WeChat Mini Program
Old Version Features

Effectively Improving the Accuracy of PBE Functional in Calculating the Solid Band Gap Via Machine Learning

COMPUTATIONAL MATERIALS SCIENCE(2021)

China Univ Min & Technol

Cited 43|Views1
Abstract
Band gap is one of the most important parameters determining the electronic, optoelectronic, and other applications of a wide range of materials including semiconductors and insulators. However, the accurate prediction of the band gap of these materials has been a persistent difficulty in quantum chemistry. Numerous studies have attempted to improve the accuracy of the predicted band gap from standard density functional theory (DFT) calculations with local density approximation (LDA) and generalized gradient approximation (GGA), which are well-known to underestimate the band gap severely. With the rapid development of material databases from both experimental and theoretical studies, herein, we develop a correction model to improve the prediction accuracy for the band gap by combing the widely used Perdew-Burke-Ernzerh (PBE-GGA) functional with machine learning approach. The correction model introduces physically meaningful but computationally efficient descriptors to fit the experimental dataset, and an artificial neural network (ANN) model is established to improve the prediction accuracy of computational results from DFT-PBE functional. The new method brings a highly accurate model for the prediction of the band gaps at high-precision G0W0 level without increasing computational cost at DFT-PBE level. Further, the error distribution of the predicted band gaps is more in line with the normal distribution compared with DFT-PBE and G0W0 methods. The band gap correction model provides a practical way to obtain GW-like quality results from standard DFT calculations, and should be valuable to perform accurate high-throughput screening of semiconductors and insulators for which GW calculations become unfeasible.
More
Translated text
Key words
Band gap,PBE functional,Machine learning,Artificial neural network
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined