NIMS WEEK 2019

学術ポスターセッション

2019年は「AI＋DATA発！材料イノベーション」

NIMS Award受賞対象領域の研究に携わる若手研究者を中心に、機関の枠を超えて、活発なポスターセッションを行います。優秀なポスター発表にはポスター賞が贈呈されます。

テーマ

AI＋DATA発！材料イノベーション

発表カテゴリー

I. Data-driven Research in Functional Materials
II. Data-driven Research in Structural Materials
III. Materials Data Infrastructure including Measurements

展示時間

2019年10月30日（水）10:00～18:00

（コアタイム）

(1) 11:50～13:30（ランチタイム）
(2) 15:30～16:15（コーヒーブレイク）

展示場所

東京国際フォーラム　ホールB5

ポスター言語

英語

ポスター会場マップ
ポスター発表一覧
ポスター発表者の方へ

＊ポスター発表者の募集は締め切りました。

ポスター会場マップ

ポスター発表一覧

I. Data-driven Research in Functional Materials (I-01~23)

I-01	Efficient recommendation tool of materials on Windows computers	Dr. Ryo Tamura NIMS
I-02	Data-Driven Materials Discovery of Indirect Band-Gap Semiconductors for Photovoltaics	Dr. Jiwon Park Materials Data Center, Korea Institute of Materials Science, Republic of Korea
I-03	Machine Learning Model for Predicting Dielectric Constants of Oxides	Mr. Jun Miyamoto Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology
I-04	Compositional descriptor-based prediction for the chemical compound stability	Mr. Taruto Atsumi Nagoya Institute of Technology
I-05	Experimental search for NASICON-type solid electrolytes with high Li-ion conductivity guided by Bayesian Optimization	Ms. Maho Harada Nagoya Institute of Technology
I-06	Machine–Learning–Assisted for Exploration of Synthesis Condition for Metastable Novel Lanthanide Metal–Organic Frameworks Using Failed Experiments	Ms. Yu Kitamura Graduate School of Science and Technology, Kwansei Gakuin University
I-07	Machine–Learning–Assisted Synthesis of Silver Thiolate Metal Organic Frameworks	Mr. Takuma Wakiya Graduate School of Science and Technology, Kwansei Gakuin University
I-08	Predicting Chemical and Electronic Properties Using a Novel Descriptor “Elemental Fingerprints set” with Neural Networks	Mr. Jaekyun Hwang The University of Tokyo
I-09	Design and Optimization of Epoxy Adhesives through Active Learning from a Small Dataset	Mr. Sirawit Pruksawan NIMS, Program in Materials Science and Engineering, University of Tsukuba
I-10	Automatic Extraction Framework of Superconductors Related Information from Scientific Literature	Mr. Luca Foppiano NIMS
I-11	Data-Driven Design of Thermoelectric Materials with Experimental Data on Starrydata	Dr. Masaya Kumagai Center for Advanced Intelligence Project, RIKEN
I-12	Starrydata: a web system for experimental data extracted from published plot images	Dr. Yukari Katsura The University of Tokyo
I-13	Active learning: A Booster for Discovering New Materials from Small Datasets	Dr. Guillaume Lambard NIMS
I-14	Accelerating metamaterials design by quantum annealing	Mr. Koki Kitai Graduate School of Frontier Sciences, The University of Tokyo
I-15	Automation of the first-principles calculation and machine learning to search functional materials of high entropy alloys	Dr. Hiori Kino NIMS
I-16	Orbital Field Matrix: A New Descriptor for Materials	Dr. Takashi Miyake AIST
I-17	Subgroup Relevance Analysis: A Scheme for Selecting Important Descriptor Groups	Dr. Takashi Miyake AIST
I-18	High-throughput production of force-fields for Li-ion battery materials	Dr. Ryo Kobayashi Nagoya Institute of Technology, NIMS (CMI2, MaDIS)
I-19	Data-driven Exploration of Ion Conductive Alkali Metal Oxide for Batteries Using Materials Simulation	Mr. Haein Lee Nagoya Institute of Technology
I-20	Active learning for level set estimation under input uncertainty	Dr. Yu Inatsu RIKEN Center for Advanced Intelligence Project
I-21	Topological Data Analysis toward Design of Functional Materials	Dr. Kazuto Akagi AIMR, Tohoku University
I-22	Leveraging Legacy Data in Materials Science via Preference Learning	Ms. Xiaolin Sun The university of Tokyo
I-23	Metabolite Identification with de novo Molecule Generation	Mr. Jinzhe Zhang Tsuda Lab, Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, Utokyo

II. Data-driven Research in Structural Materials (II-01~28)

II-01	Data Driven Research for Composite Materials (Structural and Mechanical Properties of PAN- and Pitch-based Carbon Fibers)	Dr. Kimiyoshi Niato NIMS
II-02	Experimental evaluation on the environment resistance and fracture characteristics of multi-functional CFRP	Dr. Hiroyuki Oguma NIMS
II-03	Structure and mechanical properties of carbon fibers and polymers: Nanoindentation tests and molecular dynamics simulation study	Dr. Keiichi Shirasu NIMS
II-04	Constructing crosslinked epoxy resins and investigation of mechanical properties with molecular dynamics simulations	Dr. Yinbo Zhao Department of Aerospace Engineering, Tohoku University
II-05	Field theoretical approach to polymer phase separation and its mechanical properties	Dr. Yutaka Oya Department of aerospace engineering, Tohoku University
II-06	Complex Network Representation for Describing Structure-Property Relationships of Rubber Materials	Prof. Yoshifumi Amamoto Kyushu University
II-07	Search for Prevention Condition of Solidification Cracking of Parts Made by Selecting Laser Melting Process by Bayesian Optimization Approach	Dr. Houichi Kitano NIMS
II-08	Optimization and verification of electron beam melting process of non-weldable superalloy Inconel 713ELC23	Mr. Lei Yuchao Department of Materials Processing, Tohoku University
II-09	Optimization of gas atomization process parameters for Ni-based superalloys	Mr. Takuma Kohata NIMS
II-10	Prediction of Tensile Properties of Ti-6Al-4V Fabricated by Selective Laser Melting Based on Microstructural Features	Dr. Masahiro Kusano NIMS
II-11	Influences of plasma rotating electrode process parameters on the particle size distribution and microstructure of Ti-6Al-4V powder	Dr. Yujie Cui Institute for Materials Research, Tohoku University
II-12	Effect of scanning strategy on the crystallographic texture and Young’s modulus of β-type biomedical Ti–15Mo–5Zr–3Al alloy fabricated by selective laser melting	Dr. Shi-Hai Sun Division of Materials and Manufacturing Science, Osaka University
II-13	Prediction of Precipitation Sequences in Practical High-temperature Alloys with Energetics	Dr. Yoshiaki Toda NIMS
II-14	Influence Factors on Grain Morphology in a CoCrMo Alloy Fabricated by Electron Beam Powder-bed Additive Manufacturing	Dr. Yufan Zhao Chiba Laboratory, Institute for Materials Research, Tohoku University
II-15	Prediction and Mechanism of Tensile Strengthening in Single β-phase Ti-Ta Alloys with Oxygen Solutes	Mr. Katsuya Yokota Department of Mechanical Engineering, Graduate School of Osaka University
II-16	Phase-field simulation of crystal growth under solidification condition for Additive Manufacturing	Mr. Yuta Ohigashi Division of Materials and Manufacturing Science, Graduate School of Eng. Osaka University
II-17	Automatic deconvolution of a dilatometric curve in continuous cooling transformations of steels using machine learning	Dr. Hoheok Kim Graduate School of Materials Engineering, The University of Tokyo
II-18	True stress - true strain curves up to large strain extent at elevated temperatures in Ti alloys with image analysis tensile test method	Prof. Shiro Torizuka University of Hyogo
II-19	Prediction of Creep Rupture Time for Ferritic Steels Using Machine Learning	Mr. Junya Sakurai The University of Tokyo
II-20	Universal Framework of Bayesian Creep Model Selection for Steel	Dr. Yoh-ichi Mototake Institute of Statistical Mathematics
II-21	Fatigue Performance Prediction of Steel Welded Joints by Materials Integration	Dr. Takayuki Shiraiwa Department of Materials Engineering, The University of Tokyo
II-22	Microstructure Prediction of Dual Phase Steel with Optimal Strength-Elongation Balance	Mr. Shoya Kato The University of Tokyo
II-23	Estimation of fatigue lives from the short fatigue crack growth data of a Ni-Co base superalloy	Dr. Kishan Habib NIMS
II-24	Automatic Sorting of Metallurgical Microscopy Images with Machine Learning Tools	Dr. Dmitry S. Bulgarevich NIMS
II-25	CMC Virtual Test: Material Integration for designing high temperature parts used in turbine engines	Prof. Naohiro Shichijo Tokyo University of Technology
II-26	Bayesian inference of grain growth prediction via multi-phase-field models	Dr. Shin-ichi Ito The University of Tokyo
II-27	Application of neural network for thermodynamic data of non-equilibrium multiphase field model	Dr. Hiroshi Wakameda ITOCHU Techno-Solutions Corporation
II-28	Mathematical Approach to High-dimensional Structural Materials Data	Dr. Kazuto Akagi AIMR, Tohoku University

