Nature - Communications Chemistry
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.
For details:
A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives
Raku Shirasawa 1, Ichiro Takemura 2, Shinnosuke Hattori 1 & Yuuya Nagata 3
1. Advanced Research Laboratory, R&D Center, Sony Group Corporation, Atsugi Tec. 4-14-1 Asahi-cho, Atsugi-shi, Kanagawa, 243-0014, Japan
2. Tokyo Laboratory 26, R&D Center, Sony Group Corporation, Atsugi Tec. 4-14-1 Asahi-cho, Atsugi-shi, Kanagawa, 243-0014, Japan
3. Institute for Chemical Reaction Design and Discovery, Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido, 001-0021, Japan
Communications Chemistry
Volume 5, article number: 158 (2022)
https://doi.org/10.1038/s42004-022-00770-9
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