Research Topic

Driving Innovation in Organic Optoelectronic Materials with Physics-based and Machine-learning De Novo Methods

About this Research Topic

Organic materials are a rapidly evolving class of compounds for applications in current and future optoelectronic devices. Contributing to the swift pace of discovery of new materials are computational methods based on physics, machine learning, or an astute combination of both. This is a highly interdisciplinary research area where diverse approaches yield an assortment of different chemical motifs with the desired multi-property design profile. Many of these possibilities would not have been identified using traditional approaches, and are often accompanied by new insights into fundamental structure-property relationships influencing device operation.

This Research Topic focuses on the computer-aided discovery of novel materials, leveraging machine learning, quantum mechanics and classical simulation, with an emphasis on the optical and electrical properties of organic molecules, oligomers, and polymers. High-performing material properties are an essential factor; however, the ideation of new chemistry is at the forefront of this topic. Attention will be given to methodology and applications relating to carrier transporting materials, host materials, and (co-deposited) emitting materials. Design of organic compounds in contact with metallic and oxide electrodes to reduce injection barriers are also encouraged. Finally, unique approaches to computationally-driven processing conditions, processing methods, and device structure are welcome.

We welcome Original Research, Reviews, Mini Reviews and Perspective Articles on themes including, but not limited to:
De novo design, high-throughput screening (HTS) and machine learning strategies for the design of novel carrier transporting materials, host materials, and (co-deposited) emitting materials
• Interactive and automated physics-based simulation workflows
• Machine learning models and generative networks
• Cloud based massive scale computing (e.g. embarrassingly parallel quantum mechanics simulation)
• Computer-aided materials design of a new generation of optoelectronic materials
• Design of novel organic compounds in contact with metallic and oxide electrodes
• New strategies and mechanisms for improved performance materials 
In silico frameworks for processing and fabrication optimization

Topic Editor Mathew D. Halls, is the founder and Vice President of Materials Science of Schrödinger Inc. Topic Editor Paul Winget is also affiliated with Schrödinger Inc. Topic Editor Rafael Gomez-Bombarelli is an assistant professor ant MIT DMSE, Chief Learning Officer at ZebiAI and a consultant at Kyulux North America, has received research funding from Sumitomo Chemical, Asahi Glass Company, and holds multiple patents in the area of organic light emitting diodes.


Keywords: ED, OPV, machine learning, artificial intelligence, de novo, quantum chemistry, molecular dynamics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Organic materials are a rapidly evolving class of compounds for applications in current and future optoelectronic devices. Contributing to the swift pace of discovery of new materials are computational methods based on physics, machine learning, or an astute combination of both. This is a highly interdisciplinary research area where diverse approaches yield an assortment of different chemical motifs with the desired multi-property design profile. Many of these possibilities would not have been identified using traditional approaches, and are often accompanied by new insights into fundamental structure-property relationships influencing device operation.

This Research Topic focuses on the computer-aided discovery of novel materials, leveraging machine learning, quantum mechanics and classical simulation, with an emphasis on the optical and electrical properties of organic molecules, oligomers, and polymers. High-performing material properties are an essential factor; however, the ideation of new chemistry is at the forefront of this topic. Attention will be given to methodology and applications relating to carrier transporting materials, host materials, and (co-deposited) emitting materials. Design of organic compounds in contact with metallic and oxide electrodes to reduce injection barriers are also encouraged. Finally, unique approaches to computationally-driven processing conditions, processing methods, and device structure are welcome.

We welcome Original Research, Reviews, Mini Reviews and Perspective Articles on themes including, but not limited to:
De novo design, high-throughput screening (HTS) and machine learning strategies for the design of novel carrier transporting materials, host materials, and (co-deposited) emitting materials
• Interactive and automated physics-based simulation workflows
• Machine learning models and generative networks
• Cloud based massive scale computing (e.g. embarrassingly parallel quantum mechanics simulation)
• Computer-aided materials design of a new generation of optoelectronic materials
• Design of novel organic compounds in contact with metallic and oxide electrodes
• New strategies and mechanisms for improved performance materials 
In silico frameworks for processing and fabrication optimization

Topic Editor Mathew D. Halls, is the founder and Vice President of Materials Science of Schrödinger Inc. Topic Editor Paul Winget is also affiliated with Schrödinger Inc. Topic Editor Rafael Gomez-Bombarelli is an assistant professor ant MIT DMSE, Chief Learning Officer at ZebiAI and a consultant at Kyulux North America, has received research funding from Sumitomo Chemical, Asahi Glass Company, and holds multiple patents in the area of organic light emitting diodes.


Keywords: ED, OPV, machine learning, artificial intelligence, de novo, quantum chemistry, molecular dynamics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Topic Editors

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Submission Deadlines

22 June 2021 Abstract
22 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

22 June 2021 Abstract
22 September 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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