III. Materials Data Infrastructure including Measurements (III-01~26）

III-01	M-DaC: Materials Data Conversion Tools Published to Recreate Interoperative and Reusable Measurement Data Files	Dr. Mineharu Suzuki NIMS
III-02	tq : A Comprehensive Disciplinary Language for Materials Science	Dr. Kou Amano NIMS
III-03	FigResourceMiner: A search text in images, and graph visualization platform for academic articles	Mr. Yasuhiro Takada NIMS
III-04	Materials Data Platform Infrastructure System: Construction of hardware infrastructure to support subsystem development	Ms. Junko Hosoya NIMS
III-05	Effective use of outdated experimental apparatus by electronic laboratory notebook (ELN) and IoT data transfer systems	Dr. Yoshitomo Harada NIMS
III-06	Vocabulary Management System for the Materials Data Platform using Wikibase for Peer Collaboration	Dr. Asahiko Matsuda NIMS
III-07	Data-driven spectral analysis method in electron-beam based techniques	Dr. Bo Da NIMS
III-08	A universal method to determine material-parameter-dependent empirical formula for a given experimental data	Dr. Bo Da NIMS
III-09	API-FWK: Software Adapter Component for DPFC Integration	Dr. Shinji Kikuchi NIMS
III-10	Text Data Mining Platform System to Support Mining and Machine Learning of Scientific Literatures	Mr. Masaomi Takahashi NIMS
III-11	Automatic Construction of Superconducting Materials Database from Tables in related Research Papers	Dr. Thaer Dieb NIMS
III-12	Implementation of FAIR data principles with Materials Data Platform System	Dr. Takuya Kadohira NIMS
III-13	A proposal of standardized model for data exchange among systems on MDPF	Dr. Takuya Kadohira NIMS
III-14	Method for calculating the conversion of plastic work into heat	Mr. Toshiki Ishida Metallurgical Research Laboratory, Hitachi Metals, LTD.
III-15	Materials Data Repository (MDR)	Dr. Kosuke Tanabe NIMS
III-16	Fully automatic peak and background analysis of XPS spectral data -- Sparse modeling of XPS spectra	Dr. Hiroshi Shinotsuka NIMS
III-17	Large-Scale 3D Observation of Steel Microstructures Using a desktop-sized 3D Internal Structure Microscope	Dr. Noriko Yamashita RIKEN Center for Advanced Photonics, RIKEN
III-18	CAS: Central Authentication and Authorization Service H.Naito	Mr. Hiroyuki Naito NIMS
III-19	Information Extraction of Polymer Names and Properties from Tables in Scientific Papers	Dr. Hiroyuki Shindo Nara Institute of Science and Technology
III-20	NIMS Materials Database, “MatNavi”	Mr. Isao Kuwajima NIMS
III-21	Reducing Annotation Cost for Automatic Named Entity Extraction by using Domain-Specific Dictionary	Dr. Kei Wakabayashi Universty of Tsukuba
III-22	Thermodynamic Database for Open Science	Dr. Taichi Abe NIMS
III-23	Exploratory Visual Analytics Platform with Multiple Coordinated Views for Materials Informatics	Dr. Jun Fujima NIMS
III-24	Thermophysical property database of solids for materials informatics	Dr. Tetsuya Baba NIMS
III-25	Development of materials database for sintering analysis of Ni-based superalloy	Dr. Kentaro Kudo Department of Mechanical Engineering, Kyushu University
III-26	Study of Grain Boundary Structures using Topological Data Analysis and Simple Evaluation with Local Energy	Dr. Fumiko Ogushi NIMS
III-27	High-throughput Data Collection and Automatically Data Analysis Using IoT Device	Dr. Shigeyuki Matsunami NIMS

