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Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview

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Ultraviolet–visible (UV–visible or UV–Vis) spectroscopy is an analytical technique that investigates the interaction between matter and UV–Vis light within the electromagnetic spectrum. This method is widely used for its versatility, simplicity, and relatively quick data acquisition, making it valuable for both qualitative and quantitative analysis. When UV–Vis radiation passes through a material, molecules absorb light depending on the energy required for...

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Updated: Aug 6, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

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Published on: April 28, 2022

Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning.

Mengfan Wu^1,2, Evgenii Tikhonov¹, Abudukadi Tudi^1,2

¹Research Center for Crystal Materials, CAS Key Laboratory of Functional Materials and Devices for Special Environments, Xinjiang Technical Institute of Physics & Chemistry, CAS, Xinjiang Key Laboratory of Electronic Information Materials and Devices, 40-1 South Beijing Road, Urumqi, 830011, China.

Advanced Materials (Deerfield Beach, Fla.)

|March 17, 2023

Summary

This summary is machine-generated.

A new framework accelerates the discovery of deep-ultraviolet nonlinear optical (NLO) materials using machine learning and high-throughput calculations. This approach efficiently identifies novel NLO materials for advanced laser technologies.

Keywords:

birefringence crystal structure prediction deep-UV nonlinear optical materials high-throughput calculations machine learning

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Area of Science:

Materials Science
Condensed Matter Physics
Computational Chemistry

Background:

Data-driven science is revolutionizing materials discovery.
Novel nonlinear optical (NLO) materials are crucial for deep-ultraviolet (UV) laser technologies.
Efficient methods are needed to discover NLO materials with deep-UV birefringent phase-matching capabilities.

Purpose of the Study:

To propose a target-driven materials design framework for accelerating the discovery of deep-UV NLO materials.
To develop a machine learning (ML) model for predicting birefringence based on crystal structures.
To identify stable NLO material candidates with potential applications in the deep-UV region.

Main Methods:

High-throughput calculations (HTC) to generate a dataset.
Crystal structure prediction.
Development of an interpretable ML regression model for birefringence prediction.
Efficient screening strategy based on ML-predicted birefringence.

Main Results:

A novel ML model accurately predicts birefringence using crystal structures as input.
The framework identified a list of potential chemical compositions for deep-UV NLO materials.
Eight stable material structures with promising NLO properties for the deep-UV region were discovered.

Conclusions:

The proposed design framework significantly accelerates the discovery of high-performance NLO materials.
This approach offers a low computational cost solution for exploring a broad chemical space.
The study provides new insights into the discovery of NLO materials and their structure-property relationships.