Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods
Name
fchem-10-973254.pdf
Description
Published version
Size
534.01 KB
Format
Adobe PDF
Checksum (MD5)
3817f5ed909f3804fa56324b2a8580c7
Author(s)
Winget, Paul
Halls, Mathew D
Gómez-Bombarelli, Rafael
Adachi, Chihaya
Date Issued
August 3, 2022
Journal
Frontiers in Chemistry
Publisher
Frontiers Media SA
Citation
Winget P, Halls MD, Gómez-Bombarelli R and Adachi C (2022) Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods. Front. Chem. 10:973254.
Version
Final published version
Abstract
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 chemistries would not have been identified using traditional approaches, often accompanied with new insights into fundamental structure-property relationships influencing device operation. This Research Topic brings together nine articles on the latest advances in this research area.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fchem.2022.973254
