Mikito Fujinami - Research output - Waseda University

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Websitehttps://w-rdb.waseda.jp/html/100002084_en.html

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Research activity per year

8 Article
1 Comment/debate

9 results

2024
Application and integration of computer vision technologies for automated recognition and recording of chemical experiments
Sasaki, R., Fujinami, M. & Nakai, H., 2024 Nov, In: Bulletin of the Chemical Society of Japan. 97, 11, uoae110.
Research output: Contribution to journal › Article › peer-review

Open Access
Comprehensive image dataset for enhancing object detection in chemical experiments
Sasaki, R., Fujinami, M. & Nakai, H., 2024 Feb, In: Data in Brief. 52, 110054.
Research output: Contribution to journal › Article › peer-review

Open Access
5 Citations (Scopus)
2020
Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential
Fujinami, M., Kageyama, R., Seino, J., Ikabata, Y. & Nakai, H., 2020 Jun, In: Chemical Physics Letters. 748, 137358.
Research output: Contribution to journal › Article › peer-review

Open Access
39 Citations (Scopus)
Quantum chemical reaction prediction method based on machine learning
Fujinami, M., Seino, J. & Nakai, H., 2020, In: Bulletin of the Chemical Society of Japan. 93, 5, p. 685-693 9 p.
Research output: Contribution to journal › Article › peer-review
20 Citations (Scopus)
Solvent selection scheme using machine learning based on physicochemical description of solvent molecules: Application to cyclic organometallic reaction
Fujinami, M., Maekawara, H., Isshiki, R., Seino, J., Yamaguchi, J. & Nakai, H., 2020 Jul, In: Bulletin of the Chemical Society of Japan. 93, 7, p. 841-845 5 p.
Research output: Contribution to journal › Article › peer-review
7 Citations (Scopus)
2019
Semi-local machine-learned kinetic energy density functional demonstrating smooth potential energy curves
Seino, J., Kageyama, R., Fujinami, M., Ikabata, Y. & Nakai, H., 2019 Nov, In: Chemical Physics Letters. 734, 136732.
Research output: Contribution to journal › Article › peer-review

Open Access
36 Citations (Scopus)
Virtual reaction condition optimization based on machine learning for a small number of experiments in high-dimensional continuous and discrete variables
Fujinami, M., Seino, J., Nukazawa, T., Ishida, S., Iwamoto, T. & Nakai, H., 2019, In: Chemistry Letters. 48, 8, p. 961-964 4 p.
Research output: Contribution to journal › Article › peer-review
12 Citations (Scopus)
2018
Erratum: Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density (Journal of Chemical Physics (2018) 148 (241705) DOI: 10.1063/1.5007230)
Seino, J., Kageyama, R., Fujinami, M., Ikabata, Y. & Nakai, H., 2018 Aug 21, In: Journal of Chemical Physics. 149, 7, 079901.
Research output: Contribution to journal › Comment/debate › peer-review

Open Access
Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
Seino, J., Kageyama, R., Fujinami, M., Ikabata, Y. & Nakai, H., 2018 Jun 28, In: Journal of Chemical Physics. 148, 24, 241705.
Research output: Contribution to journal › Article › peer-review
69 Citations (Scopus)