Learning excited states from ground states by using an artificial neural network

Shin Kiyohara,Teruyasu Mizoguchi,Masashi Tsubaki

doi:10.1038/s41524-020-0336-3

Shin Kiyohara, Teruyasu Mizoguchi + Show 1 more

Open Access

https://doi.org/10.1038/s41524-020-0336-3

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Abstract

Excited states are different quantum states from their ground states, and spectroscopy methods that can assess excited states are widely used in materials characterization. Understanding the spectra reflecting excited states is thus of great importance for materials science. However, understanding such spectra remains difficult because excited states have usually different atomic or electronic configurations from their corresponding ground states. If excited states could be predicted from ground states, the knowledge of the excited states would be improved. Here, we used an artificial neural network to predict the excited states of the core-electron absorption spectra from their ground states. Consequently, our model correctly learned and predicted the excited states from their ground states, providing several thousand times computational efficiency. Furthermore, it showed excellent transferability to other materials. Also, we found two physical insights about excited states: core-hole effects of amorphous silicon oxides are stronger than those of crystalline silicon oxides, and the excited-ground states relationships of some metal oxides are similar to those of the silicon oxides, which could not be obtained by conventional spectral simulation nor found until using machine leaning.

Highlights

Excited states are quantum states of an atom or electron with higher energy than its ground states
While spectroscopy is indispensable for analyzing the atomic and electronic structures of materials, understanding spectra that reflect excited states is always difficult because excited states usually have atomic or electronic configurations that are different from their corresponding ground states
Among spectroscopy methods that assess excited states, we focused on the coreelectron excited states, namely electronenergy-loss near-edge structure (ELNES)/X-ray absorption near-edge structure (XANES) spectra, and designed an artificial neural network (ANN) model to predict the fine ELNES/XANES profiles of crystalline materials from their ground-state density of states

Summary

Introduction

Excited states are quantum states of an atom or electron with higher energy than its ground states. As the fine profiles of these excited staterelated spectra reflect interactions with external fields as well as atomic configurations and chemical bonding, spectroscopy methods that assess excited states have been extensively used in materials characterization[1,2,3,4,5]. While spectroscopy is indispensable for analyzing the atomic and electronic structures of materials, understanding spectra that reflect excited states is always difficult because excited states usually have atomic or electronic configurations that are different from their corresponding ground states. Combining ELNES/ XANES with the simulation can analyze local atomic and electronic structures, but doing so is not straightforward because such simulations must treat both the ground and final states, and often must consider two-particle and multi-particle interactions[17], which leads to several hundred or thousand times computational costs than ground-state only calculation. If excited states could be predicted from ground states, the knowledge of the excited states and the understanding of spectral features would both be improved

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