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$On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature$

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High-Performance Infrared Nonlinear Optical Crystals Discovery Guided by High-Throughput Computation, Machine

Yan Xiao¹, Zhaoxi Yu², Yumiao Niu¹

¹College of Chemistry and Materials Science, Institute of Life Science and Green Development, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Chemical Biology Key Laboratory of Hebei Province, State Key Laboratory of New Pharmaceutical Preparations and Excipients, Hebei University, Baoding, China.

Angewandte Chemie (International Ed. in English)

|April 25, 2026

Summary

This summary is machine-generated.

Researchers developed a computational-experimental framework to accelerate the discovery of infrared nonlinear optical (NLO) materials. This approach integrates high-throughput calculations and machine learning to identify promising new compounds for photonic technologies.

Keywords:

high‐throughput calculation infrared nonlinear optical materials machine learning second harmonic generation

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Area of Science:

Materials Science
Optoelectronics
Computational Chemistry

Background:

Infrared nonlinear optical (NLO) materials are crucial for advanced laser and photonic technologies.
Current material discovery is hindered by fragmented systems, long development times, and trial-and-error methods.

Purpose of the Study:

To develop an integrated computational-experimental framework for accelerating the discovery of novel infrared NLO materials.
To establish a comprehensive figure of merit (CFOM) for quantifying NLO material performance trade-offs.

Main Methods:

Utilized first-principles high-throughput calculations and machine learning.
Created a multidimensional dataset of 1807 non-centrosymmetric compounds.
Developed a crystal graph neural network classifier with high predictive accuracy (AUC = 0.95).

Main Results:

Uncovered key composition-structure-performance relationships for enhanced NLO properties.
Identified 12 promising, previously unreported NLO material candidates.
Experimentally validated HgAl2Q4 (Q=S, Se, Te) exhibiting wide band gaps and strong NLO responses.

Conclusions:

The integrated framework significantly accelerates the discovery of high-performance optoelectronic materials.
This approach provides a pathway to overcome limitations in traditional NLO material development.
Identified novel defect-chalcopyrite compounds with significant potential for photonic applications.