論文・データセット - MDR: NIMS Materials Data Repository

絞り込み解除

9 件のレコードが見つかりました。

Manuscript.pdf

In Situ Forming Supramolecular Nanofiber Hydrogel as a Biodegradable Liquid Embolic Agent for Postembolization Tissue Remodeling

論文
著者: Akihiro Nishiguchi ; Miho Ohta ; Debabrata Palai ; Shima Ito ; Kensaku Mori ; Ryotaro Akagi ; Christophe Bajan ; Guillaume Lambard ; Keitaro Sodeyama ; Tetsushi Taguchi
キーワード: Hydrogel, Embolic agent, Machine learning
刊行年月日: 2024-11-07
更新時刻: 2025-02-26 16:30:29 +0900

1-s2.0-S0169743925000085-main.pdf

MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization

論文
著者: Christophe Bajan ; Guillaume Lambard
キーワード: Graphical user interface, Statistical analysis, Machine learning, Bayesian optimization, Process optimization, User-friendly
刊行年月日: 2025-01-21
更新時刻: 2025-01-27 12:30:30 +0900

d4dd00319e.pdf

Exploring the Expertise of Large Language Models in Materials Science and Metallurgical Engineering

論文
著者: Christophe Bajan ; Guillaume Lambard
キーワード: Large language models, Materials science, Metallurgical Engineering
刊行年月日: 2025-01-20
更新時刻: 2025-01-21 12:30:37 +0900

41524_2019_203_MOESM1_ESM.avi

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

論文
著者: Stephen Wu ; Yukiko Kondo ; Masa-aki Kakimoto ; Bin Yang ; Hironao Yamada ; Isao Kuwajima ; Guillaume Lambard ; Kenta Hongo ; Yibin Xu ; Junichiro Shiomi ; Christoph Schick ; Junko Morikawa ; Ryo Yoshida
キーワード: machine learning, polymer, thermal conductivity, molecular design
刊行年月日
更新時刻: 2024-11-21 16:31:23 +0900

Revised Manuscript-ActaMater_20240327.docx

ALMLBO PIPELINE APPROACH APPLY TO MATERIAL PROCESS AND COMPOSITION OPTIMIZATION FOR ENERGY-SAVING APPLICATIONS

口頭発表
著者: Cédric Bourgès ; Guillaume Lambard ; Naoki Sato ; Makoto Tachibana ; Satoshi Ishii ; Takao Mori
キーワード: Kesterite, Machine learning, Process, Thermoelectric, Ceramic
刊行年月日
更新時刻: 2025-04-10 21:55:17 +0900

Mining experimental data from materials science literature with large language models an evaluation study.pdf

Mining experimental data from materials science literature with large language models: an evaluation study

論文
著者: Luca Foppiano ; Guillaume Lambard ; Toshiyuki Amagasa ; Masashi Ishii
キーワード: Large language models, benchmark, NER, TDM, evaluation, materials science
刊行年月日: 2024-12-31
更新時刻: 2024-10-24 16:30:23 +0900

1-s2.0-S135964542400692X-main.pdf

Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development

論文
著者: Cédric Bourgès ; Guillaume Lambard ; Naoki Sato ; Makoto Tachibana ; Satoshi Ishii ; Takao Mori
キーワード: Kesterite, Machine learning, Process, Thermoelectric, Ceramic
刊行年月日: 2024-08-29
更新時刻: 2024-09-20 16:30:30 +0900

DataSummary_Extrusion_Manuscript_20210728.xlsx

Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.

データセット
著者: Lambard, Guillaume ; Sasaki, Taisuke ; Sodeyama, Keitaro ; Ohkubo, Tadakatsu ; Hono, Kazuhiro
キーワード: Maximum energy product, Coercivity, Squareness, Machine learning, Remanence, Process, Bayesian Optimization, Hot extrusion, Active learning, Nd-Fe-B magnet
刊行年月日: 2021-10-11
更新時刻: 2022-10-03 02:01:08 +0900

Prediction_and_optimization_of_epoxy_adhesive_strength_from_a_small_dataset_through_active_learning.pdf

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

論文
著者: Lambard, Guillaume ; Naito, Masanobu ; Samitsu, Sadaki ; Pruksawan, Sirawit ; Sodeyama, Keitaro
キーワード: active learning, Machine learning, adhesive
刊行年月日: 2019-12-31
更新時刻: 2024-01-05 22:11:50 +0900