Unlocking structure–property relationships in organic dyes with machine learning and statistics

Designing organic dyes with precise spectral properties remains challenging despite their importance in downstream industries. This work introduces a machine learning framework (MMoE‐CV, with MAEs <8 nm for absorption and <13 nm for emission) integrated with statistical analysis to uncover interpre‐ structure–property relationships. Experimental validation with newly synthesized thiadiazole derivatives confirms the model's high accuracy even for de novo compounds. Analysis of a library of 729 dye derivatives demonstrates that substituent effects are strongly modulated by both the parent chromophore scaffold and substitution position. This nuance is often overlooked in traditional design approaches. Statistical analysis reveals quantitative insights into these complex interactions, providing a novel rule framework for dye optimization. This approach bridges predictive power with chemical understanding, accelerating the discovery of functional organic dyes for applications in various areas and offering a new perspective on the integration of artificial intelligence in materials design and industrial implementation.