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Filtering by: Author Lambard, Guillaume Creator Keitaro Sodeyama

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.

Description/Abstract:

We implemented an active learning pipeline assisted by machine learning and Bayesian optimization (ALMLBO) for predicting magnetic proper...

Keyword:

Active learning, Machine learning, Bayesian Optimization, Nd-Fe-B magnet, Hot extrusion, Process, Coercivity, Remanence, Squareness, and Maximum energy product

Material/Specimen:

Anisotropic permanent magnets fabricated by hot extrusion using a commercial Nd14Fe76Co3.4B6Ga0.6 (at%) powder (MQU-F™)

Resource Type:

Dataset

Data origin:

experiments

Author:

Lambard, Guillaume, Sasaki, Taisuke, Sodeyama, Keitaro, Ohkubo, Tadakatsu, and Hono, Kazuhiro

Operator:

PRYTULIAK, Anastasiia and TOYOOKA, Yoshiya

Date Uploaded:

11/05/2021

Date Modified:

11/10/2021
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