Machine learning assisted composition effective design for precipitation strengthened copper alloys
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Machine learning assisted composition effective design for precipitation strengthened copper alloys
Authors
Keywords
Machine learning, Feature screening, Bayesian optimization, Alloy design, Copper alloys
Journal
ACTA MATERIALIA
Volume 215, Issue -, Pages 117118
Publisher
Elsevier BV
Online
2021-06-24
DOI
10.1016/j.actamat.2021.117118
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization
- (2020) Pei Liu et al. npj Computational Materials
- Relationship between the microstructure and properties of a peak aged Cu–Ni–Co–Si alloy
- (2019) Jiang Li et al. MATERIALS SCIENCE AND TECHNOLOGY
- Machine learning assisted design of high entropy alloys with desired property
- (2019) Cheng Wen et al. ACTA MATERIALIA
- Materials informatics: From the atomic-level to the continuum
- (2019) J.M. Rickman et al. ACTA MATERIALIA
- Materials informatics for the screening of multi-principal elements and high-entropy alloys
- (2019) J. M. Rickman et al. Nature Communications
- Co effects on Cu-Ni-Si alloys microstructure and physical properties
- (2019) Zhuan Zhao et al. JOURNAL OF ALLOYS AND COMPOUNDS
- A property-oriented design strategy for high performance copper alloys via machine learning
- (2019) Changsheng Wang et al. npj Computational Materials
- Enhanced Mechanical and Electrical Properties of a Cu-Ni-Si Alloy by Thermo-mechanical Processing
- (2019) Lei Jiang et al. METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
- Enhanced combination properties of Cu-0.8Cr alloy by Fe and P additions
- (2019) Yingtao Wang et al. JOURNAL OF NUCLEAR MATERIALS
- Accelerated Discovery of Large Electrostrains in BaTiO3 -Based Piezoelectrics Using Active Learning
- (2018) Ruihao Yuan et al. ADVANCED MATERIALS
- Correlations between microstructures and properties of Cu-Ni-Si-Cr alloy
- (2018) Yake Wu et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Inverse molecular design using machine learning: Generative models for matter engineering
- (2018) Benjamin Sanchez-Lengeling et al. SCIENCE
- Microstructure and Properties of a Novel Cu–Ni–Co–Si–Mg Alloy with Super-high Strength and Conductivity
- (2018) Jiazhen Huang et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Aging Behavior and Precipitation Analysis of Cu-Ni-Co-Si Alloy
- (2018) Xiangpeng Xiao et al. Crystals
- The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning
- (2018) Ruihao Yuan et al. IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
- Precipitation behavior of Cu-3.0Ni-0.72Si alloy
- (2018) Jiang Yi et al. ACTA MATERIALIA
- Effect of rolling and aging processes on microstructure and properties of Cu-Cr-Zr alloy
- (2017) Huadong Fu et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Effects of Cr and Zr additions on microstructure and properties of Cu-Ni-Si alloys
- (2016) Wei Wang et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Optimization of conductivity and strength in Cu-Ni-Si alloys by suppressing discontinuous precipitation
- (2016) Seung Zeon Han et al. METALS AND MATERIALS INTERNATIONAL
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Accelerated search for materials with targeted properties by adaptive design
- (2016) Dezhen Xue et al. Nature Communications
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Adaptive Strategies for Materials Design using Uncertainties
- (2016) Prasanna V. Balachandran et al. Scientific Reports
- Microstructure and Properties of a High-Strength Cu-Ni-Si-Co-Zr Alloy
- (2013) S. Chenna Krishna et al. JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE
- Tensile and electrical properties of direct aged Cu-Ni-Si-x%Ti alloys
- (2013) Eungyeong Lee et al. METALS AND MATERIALS INTERNATIONAL
- Microstructure and properties of Cu–Ni–Si–Zr alloy after thermomechanical treatments
- (2013) Xiang-Peng Xiao et al. RARE METALS
- The transformation behavior of Cu–8.0Ni–1.8Si–0.6Sn–0.15Mg alloy during isothermal heat treatment
- (2011) Qian Lei et al. MATERIALS CHARACTERIZATION
- Microstructure and mechanical properties of Cu–Ni–Si alloys
- (2007) Ryoichi Monzen et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish 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 More