The Library
High-throughput property-driven generative design of functional organic molecules
Tools
Westermayr, Julia, Gilkes, Joe, Barrett, Rhyan and Maurer, Reinhard J. (2023) High-throughput property-driven generative design of functional organic molecules. Nature Computational Science, 3 . pp. 139-148. doi:10.1038/s43588-022-00391-1 ISSN 2662-8457.
|
PDF
WRAP-high-throughput-property-driven-generative-design-functional-organic-molecules-2023.pdf - Accepted Version - Requires a PDF viewer. Download (5Mb) | Preview |
Official URL: https://doi.org/10.1038/s43588-022-00391-1
Abstract
The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design.
Item Type: | Journal Article | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QD Chemistry | ||||||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | ||||||||||||||||||||||||
SWORD Depositor: | Library Publications Router | ||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Molecular structure, Molecular structure -- Computer simulation, Quantum chemistry -- Computer programs | ||||||||||||||||||||||||
Journal or Publication Title: | Nature Computational Science | ||||||||||||||||||||||||
Publisher: | Nature Publishing Group | ||||||||||||||||||||||||
ISSN: | 2662-8457 | ||||||||||||||||||||||||
Official Date: | 6 February 2023 | ||||||||||||||||||||||||
Dates: |
|
||||||||||||||||||||||||
Volume: | 3 | ||||||||||||||||||||||||
Page Range: | pp. 139-148 | ||||||||||||||||||||||||
DOI: | 10.1038/s43588-022-00391-1 | ||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s43588-022-00391-1. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/acceptedmanuscript-terms. | ||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||||||||
Date of first compliant deposit: | 17 February 2023 | ||||||||||||||||||||||||
Date of first compliant Open Access: | 6 August 2023 | ||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year