Machine learning modeling of electronic spectra and thermodynamic stability for a comprehensive chemical space of melanin

Abstract

Melanin as a bio-optoelectronic material holds immense potential. However, the understanding of its exact molecular structure has been stalling for decades due to difficulties in experiments, which hinders uncovering its structure–property relationship. Conventional theoretical modeling is also limited due to the huge size of its chemical space resulting from millions of possible oligomer structures. Here, we design a comprehensive virtual chemical space of melanin oligomers and develop a machine learning-based approach for predicting their entire UV-visible spectra and thermodynamic stability using fingerprint input. We also show the similarity of our predicted Boltzmann-weighted average spectrum with the experimental spectrum and discuss their potentiality towards bio-optoelectronics.