Explainable Artificial Intelligence Approach to Identify the Origin of Phonon‐Assisted Emission in WSe2 Monolayer

Author:

Yoo Jaekak12,Cho Youngwoo3,Jeong Byeonggeun1,Choi Soo Ho1,Kim Ki Kang1,Lim Seong Chu14,Lee Seung Mi2,Choo Jaegul3,Jeong Mun Seok56ORCID

Affiliation:

1. Department of Energy Science Sungkyunkwan University Suwon 16419 Republic of Korea

2. Korea Research Institute of Standards and Science Daejeon 34113 Republic of Korea

3. Kim Jaechul Graduate School of Artificial Intelligence Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea

4. Department of Smart Fabrication Technology Sungkyunkwan University Suwon 16419 Republic of Korea

5. Department of Physics Hanyang University Seoul 04763 Republic of Korea

6. SMC Lab. Inc. Seoul 04763 Republic of Korea

Abstract

The application of explainable artificial intelligence in nanomaterial research has emerged in the past few years, which has facilitated the discovery of novel physical findings. However, a fundamental question arises concerning the physical insights presented by deep neural networks; the model interpretation results have not been carefully evaluated. Herein, explainable artificial intelligence and quantum mechanical calculations is bridged to investigate the correlation between light scattering and emission in a WSe2 monolayer. Convolutional neural networks using light scattering and emission data are first trained, while expecting the networks to determine the relationships between them. The trained models are interpreted and the specific phonon contribution during the exciton relaxation process is derived. Finally, the findings are independently evaluated through quantum mechanical calculations, such as the Born–Oppenheimer molecular dynamics simulation and density functional perturbation theory. The study provides reliable fundamental physical insight by evaluating the results of neural networks and suggests a novel methodology that can be applied in materials science.

Funder

Ministry of Science and ICT, South Korea

Air Force Office of Scientific Research

Ministry of Education

Publisher

Wiley

Subject

General Medicine

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