Predicting and optimizing coupling effect in magnetoelectric multi-phase composites based on machine learning algorithm
出版年份 2021 全文链接
标题
Predicting and optimizing coupling effect in magnetoelectric multi-phase composites based on machine learning algorithm
作者
关键词
Machine learning, Magnetoelectric coupling, Multi-phase composite, Finite Element method, Optimization
出版物
COMPOSITE STRUCTURES
Volume 271, Issue -, Pages 114175
出版商
Elsevier BV
发表日期
2021-05-26
DOI
10.1016/j.compstruct.2021.114175
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization
- (2020) Yuhao Wang et al. ACTA MATERIALIA
- Machine learning assisted design of high entropy alloys with desired property
- (2019) Cheng Wen et al. ACTA MATERIALIA
- Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel
- (2019) Chunguang Shen et al. ACTA MATERIALIA
- A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization
- (2019) Khader M. Hamdia et al. FINITE ELEMENTS IN ANALYSIS AND DESIGN
- Thermal Driven Giant Spin Dynamics at Three-Dimensional Heteroepitaxial Interface in Ni0.5Zn0.5Fe2O4/BaTiO3-Pillar Nanocomposites
- (2018) Guohua Dong et al. ACS Nano
- Material structure-property linkages using three-dimensional convolutional neural networks
- (2018) Ahmet Cecen et al. ACTA MATERIALIA
- Advanced Steel Microstructural Classification by Deep Learning Methods
- (2018) Seyed Majid Azimi et al. Scientific Reports
- Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
- (2017) Austin D. Sendek et al. Energy & Environmental Science
- Ferromagnetic, ferroelectric and magnetoelectric properties of (001)-oriented Pb(Zr 0.52 Ti 0.48 )O 3 /La 0.67 Sr 0.33 MnO 3 composite films deposited on Si substrates using chemical solution deposition
- (2017) Zongfan Duan et al. JOURNAL OF ALLOYS AND COMPOUNDS
- A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
- (2017) Seiji Kajita et al. Scientific Reports
- Magnetostatic Interactions in Self-Assembled CoxNi1–xFe2O4/BiFeO3 Multiferroic Nanocomposites
- (2016) Shuchi Ojha et al. ACS Nano
- Data-driven computational mechanics
- (2016) T. Kirchdoerfer et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Magneto-electric coupling study in multiferroic La0.7Ba0.3MnO3–BaTiO3 composite ceramic at room temperature
- (2015) Ling Zhou et al. CERAMICS INTERNATIONAL
- Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening
- (2015) Xianfeng Ma et al. Journal of Physical Chemistry Letters
- Multiferroic Polymer Laminate Composites Exhibiting High Magnetoelectric Response Induced by Hydrogen-Bonding Interactions
- (2013) Jiezhu Jin et al. ADVANCED FUNCTIONAL MATERIALS
- Enhanced sensitivity to direct current magnetic field changes in Metglas/Pb(Mg1/3Nb2/3)O3–PbTiO3 laminates
- (2011) Junqi Gao et al. JOURNAL OF APPLIED PHYSICS
- Damage detection in initially nonlinear systems
- (2010) Luke Bornn et al. INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE
- Present status of theoretical modeling the magnetoelectric effect in magnetostrictive-piezoelectric nanostructures. Part I: Low frequency and electromechanical resonance ranges
- (2010) M. I. Bichurin et al. JOURNAL OF APPLIED PHYSICS
- Verified finite element simulation of multiferroic structures: Solutions for conducting and insulating systems
- (2008) John F. Blackburn et al. JOURNAL OF APPLIED PHYSICS
- Magnetoelectric Laminate Composites: An Overview
- (2008) Junyi Zhai et al. JOURNAL OF THE AMERICAN CERAMIC SOCIETY
- A survey of robot learning from demonstration
- (2008) Brenna D. Argall et al. ROBOTICS AND AUTONOMOUS SYSTEMS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now