High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification

ABSTRACT Infrared nonlinear optical (NLO) materials are essential for laser and photonic technologies, limited by fragmented material systems, lengthy development cycles, and trial‐and‐error synthesis. To overcome these barriers, we developed an integrated computational‐experimental framework integrating first‐principles high‐throughput calculations, machine learning, and targeted synthesis. We establish a multidimensional properties dataset of 1807 non‐centrosymmetric compounds and define a comprehensive figure of merit (CFOM) Q based on the statistical average of this dataset to quantify performance trade‐offs. Multidimensional statistical analysis uncovers composition–structure–performance relationships, and reveals superior structure and chemical compositions governing enhanced NLO performance. A Q ‐based crystal graph neural network classifier is developed, achieving strong predictive accuracy (AUC = 0.95). We identify 12 unreported candidates ( Q > 2) from 5105 compounds combining high‐throughput calculation and machine learning. Experiments confirm that defect‐chalcopyrite HgAl 2 Q 4 ( Q = S, Se, Te) shows wide band gaps (1. 55–2.82 eV), suitable birefringence (0.06–0.08), and strong NLO responses (2.2–5 × AGS). This work provides an effective pathway for accelerating the discovery of high‐performance optoelectronic materials.