File:Fig3 Hatakeyama-Sato njpCompMat22 8.png

Fig. 3 Automated feature analysis. a Conversion of process and measurement data into a numerical array. A full ML scheme is shown in Supplementary information, Supplementary Fig. 2. b Fingerprint generation from flowcharts. A binary array expresses specific experimental steps (e.g., in the figure, ‘Protocol A’ has steps a, b, c, and not c’ or d: this yields a fingerprint of 11100). ‘Cool’ steps in Protocol A and B are distinguished by c or c’ because only the latter is connected to ‘Stir’. Then, BERT and kNN automatically group similar steps (e.g., ‘Heat’ and ‘Hot plate’ can be categorized in the same group). c Prediction of ionic conductivity (σ_ion) using LightGBM regressor with statistically essential electrolyte parameters extracted by Boruta. d SHAP values during the prediction (explanations of parameters are shown in Supplementary information. Supplementary Table 2). e Causal relations estimated by unsupervised ML.

Hatakeyama-Sato, K.; Umeki, M.; Adachi, H.; Kuwata, N.; Hasegawa, G.; Oyaizu, K. (2022). "Exploration of organic superionic glassy conductors by process and materials informatics with lossless graph database". npj Computational Materials 8: 170. doi:10.1038/s41524-022-00853-0. 

2022

Hatakeyama-Sato, K.; Umeki, M.; Adachi, H.; Kuwata, N.; Hasegawa, G.; Oyaizu, K.

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