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ポスターセッション

学術ポスターセッション

2019年は「AI＋DATA発！材料イノベーション」

テーマ

ポスター会場マップ

ポスター発表一覧

I. Data-driven Research in Functional Materials (I-01~23)

Efficient recommendation tool of materials on Windows computers

Data-Driven Materials Discovery of Indirect Band-Gap Semiconductors for Photovoltaics

Machine Learning Model for Predicting Dielectric Constants of Oxides

Compositional descriptor-based prediction for the chemical compound stability

Experimental search for NASICON-type solid electrolytes with high Li-ion conductivity guided by Bayesian Optimization

Machine–Learning–Assisted for Exploration of Synthesis Condition for Metastable Novel Lanthanide Metal–Organic Frameworks Using Failed Experiments

Machine–Learning–Assisted Synthesis of Silver Thiolate Metal Organic Frameworks

Predicting Chemical and Electronic Properties Using a Novel Descriptor “Elemental Fingerprints set” with Neural Networks

Design and Optimization of Epoxy Adhesives through Active Learning from a Small Dataset

Automatic Extraction Framework of Superconductors Related Information from Scientific Literature

Data-Driven Design of Thermoelectric Materials with Experimental Data on Starrydata

Starrydata: a web system for experimental data extracted from published plot images

Active learning: A Booster for Discovering New Materials from Small Datasets

Accelerating metamaterials design by quantum annealing

Automation of the first-principles calculation and machine learning to search functional materials of high entropy alloys

Orbital Field Matrix: A New Descriptor for Materials

Subgroup Relevance Analysis: A Scheme for Selecting Important Descriptor Groups

High-throughput production of force-fields for Li-ion battery materials

Data-driven Exploration of Ion Conductive Alkali Metal Oxide for Batteries Using Materials Simulation

Active learning for level set estimation under input uncertainty

Topological Data Analysis toward Design of Functional Materials

Leveraging Legacy Data in Materials Science via Preference Learning

Metabolite Identification with de novo Molecule Generation

II. Data-driven Research in Structural Materials (II-01~28)

Data Driven Research for Composite Materials (Structural and Mechanical Properties of PAN- and Pitch-based Carbon Fibers)

Experimental evaluation on the environment resistance and fracture characteristics of multi-functional CFRP

Structure and mechanical properties of carbon fibers and polymers: Nanoindentation tests and molecular dynamics simulation study

