Machine learning glass transition temperature of polymers

Y Zhang, X Xu - Heliyon, 2020 - cell.com
As an important thermophysical property, polymers' glass transition temperature, Tg, could
sometimes be difficult to determine experimentally. Modeling methods, particularly data …

Transformation temperature predictions through computational intelligence for NiTi-based shape memory alloys

Y Zhang, X Xu - Shape Memory and Superelasticity, 2020 - Springer
Shape memory alloys (SMAs) are a class of metallic compounds that can return to their
original forms, shapes, or sizes, when subjected to environmental stimuli, such as …

Lattice misfit predictions via the Gaussian process regression for Ni-based single crystal superalloys

Y Zhang, X Xu - Metals and Materials International, 2021 - Springer
Ni-based single crystal superalloys exhibit superb mechanical strength, particularly, creep
resistance at elevated temperature. The unique microstructure, which is consisted of γ γ and …

Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning

Y Zhang, X Xu - International Journal of Materials Research, 2021 - degruyter.com
Recently, Fe-based superconductors have shown promising properties of high critical
temperature and high upper critical fields, which are prerequisites for applications in high …

Modeling oxygen ionic conductivities of ABO3 Perovskites through machine learning

Y Zhang, X Xu - Chemical Physics, 2022 - Elsevier
The ABO 3 crystal structure allows for enormous flexibilities in ion doping and substitutions,
which enables tailoring of oxygen ionic conductivities to fit practical applications. We …

Machine learning f-doped Bi (Pb)–Sr–Ca–Cu–O superconducting transition temperature

Y Zhang, X Xu - Journal of Superconductivity and Novel Magnetism, 2021 - Springer
The increase in critical temperature of high-temperature superconductors fulfills needs of
practical applications with liquid-helium-free refrigeration and a delay in magnet failure. But …

Machine learning lattice constants for spinel compounds

Y Zhang, X Xu - Chemical Physics Letters, 2020 - Elsevier
Spinels can house a large variety of elements into the crystal structure. As a crystallographic
parameter, the lattice constant, a, is highly sought in further investigations into materials …

Machine learning decomposition onset temperature of lubricant additives

Y Zhang, X Xu - Journal of Materials Engineering and Performance, 2020 - Springer
The thermal stability of lubricant additives is a fundamental parameter in practical
applications, which is determined by the molecular structure. The ability to predict thermal …

Modeling of lattice parameters of cubic perovskite oxides and halides

Y Zhang, X Xu - Heliyon, 2021 - cell.com
Perovskites having the chemical formulae of ABX 3 are promising candidates for various
electronic, magnetic, and thermal applications. One of the important structural factors is a …

Machine learning the central magnetic flux density of superconducting solenoids

Y Zhang, X Xu - Materials Technology, 2022 - Taylor & Francis
The central magnetic flux density is usually simulated via finite element methods that require
a significant amount of inputs and computation resources. We develop the Gaussian …