high-entropy alloys, nanocrystalline alloys, porous metals, quasicrystals, and titanium alloys and metallic composites


additive manufacturing (3D printing), cold spray, powder metallurgy, and micro/nano-fabrication


multiscale mechanics, in-situ instrumentation, and high strain-rate/temperature deformation, and fracture and failure analysis

Machine Learning

Machine learning for materials design and intelligent manufacturing

Sample preparation of HEAs: (a) the bulk coarse-grained HEAs produced by arc melting and (c) the inverse pole figure map of the bulk HEAs; (b) the thin-film nanocrystalline (<100 nm grain size) HEAs produced by the magnetron co-sputtering system equipped with four targets arranged in a symmetry, and (d) their inverse pole figure map showing  texture. 

  • Y. Zou, et al. “Nanocrystalline high entropy alloys: A new paradigm in high temperature strength and stability” Nano Letters (2017))

  • Y. Zou, et al. “Ultrastrong, ductile and stable high-entropy alloys at small scales” Nature Communications 6 (2015).

  • Y. Zou, et al., “Size-dependent plasticity in an Nb25Mo25Ta25W25 refractory high-entropy alloy” Acta Materialia (2014)

Metallic Materials

     ​ Design of high-strength and thermally stable alloys using novel fabrication and testing techniques.


Study structure-property relationship from Simple Ionic Crystals to Complex Intermetallic Phases

Multiscale Mechanics 

  • Mechanics: multiscale mechanics, in-situ instrumentation, and high strain-rate/temperature deformation

  • More details coming soon...


SEM in situ nanomechanical testing system (left) and standalone nanoindenter system (right) 

Additive Manufacturing

  • Manufacturing: metal additive manufacturing (3D printing), cold spray technology, powder metallurgy, and micro- and nanofabrication

  • More details coming soon...


Steel (left) and titanium (right) samples produced by additive manufacturing


Machine Learning for alloy design and processing optimization

  • Machine Learning: with the rapid development of artificial intelligence (AI), intelligent methods have been widely applied in materials science research.

  • Our group is seeking novel approaches to integrate ML within materials research. Teams in our group are focusing on ML for mechanics, alloy design, topological optimization, and in-situ monitoring.and control for 3D printing.

Phase recognition using K-means clustering 

Additive manufacturing process monitoring : (a) experimental setup (b) our proposed vanilla model (c) class-discriminative maps of the melt pool

Our proposed detection and classification deep learning model for the melt tracks

© 2020 Yu Zou