우리 연구실 신다은 연구원의 첫 논문이자, 우리 연구실 대학원생의 첫 주저자 연구논문이 온라인 출판되었습니다! 특히 Journal of Materials Chemistry의 Back Cover로 선정되어 곧 업데이트될 예정입니다.
본 연구는 서강대학교 백서인교수 연구팀과 공동으로 진행되었습니다.
축하합니다!
Mok, D. H.†, Shin, D.†, Na, J.*, & Back, S.* (2023). Chemically Inspired Convolutional Neural Network using Electronic Structure Representation. Journal of Materials Chemistry A, accepted. [Link]
[Highlights on Back Cover Article]
Daeun’s first paper “A chemically inspired convolutional neural network using electronic structure representation” has been selected as back cover in Journal of Materials Chemistry A.
Congratulation!
In recent years, the 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 the 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 the 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 an appropriate input for property prediction and the attention module is capable of properly featurizing DOS signals by capturing the contributions of each spin and orbital state. In addition, we statistically evaluated the stability screening performance of ESNet, measuring the 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.
