Mohammed Azzouzi^1,2*, Steven Benett², Victor Posligua², Roberto Bondesan³, Martijn Zwijnenburg⁴, Kim Jelfs^2*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL)

2 Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, London United Kingdom

3 Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom

4 Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, United Kingdom

* Corresponding authors emails: mohammed.azzouzi@epfl.ch, K.jelfs@imperial.ac.uk

DOI10.24435/materialscloud:t7-5a [version v1]

Publication date: Nov 04, 2024

How to cite this record

Mohammed Azzouzi, Steven Benett, Victor Posligua, Roberto Bondesan, Martijn Zwijnenburg, Kim Jelfs, Exploring different search approaches to discover donor molecules for organic solar cells, Materials Cloud Archive 2024.178 (2024), https://doi.org/10.24435/materialscloud:t7-5a

Description

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 10¹⁴ molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources. This record contains the dataset generated during the exploration of the space of molecules formed of 6 building blocks for application as donor molecules for OPV application, with calculated properties such as Ionisation potential, excited state energy and oscillator strength.

Materials Cloud sections using this data

Files

File name	Size	Description
data_precurosr_db.7z MD5md5:1c86c777fd71fa769afd68eb377b2229	366.2 KiB	Zip file with the json files necessary to load the precursor database into a mongo db database using the notebook: 00_load_database_to_mongodb.ipynb
00_load_database_to_mongodb.ipynb MD5md5:9b6e5eb0d21f86f79dcd9488b211e052	42.1 KiB	Notebook to load the data into a mongodb database
stk_constructed.zip MD5md5:ac0e367515c0949dd49c27629934e5ba	737.2 MiB	File containing the json files for the constructed molecules database.
read_me.txt MD5md5:57878a4831e4c60a0dfd459db4068467	624 Bytes	Description of the files in the record

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Organic solar cells bayesian optimisation genetic algorithm building block approach Representation learning