Imperial College London


Faculty of Natural SciencesDepartment of Chemistry

Senior Lecturer



+44 (0)20 7594 3438k.jelfs Website




207AMolecular Sciences Research HubWhite City Campus






BibTex format

author = {Yuan, Q and Santana, Bonilla A and Zwijnenburg, MA and Jelfs, K},
doi = {10.1039/C9NR10687A},
journal = {Nanoscale},
pages = {6744--6758},
title = {Molecular generation targeting desired electronic properties via deep generative models},
url = {},
volume = {12},
year = {2020}

RIS format (EndNote, RefMan)

AB - 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.
AU - Yuan,Q
AU - Santana,Bonilla A
AU - Zwijnenburg,MA
AU - Jelfs,K
DO - 10.1039/C9NR10687A
EP - 6758
PY - 2020///
SN - 2040-3364
SP - 6744
TI - Molecular generation targeting desired electronic properties via deep generative models
T2 - Nanoscale
UR -
UR -!divAbstract
UR -
VL - 12
ER -