Shengzhou Li

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Shengzhou verified their affiliation via an institutional email.

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• Doctor of Engineering
• PostDoc Position at National Institute for Materials Science

Size control of Pt nanoparticle catalysts is one of the key challenges in fuel cell development. Miniaturization of Pt nanoparticles is expected to increase activity and save the amount of Pt, while very small particles (e.g. sub-nano particles or clusters) have different electronic structures and stabilities from “nano” particles. To investigate t...

Traditional knowledge extraction methods often rely on human expertise, which can be time-consuming and prone to cognitive biases. This work presents a comprehensive predictive framework that integrates rule extraction with machine learning (ML) to enhance knowledge discovery in materials science. We used subgroup discovery algorithms to extract ru...

The size and site dependences of atomic and electronic structures in isolated and supported gold nanoparticles have been investigated by large-scale density functional theory (DFT) calculations using multi-site support functions....

Materials science research benefits from the powerful machine-learning (ML) surrogate models, but it is also limited by the implicit requirement for sufficiently big and balanced data distribution for ML. In this paper, we propose a model to obtain more credible results for small and imbalanced materials data sets as well as chemical knowledge. Tak...

Material datasets are high-dimensional and high-noise, which makes most machine learning (ML) methods inefficient. We present a new framework which embeds material domain knowledge into the ML method. By doing so, we illustrate its role and improve the prediction accuracy of 540 perovskite materials.

Acquiring knowledge and assisting materials design from computing and experimental data is very interesting and important at the intersection of materials and data science. In this work, we construct a whole framework to find an easy-to-interpret equation by taking a study of Young’s modulus of Ti-Nb alloys as an example. Here, we used Young’s modu...

The selection of machine learning （ML） models and input features can help to understand the internal rules of the task of interest， especially for the researches of high entropy alloys （HEAs） phase with many formation factors. Exploring the rules of high entropy alloys phase formation has clear guiding significance for the design of new alloys. In...

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...

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...

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...