Chemically Inspired Convolutional Neural Network using Electronic Structure Representation

30 January 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In recent years, a development of appropriate crystal representations for accurate prediction of inorganic crystal properties has been considered as one of the essential tasks to accelerate materials discovery through high-throughput virtual screening (HTVS). However, many of them were developed aiming to predict properties of the given structures, although property predictions of ground state structures using unrelaxed structures as inputs are much more important in practical HTVS. To tackle this challenge, we develop a chemically inspired convolutional neural network based on convolution block attention modules using density of states of unrelaxed initial structures (IS-DOS) as inputs. Our model, Electronic Structure Network (ESNet), achieved the highest accuracy for predicting formation energy, proving that IS-DOS is appropriate input for the property prediction and the attention module is capable of properly featurizing DOS signals by capturing contributions of each spin and orbital state. In addition, we statistically evaluated a stability screening performance of ESNet, measuring computational cost and capability of materials discovery simultaneously. We found that ESNet outperformed previously reported models and various models with different types of input features and architectures. Indeed, ESNet successfully discovered 926 stable materials from 15,318 unrelaxed structures with 82 % reduced computational cost compared to the complete DFT validation.


High-throughput screening
Thermodynamic stability
Density of states
Machine learning
Convolutional neural network

Supplementary materials

Supporting Information for “Chemically Inspired Convolutional Neural Network using Electronic Structure Representation”
Supplementary Figure 1 – 8, Supplementary Table 1 – 2


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.