Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics

Moore, Gareth John; Bardagot, Olivier; Banerji, Natalie (2022). Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics. Advanced theory and simulations, 5(5), p. 2100511. Wiley 10.1002/adts.202100511

[img]
Preview
Text
Advcd_Theory_and_Sims_-_2022_-_Moore_-_Deep_Transfer_Learning__A_Fast_and_Accurate_Tool_to_Predict_the_Energy_Levels_of.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (1MB) | Preview

Molecular engineering is driving the recent efficiency leaps in organicphotovoltaics (OPVs). A presynthetic determination of frontier energy levelsmakes the screening of potential molecules more efficient, exhaustive, andcost-effective. Here, a convolutional neural network is developed to predictthe highest occupied and lowest unoccupied molecular orbital(HOMO/LUMO) levels of donor molecules for OPV. The model takes a 2Dstructure image and returns a prediction of its HOMO/LUMO levelscomparable to experimental values. Insufficient experimental datasets areovercome with transfer learning where the model is initially trained on thelarge Harvard Clean Energy Project dataset and then fine-tuned usingexperimental data from the Harvard Organic Photovoltaic dataset. Errormargins on predicted HOMO/LUMO levels below 200 meV are achieved,without any chemical knowledge implemented. Noticeably, the model outputshave higher accuracy and precision than corresponding density functionaltheory (DFT) estimations. The model and its limitations are further tested ona home-built dataset of commercially available donor polymers reported inOPVs (e.g., P3HT, PTB7-Th, PM6, D18). The results demonstrate both thepractical utility of this model, to foster rational molecular engineering for OPVoptimization, and the potential for deep learning techniques, in general, torevolutionize the energy materials research and development sector.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP)

UniBE Contributor:

Moore, Gareth John; Bardagot, Olivier Nicolas Ludovic and Banerji, Natalie

Subjects:

500 Science > 540 Chemistry

ISSN:

2513-0390

Publisher:

Wiley

Funders:

[4] Swiss National Science Foundation ; [UNSPECIFIED] University of Bern

Language:

English

Submitter:

Olivier Nicolas Ludovic Bardagot

Date Deposited:

06 Apr 2022 10:57

Last Modified:

15 May 2022 01:53

Publisher DOI:

10.1002/adts.202100511

BORIS DOI:

10.48350/168360

URI:

https://boris.unibe.ch/id/eprint/168360

Actions (login required)

Edit item Edit item
Provide Feedback