Issue 20, 2024

Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks

Abstract

In modern laser science, nonlinear optical crystals play a crucial role as indispensable functional materials. Overcoming the inherent defects of traditional materials such as two-photon absorption and low laser damage thresholds, the search for high-performance nonlinear optical crystals remains an urgent and unresolved challenge. In this study, we extensively utilized deep learning techniques to construct a lightweight convolutional neural network. This network is built on the basis of information about the nuclear charge of elements and the number of atoms in compounds, creating carefully designed feature maps for accurately predicting the formation energy of nonlinear optical crystals. Our model demonstrates outstanding performance, with predictive metrics showing R2 = 0.985, RMSE = 0.128, and MAE = 0.083. This remarkable predictive capability enables our network to efficiently predict the formation energy of crystals and reliably assess their stability. Our approach is poised to play a crucial role in high-throughput screening, significantly reducing computational burdens and accelerating the discovery process of novel crystals. This significant achievement provides an efficient and reliable tool for crystal material research, opening up new possibilities for future materials science investigations.

Graphical abstract: Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks

Supplementary files

Article information

Article type
Paper
Submitted
12 fev 2024
Accepted
17 apr 2024
First published
03 may 2024

CrystEngComm, 2024,26, 2652-2661

Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks

Z. Fan, S. Lian, G. Jin, C. Xin, Y. Li and B. Yuan, CrystEngComm, 2024, 26, 2652 DOI: 10.1039/D4CE00133H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements