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

Research Area

Author

  • Raku Shirasawa, Ichiro Takemura, Shinnosuke Hattori, Yuuya Nagata*

Company

  • Sony Group Corporation

Venue

  • Communications Chemistry

Date

  • 2022

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

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Abstract

Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted ‘good solvents’ with an accuracy >0.8. Overall, we confirmed the effectivity of the proposed semi-automated scheme in early-stage material search projects for accelerating a wider range of material research.

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