Constructing crosslinked epoxy resins and investigation of mechanical properties with molecular dynamics simulations

Field theoretical approach to polymer phase separation and its mechanical properties

Complex Network Representation for Describing Structure-Property Relationships of Rubber Materials

Search for Prevention Condition of Solidification Cracking of Parts Made by Selecting Laser Melting Process by Bayesian Optimization Approach

Optimization and verification of electron beam melting process of non-weldable superalloy Inconel 713ELC23

Optimization of gas atomization process parameters for Ni-based superalloys

Prediction of Tensile Properties of Ti-6Al-4V Fabricated by Selective Laser Melting Based on Microstructural Features

Influences of plasma rotating electrode process parameters on the particle size distribution and microstructure of Ti-6Al-4V powder

Effect of scanning strategy on the crystallographic texture and Young’s modulus of β-type biomedical Ti–15Mo–5Zr–3Al alloy fabricated by selective laser melting

Prediction of Precipitation Sequences in Practical High-temperature Alloys with Energetics

Influence Factors on Grain Morphology in a CoCrMo Alloy Fabricated by Electron Beam Powder-bed Additive Manufacturing

Prediction and Mechanism of Tensile Strengthening in Single β-phase Ti-Ta Alloys with Oxygen Solutes

Phase-field simulation of crystal growth under solidification condition for Additive Manufacturing

Automatic deconvolution of a dilatometric curve in continuous cooling transformations of steels using machine learning

True stress - true strain curves up to large strain extent at elevated temperatures in Ti alloys with image analysis tensile test method

Prediction of Creep Rupture Time for Ferritic Steels Using Machine Learning

Universal Framework of Bayesian Creep Model Selection for Steel

Fatigue Performance Prediction of Steel Welded Joints by Materials Integration

Microstructure Prediction of Dual Phase Steel with Optimal Strength-Elongation Balance

Estimation of fatigue lives from the short fatigue crack growth data of a Ni-Co base superalloy

Automatic Sorting of Metallurgical Microscopy Images with Machine Learning Tools

CMC Virtual Test: Material Integration for designing high temperature parts used in turbine engines

Bayesian inference of grain growth prediction via multi-phase-field models

Application of neural network for thermodynamic data of non-equilibrium multiphase field model

Mathematical Approach to High-dimensional Structural Materials Data

III. Materials Data Infrastructure including Measurements (III-01~26）

M-DaC: Materials Data Conversion Tools Published to Recreate Interoperative and Reusable Measurement Data Files

tq : A Comprehensive Disciplinary Language for Materials Science

FigResourceMiner: A search text in images, and graph visualization platform for academic articles

Materials Data Platform Infrastructure System: Construction of hardware infrastructure to support subsystem development

Effective use of outdated experimental apparatus by electronic laboratory notebook (ELN) and IoT data transfer systems

Vocabulary Management System for the Materials Data Platform using Wikibase for Peer Collaboration

Data-driven spectral analysis method in electron-beam based techniques

A universal method to determine material-parameter-dependent empirical formula for a given experimental data

API-FWK: Software Adapter Component for DPFC Integration

Text Data Mining Platform System to Support Mining and Machine Learning of Scientific Literatures

Automatic Construction of Superconducting Materials Database from Tables in related Research Papers

Implementation of FAIR data principles with Materials Data Platform System

A proposal of standardized model for data exchange among systems on MDPF

Method for calculating the conversion of plastic work into heat

Materials Data Repository (MDR)

Fully automatic peak and background analysis of XPS spectral data -- Sparse modeling of XPS spectra

Large-Scale 3D Observation of Steel Microstructures Using a desktop-sized 3D Internal Structure Microscope

CAS: Central Authentication and Authorization Service H.Naito

Information Extraction of Polymer Names and Properties from Tables in Scientific Papers

NIMS Materials Database, “MatNavi”