npsm 새물리 New Physics : Sae Mulli

pISSN 0374-4914 eISSN 2289-0041
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Article

Research Paper

New Phys.: Sae Mulli 2020; 70: 920-927

Published online November 30, 2020 https://doi.org/10.3938/NPSM.70.920

Copyright © New Physics: Sae Mulli.

Deep Learning Applied to Peak Fitting of Spectroscopic Data in Frequency Domain

Hyeong Seon PARK, Seong-Heum PARK, Hyunbok LEE, Heung-Sik KIM* 

Department of Physics, Kangwon National University, Chuncheon 24341, Korea

Correspondence to:heungsikim@kangwon.ac.kr

Received: August 31, 2020; Revised: September 17, 2020; Accepted: September 21, 2020

Abstract

A data-driven study of material properties and functional materials design based on it requires high-throughput and comparative analyses of the results of experimental spectroscopy with those from first-principles electronic structure calculations. Hence, an efficient machine-learning-based computational tool to extract electronic structure information from experimental data without human intervention is in high demand. Here, we test the capability of deep neural network models to fit photoemission spectroscopy (PES) data in the frequency domain with unknown PES peak positions, numbers, and widths. A one-dimensional convolution neural network (CNN) was employed in combination with fully connected layers (FCL), and the trained model was applied to photoemission spectra for the sulfur $2p$ states in poly(3-hexylthiophene) (P3HT) molecules and oxygen $1s$ states in indium tin oxide (ITO). We conclude by further discussing potential ways to improve the performance of the model.

Keywords: Photoemission spectroscopy, Machine learning, Deep neural network

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