Molecular generation targeting desired electronic properties via deep generative models

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Title: Molecular generation targeting desired electronic properties via deep generative models
Authors: Yuan, Q
Santana Bonilla, A
Zwijnenburg, MA
Jelfs, K
Item Type: Journal Article
Abstract: As we seek to discover new functional materials, we need ways to explore the vast chemical space of precursor building blocks, not only generating large numbers of possible building blocks to investigate, but trying to find non-obvious options, that we might not suggest by chemical experience alone. Artificial intelligence techniques provide a possible avenue to generate large numbers of organic building blocks for functional materials, and can even do so from very small initial libraries of known building blocks. Specifically, we demonstrate the application of deep recurrent neural networks for the exploration of the chemical space of building blocks for a test case of donor–acceptor oligomers with specific electronic properties. The recurrent neural network learned how to produce novel donor–acceptor oligomers by trading off between selected atomic substitutions, such as halogenation or methylation, and molecular features such as the oligomer's size. The electronic and structural properties of the generated oligomers can be tuned by sampling from different subsets of the training database, which enabled us to enrich the library of donor–acceptors towards desired properties. We generated approximately 1700 new donor–acceptor oligomers with a recurrent neural network tuned to target oligomers with a HOMO–LUMO gap <2 eV and a dipole moment <2 Debye, which could have potential application in organic photovoltaics.
Issue Date: 9-Mar-2020
Date of Acceptance: 4-Mar-2020
DOI: 10.1039/C9NR10687A
ISSN: 2040-3364
Publisher: Royal Society of Chemistry
Start Page: 6744
End Page: 6758
Journal / Book Title: Nanoscale
Volume: 12
Issue: 12
Copyright Statement: © The Royal Society of Chemistry 2020
Sponsor/Funder: The Royal Society
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
The Royal Society
Funder's Grant Number: UF120469
Keywords: 02 Physical Sciences
03 Chemical Sciences
10 Technology
Nanoscience & Nanotechnology
Publication Status: Published online
Online Publication Date: 2020-03-09
Appears in Collections:Chemistry