Shengzhou Li

Shengzhou Li
University of Tsukuba · Department of Computer Science

Doctor of Engineering

About

4
Publications
650
Reads
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39
Citations
Citations since 2016
4 Research Items
38 Citations
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Introduction
I am working on Machine Learning with DFT calculation.
Education
October 2020 - April 2023
University of Tsukuba
Field of study
  • Machine Learning, DFT, Material Science
October 2019 - August 2020
Northeast Normal University
Field of study
  • Japanese Language
September 2016 - April 2019
Shanghai University
Field of study
  • Machine Learning and Material Science

Publications

Publications (4)
Article
Hume-Rothery rules, occupying a central space at the heart of metallurgy, are one of the most important rules in materials science. One limitation to the binary alloys is the lack of predictable tools for understanding the relationship between alloy structure and solid solubility. In this paper, with Hume-Rothery rules, we demonstrated the proposed...
Article
The formation of solid solution alloy systems happens to two kinds of atoms with similar radii to comply with Hume-Rothery rules as a common feature. In recent years, as a useful tool, Machine Learning (ML) has been widely used in material science research to obtain useful information, including material preparation and process and so on. In this w...
Article
Full-text available
A high-transition-temperature (high-TC) superconductor is an important material used in many practical applications like magnetically levitated trains and power transmission. The superconducting transition temperature TC is determined by the layered crystals, bond lengths, valency properties of the ions and Coulomb coupling between electronic bands...

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