Machine-Learning Discovery of Metal-Organic Framework Crystals for Second-Harmonic Generation

Nonlinear optical (NLO) crystals are critical for frequency conversion, light generation, and nonlinear photonics. Among inorganic and organic NLO crystals, hybrid metal-organic frameworks (MOFs) are emerging as promising crystalline alternatives owing to their high polarizability, endurance, and structural tunability. However, the latter leads to enormous compositional and structural diversity among MOFs (>130,000 reported structures), making the rational discovery of high-performance NLO MOFs challenging using experimental and computational approaches. Here, we report a unified dataset of 2327 crystals: 937 non-centrosymmetric (including 67 experimentally validated NLO MOFs and 870 inorganic NLO crystals) and 1390 centrosymmetric crystals as a reference. Based on this dataset, we develop a machine-learning (ML) model for screening MOFs and identifying promising non-centrosymmetric NLO candidates for second-harmonic generation (SHG). The model predicts SHG efficiencies with an overall accuracy of 2.05 pm V-1 (mean absolute value), and 0.39 pm V-1 for the experimental MOF dataset, and ranks non-centrosymmetric crystals with a Spearman correlation of 0.66. Screening is 105 times faster than DFT, positioning the model as a ranking filter for prioritizing candidates. This work establishes a data-driven ML model for accelerating the discovery of next-generation NLO MOF crystals and expanding the design space for advanced nonlinear photonics.