A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

Abstract

Prediction of material properties is crucial for early stages of material research, but current experimental data-based strategies possess limited accuracy. Here, the authors develop a machine learning-based semi-automated material exploration scheme to predict the solubility of tetraphenylporphyrin derivatives with an accuracy above 0.8.