Next‐Generation Display Materials Development with a Smart Digital Chemistry Platform0High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes22Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Energy Levels0Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels1Hole transport materials for QLEDs: a combined approach of machine learning and atomistic simulation1Machine-learning-assisted performance improvements for multi-resonance thermally activated delayed fluorescence molecules18COMPAS-3: a dataset of peri -condensed polybenzenoid hydrocarbons21Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery278A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices9COMPAS-3: a Data Set of peri-CondensedPolybenzenoid Hydrocarbons0Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning25Precise Regulation on the Bond Dissociation Energy of Exocyclic C–N Bonds in Various N-Heterocycle Electron Donors via Machine Learning17Deep Learning Prediction of Triplet–Triplet Annihilation Parameters in Blue Fluorescent Organic Light‐Emitting Diodes10Synthesis, Crystal Structure, Hirshfeld Surface and Computational Analysis of Bis-[2-(3,4-dihydroxyphenyl)-4,5-diphenyl-1H-imidazol-3-ium] Oxalate Ethanol Solvate185‐4: Digital Chemistry, Data Processing, and Collaborative Ideation for Development of OLEDs0Construction frontier molecular orbital prediction model with transfer learning for organic materials36AI-Guided Design of MALDI Matrices: Exploring the Electron Transfer Chemical Space for Mass Spectrometric Analysis of Low-Molecular-Weight Compounds4Designing the next generation of polymers with machine learning and physics-based models14Symmetry-constrained generation of diverse low-bandgap molecules with Monte Carlo tree search0Efficient Molecular Discovery via Bayesian Optimization with Adaptive Parallel Evaluation0Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities16EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions1Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs3Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review3Digital discovery of large-scale optoelectronic materials via MPNICE machine-learning force fields1High-throughput virtual screening for organic electronics: From energy levels to advanced properties and physical insight0Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding083‐1: Accelerating Optoelectronic Innovation via Integrating Machine Learning and Physics‐Based Modeling0Organic Materials of Tomorrow: Horizons of Artificial Intelligence0Artificial Intelligence in Organic Synthesis0

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  1. Utilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite

    Proceedings of the Python in Science Conferences · 2022-01-01

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  2. Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics

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  3. Virtual Screening for Organic Solar Cells and Light Emitting Diodes

    Advanced Science · 2022-04-22

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  4. 66‐3: Active Learning for the Design of Novel OLED Materials

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  5. Machine Learning for the Design of Novel OLED Materials

    ACS symposium series · 2022-06-14

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  6. Definition and exploration of realistic chemical spaces using the connectivity and cyclic features of ChEMBL and ZINC

    Digital Discovery · 2023-01-01

    Thomas Cauchy, Jules Leguy, Benoit Da Mota

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  7. Generative organic electronic molecular design informed by quantum chemistry

    Chemical Science · 2023-01-01

    Cheng-Han Li, Daniel P. Tabor

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  8. Seven-member-ring-based electron-transporting materials for high-efficiency OLEDs

    Materials Advances · 2023-01-01

    Kuo-Hsien Chou, Tun-Hao Chen, Xianqing Huang, Chia-Sheng Huang, Chih‐Hao Chang, Chien‐Tien Chen, Jwo‐Huei Jou

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  9. Synthesis, Crystal Structure, Hirshfeld Surface, and Computational Analysis of Three Novel Imidazolium Oxalate as Charge Transport Material

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  10. The Problem of Scientific Teleology in Goal-Directed Materials Optimization

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

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  11. Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

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    Liqianyun Xu, Feng Wu, Renjie Chen, Li Li

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  12. 33‐4: Supramolecules in Thin Films and OLED Efficiencies

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  13. 57‐1: Accelerating Next‐Generation Display Materials Development with a Smart Digital Chemistry Platform

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  14. High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes

    Chemistry of Materials · 2023-06-27

    Hadi Abroshan, H. Shaun Kwak, Anand R. Chandrasekaran, Alex K. Chew, Alexandr Fonari, Mathew David Halls

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  15. Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Energy Levels

    ChemRxiv · 2023-06-30

    Cheng-Han Li, Daniel P. Tabor

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  16. Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels

    ChemRxiv · 2023-07-02

    Cheng-Han Li, Daniel P. Tabor

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  17. Hole transport materials for QLEDs: a combined approach of machine learning and atomistic simulation

    Unknown journal · 2023-08-17

    Hadi Abroshan, H. Shaun Kwak, Anand R. Chandrasekaran, Alex K. Chew, Alexandr Fonari, Mathew David Halls

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  18. Machine-learning-assisted performance improvements for multi-resonance thermally activated delayed fluorescence molecules

    Physical Chemistry Chemical Physics · 2023-11-29

    Wanlin Cai, Cheng Zhong, Ziwei Ma, Zhuan‐Yun Cai, Yue Qiu, Zubia Sajid, De‐Yin Wu

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  19. COMPAS-3: a dataset of peri -condensed polybenzenoid hydrocarbons

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  20. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

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  21. A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices

    Scientific Reports · 2024-02-22

    Kosuke Sato, Kazuki Hattori, Fuminari Uehara, Tomoko Kitaguni, Toshiki Nishiura, Takuya Yamagata, Keisuke Nomura, Naoki Matsumoto, Tsuyoshi Tanaka, Hidenori Aihara

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  22. COMPAS-3: a Data Set of peri-CondensedPolybenzenoid Hydrocarbons

    ChemRxiv · 2024-02-26

    Alexandra Wahab, Renana Gershoni‐Poranne

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  23. Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

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  24. Precise Regulation on the Bond Dissociation Energy of Exocyclic C–N Bonds in Various N-Heterocycle Electron Donors via Machine Learning

    The Journal of Physical Chemistry Letters · 2024-04-16

    Qingyu Meng, Rui Wang, Haoyun Shao, Yilei Wang, Xue-Liang Wen, Cheng-Yu Yao, Juan Qiao

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  25. Deep Learning Prediction of Triplet–Triplet Annihilation Parameters in Blue Fluorescent Organic Light‐Emitting Diodes

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    Junseop Lim, Jae‐Min Kim, Jun Yeob Lee

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  26. Synthesis, Crystal Structure, Hirshfeld Surface and Computational Analysis of Bis-[2-(3,4-dihydroxyphenyl)-4,5-diphenyl-1H-imidazol-3-ium] Oxalate Ethanol Solvate

    Journal of Chemical Crystallography · 2024-05-05

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  27. 85‐4: Digital Chemistry, Data Processing, and Collaborative Ideation for Development of OLEDs

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  28. Construction frontier molecular orbital prediction model with transfer learning for organic materials

    npj Computational Materials · 2024-09-11

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  29. AI-Guided Design of MALDI Matrices: Exploring the Electron Transfer Chemical Space for Mass Spectrometric Analysis of Low-Molecular-Weight Compounds

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  30. Designing the next generation of polymers with machine learning and physics-based models

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  31. Symmetry-constrained generation of diverse low-bandgap molecules with Monte Carlo tree search

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  33. Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities

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  34. EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions

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  35. Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs

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  36. Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review

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  37. Digital discovery of large-scale optoelectronic materials via MPNICE machine-learning force fields

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