Rapid and complete prediction of Madder natural dyes’ color and properties through computational method

Experimental chemists traditionally hold experimentation in high regard, but computational chemistry has the potential to revolutionize this perspective. Our work exemplifies this shift, employing computational methods such as Density Functional Theory (DFT), Time-Dependent Density Functional Theory (TD-DFT), and Quantum Mechanic/Molecular Mechanic (QM/MM) to delve into the realm of photodegradation of color. Focused on Madder dye, a natural and historical pigment renowned for its diverse color properties, we tackle challenges that traditional experimentation struggled to address. The historical difficulty in deciphering the spectroscopic properties of Madder's colorants due to extraction challenges, impurities, and high costs is overcome through computational spectroscopy techniques. By marrying computational insights with experimental data, we predict UV-Vis, color, and NMR spectra, considering factors like pH, solvent effects, and conformers. The study underscores the impact of solute-solvent interactions on reproducing experimental measurements, laying the foundation for a comprehensive database to understand color properties in cultural heritage. These initial findings pave the way for future exploration of complex systems like Madder lake. This transformative shift empowers researchers and scientists to conquer crucial challenges in comprehending the properties of ancient colors for cultural heritage preservation, advancing the development of durable bio-sourced colors, and discovering new color applications.