Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials-Reference-Cited by-同舟云学术

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Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials

Published:2023-12 Issue: Volume:966 Page:171613
ISSN:0925-8388
Container-title:Journal of Alloys and Compounds
language:en
Short-container-title:Journal of Alloys and Compounds

Author:

Ishii Akimitsu^ORCID,Kikuchi Shinjiro,Yamanaka Akinori,Yamamoto Akiyasu^ORCID

Funder

Core Research for Evolutional Science and Technology

Japan Science and Technology Agency

Publisher

Elsevier BV

Subject

Materials Chemistry,Metals and Alloys,Mechanical Engineering,Mechanics of Materials

Reference49 articles.

1. Optimization of direct extrusion process for Nd-Fe-B magnets using active learning assisted by machine learning and Bayesian optimization;Lambard;Scr. Mater.,2022

2. Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints;Osada;Mater. Today Commun.,2020

3. Efficient closed-loop maximization of carbon nanotube growth rate using Bayesian optimization;Chang;Sci. Rep.,2020

4. Bayesian optimization for chemical products and functional materials;Wang;Curr. Opin. Chem. Eng.,2022

5. Accelerate synthesis of metal–organic frameworks by a robotic platform and Bayesian optimization;Xie;ACS Appl. Mater. Interfaces,2021

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1. Current Status and Future Perspective of the Utilization of Data Science Methods in the Development of Fabrication Processes for Superconducting Materials;IEEJ Transactions on Fundamentals and Materials;2024-09-01

2. Research on optimization of key thermal parameters of the secondary loop of PWR based on improved BP neural network;Annals of Nuclear Energy;2024-07

3. Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design;NPG Asia Materials;2024-06-07

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