Imperial College London
Portrait Picture

ProfessorAronWalsh

Faculty of Engineering, Department of Materials

Chair in Materials Design
 
 
 
//

Contact

 

+44 (0)20 7594 1178a.walsh Website

 
 
//

Location

 

2.10Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Verma:2022:10.1063/5.0084535,
author = {Verma, S and Rivera, M and Scanlon, DO and Walsh, A},
doi = {10.1063/5.0084535},
journal = {Journal of Chemical Physics},
pages = {134116--134116},
title = {Machine learned calibrations to high-throughput molecular excited state calculations.},
url = {http://dx.doi.org/10.1063/5.0084535},
volume = {156},
year = {2022}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions), so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique [eXtended Tight Binding based simplified Tamm-Dancoff approximation (xTB-sTDA)] against a higher accuracy one (time-dependent density functional theory). Testing the calibration model shows an approximately sixfold decrease in the error in-domain and an approximately threefold decrease in the out-of-domain. The resulting mean absolute error of ∼0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates that machine learning can be used to develop a cost-effective and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.
AU  - Verma,S
AU  - Rivera,M
AU  - Scanlon,DO
AU  - Walsh,A
DO  - 10.1063/5.0084535
EP  - 134116
PY  - 2022///
SN  - 0021-9606
SP  - 134116
TI  - Machine learned calibrations to high-throughput molecular excited state calculations.
T2  - Journal of Chemical Physics
UR  - http://dx.doi.org/10.1063/5.0084535
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35395896
UR  - https://aip.scitation.org/doi/10.1063/5.0084535
UR  - http://hdl.handle.net/10044/1/96365
VL  - 156
ER  -
Download