Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints-Reference-Cited by-同舟云学术

Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints

Published:2020-12 Issue: Volume:25 Page:101538
ISSN:2352-4928
Container-title:Materials Today Communications
language:en
Short-container-title:Materials Today Communications

Author:

Osada Keiichi^ORCID,Kutsukake Kentaro,Yamamoto Jun,Yamashita Shigeo,Kodera Takashi,Nagai Yuta,Horikawa Tomoyuki,Matsui Kota,Takeuchi Ichiro,Ujihara Toru

Funder

Center for Advanced Intelligence Project, RIKEN

Publisher

Elsevier BV

Subject

Materials Chemistry,Mechanics of Materials,General Materials Science

Reference26 articles.

1. Crystal structure prediction accelerated by Bayesian optimization;Yamashita;Phys. Rev. Mater.,2018

2. Bayesian inference of atomistic structure in functional materials;Todorović;NPJ Comput. Mater.,2019

3. Acceleration of stable interface structure searching using a kriging approach;Kiyohara;J. Appl. Phys.,2016

4. Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structure;Bondevik;Comput. Mater. Sci.,2019

5. Rapid prediction of molecule arrangements on metal surfaces via Bayesian optimization;Packwood;Appl. Phys. Express,2017

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Crystal growth, bulk: Theory and models;Encyclopedia of Condensed Matter Physics;2024

2. Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials;Journal of Alloys and Compounds;2023-12

3. Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation;Materials;2023-10-28

4. MIOpt: optimization framework for backward problems on the basis of the concept of materials integration;Science and Technology of Advanced Materials: Methods;2023-09-08

5. Data-driven automated control algorithm for floating-zone crystal growth derived by reinforcement learning;Scientific Reports;2023-05-09