A property-oriented design strategy for high performance copper alloys via machine learning

Materials data validation and imputation with an artificial neural network

(2018) P.C. Verpoort et al.   COMPUTATIONAL MATERIALS SCIENCE

Matminer: An open source toolkit for materials data mining

(2018) Logan Ward et al.   COMPUTATIONAL MATERIALS SCIENCE

Effects of Ni content on the cast and solid-solution microstructures of Cu-0.4wt%Be alloys

(2018) Shuang-jiang He et al.   International Journal of Minerals Metallurgy and Materials

Effect of magnesium on microstructure and properties of Cu-Cr alloy

(2018) Ziqian Zhao et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Effect of Ag addition on the microstructure and mechanical properties of Cu-Cr alloy

(2018) Sheng Xu et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Tensile deformation characteristics of a Cu−Ni−Si alloy containing trace elements processed by high-pressure torsion with subsequent aging

(2018) Hikaru Watanabe et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

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

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

(2018) Andrea Rovinelli et al.   npj Computational Materials

Two-way design of alloys for advanced ultra supercritical plants based on machine learning

(2018) Xiaobing Hu et al.   COMPUTATIONAL MATERIALS SCIENCE

Correlation between microstructures and mechanical properties of cryorolled CuNiSi alloys with Cr and Zr alloying

(2018) Wei Wang et al.   MATERIALS CHARACTERIZATION

Deep-learning-based inverse design model for intelligent discovery of organic molecules

(2018) Kyungdoc Kim et al.   npj Computational Materials

Precipitation behavior of Cu-3.0Ni-0.72Si alloy

(2018) Jiang Yi et al.   ACTA MATERIALIA

Minor-alloyed Cu-Ni-Si alloys with high hardness and electric conductivity designed by a cluster formula approach

(2017) Dongmei Li et al.   Progress in Natural Science-Materials International

Minor-alloyed Cu-Ni-Si alloys with high hardness and electric conductivity designed by a cluster formula approach

(2017) Dongmei Li et al.   Progress in Natural Science-Materials International

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Microstructure and Precipitate's Characterization of the Cu-Ni-Si-P Alloy

(2016) Yi Zhang et al.   JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE

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

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

Data mining our way to the next generation of thermoelectrics

(2016) Taylor D. Sparks et al.   SCRIPTA MATERIALIA

Accelerated search for materials with targeted properties by adaptive design

(2016) Dezhen Xue et al.   Nature Communications

An artificial neural network for predicting corrosion rate and hardness of magnesium alloys

(2016) X. Xia et al.   MATERIALS & DESIGN

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Design of medium carbon steels by computational intelligence techniques

(2015) N.S. Reddy et al.   COMPUTATIONAL MATERIALS SCIENCE

Improvement in strength and thermal conductivity of powder metallurgy produced Cu–Ni–Si–Cr alloy by adjusting Ni/Si weight ratio and hot forging

(2015) Huei-Sen Wang et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Microstructural design of new high conductivity – high strength Cu-based alloy

(2015) S. Gorsse et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Studies on Dynamic Elastic and Internal Friction Properties of Cu-Cr-Zr-Ti Alloy Between 25 and 650 °C

(2015) K. Saravanan et al.   JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE

Deformation microstructures, strengthening mechanisms, and electrical conductivity in a Cu–Cr–Zr alloy

(2015) R. Mishnev et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Effects of Small Addition of Ti on Strength and Microstructure of a Cu-Ni-Si Alloy

(2015) Chihiro Watanabe et al.   METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE

Microstructure and properties of Cu–2.3Fe–0.03P alloy during thermomechanical treatments

(2015) Qi-yi DONG et al.   TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA

Artificial neural network prediction of aging effects on the wear behavior of IN706 superalloy

(2015) Osman Palavar et al.   MATERIALS & DESIGN

Hot working behavior of a super high strength Cu–Ni–Si alloy

(2013) Q. Lei et al.   MATERIALS & DESIGN

Solid-solution copper alloys with high strength and high electrical conductivity

(2013) Kazunari Maki et al.   SCRIPTA MATERIALIA

Improvement in Mechanical Properties of a Cu–2.0 mass%Ni–0.5 mass%Si–0.1 mass%Zr Alloy by Combining Both Accumulative Roll-Bonding and Cryo-Rolling with Aging

(2012) Yusaku Takagawa et al.   MATERIALS TRANSACTIONS

Effects of deep cryogenic treatment on the solid-state phase transformation of Cu–Al alloy in cooling process

(2012) Yuhui Wang et al.   PHASE TRANSITIONS

Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys

(2008) Mehmet Sirac Ozerdem et al.   MATERIALS & DESIGN

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

Study of rare earth elements on the physical and mechanical properties of a Cu–Fe–P–Cr alloy

(2007) F.A. Guo et al.   Materials Science and Engineering B-Advanced Functional Solid-State Materials