Interpretable Machine Learning of Two-Photon Absorption

Molecules with strong two-photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC) calculations. Here we perform a systematic study by collecting and analyzing with interpretable machine learning (ML) experimental TPA database with ca. 900 molecules. We uncovered that only very few molecular features are sufficient to explain the TPA magnitudes. The most important feature is conjugation length (rather than area as believed before) followed by features reflecting effects of donor and acceptor substitution and coplanarity. These features are used to create a very fast ML model with prediction errors of similar magnitude compared to experimental and affordable QC meth-ods errors. Our ML model has the potential for high-throughput screening as additionally validated with our new experimental measurements.