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Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.

Published version
Peer-reviewed

Repository DOI


Change log

Authors

Jung, Guwon 

Abstract

Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.

Description

Publication status: Published

Keywords

Bayes Theorem, Neural Networks, Computer, Machine Learning, Density Functional Theory, Spectrum Analysis

Journal Title

J Chem Inf Model

Conference Name

Journal ISSN

1549-9596
1549-960X

Volume Title

64

Publisher

American Chemical Society (ACS)
Sponsorship
ISIS Neutron and Muon Source (NA)
BASF (NA)
Rutherford Appleton Laboratory (NA)
Royal Academy of Engineering (RCSRF1819/7/10)