2024/04/16 更新

写真a

フジナミ　ミキト
藤波　美起登
所属
理工学術院 理工学術院総合研究所
職名
次席研究員（研究院講師）
学位
博士（理学） ( 2020年03月 早稲田大学 )
 

論文

  • Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction

    Mikito Fujinami, Hiroki Maekawara, Ryota Isshiki, Junji Seino, Junichiro Yamaguchi, Hiromi Nakai

    Bulletin of the Chemical Society of Japan   93 ( 7 ) 841 - 845  2020年07月

    DOI

    Scopus

    7
    被引用数
    (Scopus)

  • Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

    Mikito Fujinami, Ryo Kageyama, Junji Seino, Yasuhiro Ikabata, Hiromi Nakai

    Chemical Physics Letters   748   137358 - 137358  2020年06月

    DOI

    Scopus

    33
    被引用数
    (Scopus)

  • Quantum Chemical Reaction Prediction Method Based on Machine Learning

    Mikito Fujinami, Junji Seino, Hiromi Nakai

    Bulletin of the Chemical Society of Japan   93 ( 5 ) 685 - 693  2020年05月

    DOI

  • Semi-local machine-learned kinetic energy density functional demonstrating smooth potential energy curves

    Junji Seino, Ryo Kageyama, Mikito Fujinami, Yasuhiro Ikabata, Hiromi Nakai

    Chemical Physics Letters   734   136732 - 136732  2019年11月

    DOI

    Scopus

    29
    被引用数
    (Scopus)

  • Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables

    Mikito Fujinami, Junji Seino, Takumi Nukazawa, Shintaro Ishida, Takeaki Iwamoto, Hiromi Nakai

    Chemistry Letters   48 ( 8 ) 961 - 964  2019年08月

    DOI

    Scopus

    10
    被引用数
    (Scopus)

  • Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density

    Junji Seino, Ryo Kageyama, Mikito Fujinami, Yasuhiro Ikabata, Hiromi Nakai

    The Journal of Chemical Physics   148 ( 24 ) 241705 - 241705  2018年06月

    DOI

    Scopus

    63
    被引用数
    (Scopus)

  • Development of Reaction Prediction Scheme Based on Machine Learning with Quantum Chemical Descriptors

    Mikito FUJINAMI, Junji SEINO, Hiromi NAKAI

    Journal of Computer Chemistry, Japan   15 ( 3 ) 63 - 65  2016年

    DOI

▼全件表示

 

特定課題制度（学内資金）

  • 化学実験画像の物体検出のためのデータセットの構築

    2022年   中井 浩巳

    　概要を見る

        In this research, we aim to develop a system that automatically recognizes videos of chemical experiments using image recognition technology. The system is expected to be applied to create electronic laboratory notebooks and to warn against dangerous actions automatically. Image recognition using machine learning requires a dataset with coordinates and names of objects in images. We have constructed a dataset of about 2300 images and 5000 objects. However, further expansion of the dataset is necessary to improve the prediction accuracy. In this project, we expanded the chemical experiment image data set. Documentation was prepared so that anyone could create a dataset. The dataset was expanded with the research assistants' cooperation. We extended the dataset to about 5000 images and 16000 objects. The prediction accuracy of test data was evaluated. Mean Average Precision (mAP) was used as an evaluation index. The mAP takes values in the range from 0 to 1, and the closer to 1, the higher the prediction performance. Learning on the expanded dataset improved mAP to above 0.8. This study was reported in two conference presentations, including an international conference.