Acquisition of absorption and fluorescence spectral data using chatbots†

The field of photochemistry underpins broad scientific endeavors, encompasses diverse molecular substances, and incorporates descriptions of qualitative and quantitative properties, all of which together may be representative of many scientific disciplines. Yet finding absorption and fluorescence spectra along with companion values of the molar absorption coefficient (ε) and fluorescence quantum yield (Φ_f) for a given compound is an arduous task even with the most advanced search methods. To gauge whether chatbots could be used to reliably search the literature, the absorption and fluorescence spectra and quantitative parameters (ε and Φ_f) for 16 popular dyes and fluorophores were sought using ChatGPT 3.5, ChatGPT 4o, Microsoft Copilot, Google Gemini, Gemini advanced, and Meta AI. In most cases, the values of ε and Φ_f returned by the chatbots accurately cohered with known values from established resources, whereas the retrieval of spectra was only marginally successful. The chatbots were further challenged to find data for fictive compounds (e.g., rhodamine 7G). The results from each chatbot were categorized as follows: “fabricated” (provides numbers that do not exist in the context queried), “fooled” (mis-identifies the compound but does not return any data), “feigned” (acts as if the fictive compound is real but does not provide any data), or “faithful” (responds that the compound is not known or is not available). In summary, the present shortcomings should not cloud the view that chatbots – judiciously used – already provide a valuable resource for the challenging scientific task of finding granular data, and to lesser degree, spectral traces for known compounds.