Index Catalog // MDR

Automatic Experimental Data Collection System Using a Wireless LAN Capable SD Card as an IoT Device

Description/Abstract:: 実験・計算・データ科学を融合させた材料の研究開発を推進するマテリアルズインフォマティクス(MI)が推進されているが、実験データの収集については既存データベースのインポートや出版済み論文のデータマイニングに限られる場合が多い。効果的な MI のためには各種装置から自動的に実験...
Keyword:: FlashAir, IoT, Wi-Fi, and data transfer
Resource Type:: Presentation
Author:: MATSUDA, Asahiko, YOSHIKAWA, Hideki, and CHIKYOW, Toyohiro
Date Uploaded:: 23/10/2020
Date Modified:: 01/07/2021

Materials metadata: as a custom schema, as directories, or in a data package

Description/Abstract:: Metadata for a research dataset can be about the dataset's bibliographic properties as well as their scientific properties. The scientifi...
Keyword:: data platform, materials informatics, and metadata
Resource Type:: Poster
Author:: MATSUDA, Asahiko, TANABE, Kosuke, KADOHIRA, Takuya, and TANIFUJI, Mikiko
Date Uploaded:: 23/10/2020
Date Modified:: 01/07/2021

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning

Description/Abstract:: Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental d...
Keyword:: Machine learning, active learning, and adhesive
Resource Type:: Article
Author:: Pruksawan, Sirawit, Lambard, Guillaume, Samitsu, Sadaki, Sodeyama, Keitaro, and Naito, Masanobu
Journal:: Science and Technology of Advanced Materials
Date Uploaded:: 02/01/2021
Date Modified:: 18/10/2022

Microbubble flows in superwettable fluidic channels

Description/Abstract:: The control of bubble adhesion underwater is important for various applications, yet the dynamics under flow conditions are still to be u...
Keyword:: microbubble and superwettability
Resource Type:: Article
Author:: Doi, Kotaro, Naito, Masanobu, and Tenjimbayashi, Mizuki
Journal:: RSC Advances
Date Uploaded:: 02/01/2021
Date Modified:: 18/10/2022

Leveraging Segmentation of Physical Units through a Newly Open Source Corpus

Description/Abstract:: The identification of physical measurements is a recurrent task in material informatics (MI). Quantities and units are contained in unstr...
Keyword:: measurements, physical quantities, quantities, and segmentation
Resource Type:: Presentation
Author:: DIEB M. Thear, SUZUKI, Akira, ISHII, Masashi, FOPPIANO, Luca, and TANIFUJI, Mikiko
Date Uploaded:: 18/10/2019
Date Modified:: 15/06/2020

Leveraging Segmentation of Physical Units through a Newly Open Source Corpus

Description/Abstract:: The identification of physical measurements is a recurrent need in materials informatics (MI). For example, the extraction of superconduc...
Resource Type:: Dataset
Data origin:: informatics and data science
Author:: ISHII, Masashi, SUZUKI, Akira, DIEB M. Thear, FOPPIANO, Luca, and TANIFUJI, Mikiko
Date Uploaded:: 25/10/2019
Date Modified:: 15/06/2020

Machine Learning-Based Experimental Design in Materials Science

Description/Abstract:: In materials design and discovery processes, optimal experimental design (OED) algorithms are getting more popular. OED is often modeled ...
Keyword:: Machine learning, Materials design, and Optimal experiment design
Resource Type:: Part of Book
Author:: Tsuda, Koji and Dieb, Thaer M.
Journal:: Nanoinformatics
Date Uploaded:: 02/10/2020
Date Modified:: 16/10/2020

SuperMat: Construction of a linked annotated dataset from superconductors-related publications

Description/Abstract:: A growing number of papers are published in the area of superconducting materials science. However, novel text and data mining (TDM) pro...
Keyword:: annotation, annotation guidelines, dataset, superconductors, and tdm
Resource Type:: Article
Author:: FOPPIANO, Luca, DIEB, Sae, SUZUKI, Akira, BAPTISTA DE CASTRO, Pedro, IWASAKI, Suguru, UZUKI, Azusa, ESPARZA ECHEVARRIA, Miren Garbine, MENG, Yan, TERASHIMA, Kensei, TAKANO, Yoshihiko, and ISHII, Masashi
Date Uploaded:: 07/01/2021
Date Modified:: 01/07/2021

Structure prediction of boron-doped graphene by machine learning

Description/Abstract:: Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure de...
Keyword:: Materials design and Monte Carlo tree search
Resource Type:: Article
Author:: Dieb, Thaer M., Hou, Zhufeng, and Tsuda, Koji
Journal:: The Journal of Chemical Physics
Date Uploaded:: 02/10/2020
Date Modified:: 16/10/2020

MDTS: automatic complex materials design using Monte Carlo tree search

Description/Abstract:: Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python...
Keyword:: Monte Carlo tree search and materials design
Resource Type:: Article
Author:: Dieb, Thaer M., Tsuda, Koji, Shiomi, Junichiro, Yoshizoe, Kazuki, Ju, Shenghong, and Hou, Zhufeng
Journal:: Science and Technology of Advanced Materials
Date Uploaded:: 02/10/2020
Date Modified:: 16/10/2020

Automatic Experimental Data Collection System Using a Wireless LAN Capable SD Card as an IoT Device

Materials metadata: as a custom schema, as directories, or in a data package

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning

Microbubble flows in superwettable fluidic channels

Leveraging Segmentation of Physical Units through a Newly Open Source Corpus

Leveraging Segmentation of Physical Units through a Newly Open Source Corpus

Machine Learning-Based Experimental Design in Materials Science

SuperMat: Construction of a linked annotated dataset from superconductors-related publications

Structure prediction of boron-doped graphene by machine learning

MDTS: automatic complex materials design using Monte Carlo tree search

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