Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
出版年份 2019 全文链接
标题
Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
作者
关键词
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出版物
APL Materials
Volume 7, Issue 10, Pages 101114
出版商
AIP Publishing
发表日期
2019-10-16
DOI
10.1063/1.5123019
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Ferromagnetism and Conductivity in Atomically Thin SrRuO3
- (2019) H. Boschker et al. Physical Review X
- Ferromagnetism above 1000 K in a highly cation-ordered double-perovskite insulator Sr3OsO6
- (2019) Yuki K. Wakabayashi et al. Nature Communications
- Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material
- (2019) Zhufeng Hou et al. ACS Applied Materials & Interfaces
- Design and exploration of semiconductors from first principles: A review of recent advances
- (2018) Fumiyasu Oba et al. Applied Physics Express
- Synthesis science of SrRuO3 and CaRuO3 epitaxial films with high residual resistivity ratios
- (2018) Hari P. Nair et al. APL Materials
- Accelerating the discovery of materials for clean energy in the era of smart automation
- (2018) Daniel P. Tabor et al. Nature Reviews Materials
- Multifunctional structural design of graphene thermoelectrics by Bayesian optimization
- (2018) Masaki Yamawaki et al. Science Advances
- Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
- (2018) Fang Ren et al. Science Advances
- Machine learning modeling of superconducting critical temperature
- (2018) Valentin Stanev et al. npj Computational Materials
- Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling
- (2018) Tetsuro Ueno et al. npj Computational Materials
- Charting the energy landscape of metal/organic interfaces via machine learning
- (2018) Michael Scherbela et al. PHYSICAL REVIEW MATERIALS
- Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory
- (2018) Prasanna V. Balachandran et al. PHYSICAL REVIEW MATERIALS
- Improved adaptive sampling method utilizing Gaussian process regression for prediction of spectral peak structures
- (2018) Yuki K. Wakabayashi et al. Applied Physics Express
- Cation distribution and magnetic properties in ultrathin (Ni1–xCox)Fe2O4(x=0–1) layers on Si(111) studied by soft x-ray magnetic circular dichroism
- (2018) Yuki K. Wakabayashi et al. PHYSICAL REVIEW MATERIALS
- Enhanced metallic properties of SrRuO3 thin films via kinetically controlled pulsed laser epitaxy
- (2016) J. Thompson et al. APPLIED PHYSICS LETTERS
- High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites
- (2016) Ambroise van Roekeghem et al. Physical Review X
- Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
- (2015) Atsuto Seko et al. PHYSICAL REVIEW LETTERS
- Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
- (2014) Atsuto Seko et al. PHYSICAL REVIEW B
- Multi-source MBE with high-precision rate control system as a synthesis method sui generis for multi-cation metal oxides
- (2013) Hideki Yamamoto et al. JOURNAL OF CRYSTAL GROWTH
- Quasiparticle Mass Enhancement and Temperature Dependence of the Electronic Structure of FerromagneticSrRuO3Thin Films
- (2013) D. E. Shai et al. PHYSICAL REVIEW LETTERS
- Structure, physical properties, and applications ofSrRuO3thin films
- (2012) Gertjan Koster et al. REVIEWS OF MODERN PHYSICS
- A perpendicular-anisotropy CoFeB–MgO magnetic tunnel junction
- (2010) S. Ikeda et al. NATURE MATERIALS
- Spin transfer switching in TbCoFe∕CoFeB∕MgO∕CoFeB∕TbCoFe magnetic tunnel junctions with perpendicular magnetic anisotropy
- (2008) Masahiko Nakayama et al. JOURNAL OF APPLIED PHYSICS
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