Understanding Colour Tuning Rules and Predicting Absorption Wavelengths of Microbial Rhodopsins by Data-Driven Machine-Learning Approach by Masayuki Karasuyama, Keiichi Inoue, Ryoko Nakamura, Hideki Kandori, Ichiro Takeuchi

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Deep learning based emulator for predicting voltage behaviour in lithium ion batteries by Kanato Oka, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Syuto Noguchi, Masayuki Karasuyama, Yoshiya Fujiwara, Takuhiro Miyuki

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Drawing a materials map with an autoencoder for lithium ionic conductors by Yudai Yamaguchi, Taruto Atsumi, Kenta Kanamori, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ichiro Takeuchi

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Prediction of Li-ion conductivity in Ca and Si co-doped LiZr2(PO4)3 using a denoising autoencoder for experimental data by Yumika Yokoyama, Shuto Noguchi, Kazuki Ishikawa, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Ryo Kobayashi, Masayuki Karasuyama

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Optimization of force-field potential parameters using conditional variational autoencoder by Koki Matsunoshita, Yudai Yamaguchi, Masato Hamaie, Motoki Horibe, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ryo Kobayashi

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Author Correction: Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design by Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi

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Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design by Keiichi Inoue, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi

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