X.-D.Xiang

Department of Materials Science and Engineering, Southern University of Science
and Technology, Shenzhen 518055, China 

EXTENDED ABSTRACT: Material genes could be understood as the relationship between composition (element, valence state, function group, etc.), structure (lattice, molecular weight, defect, etc.), thermodynamic parameters (temperature, time, pressure, etc.) and physical properties, represented as materials phase diagrams. Combinatorial Materials Chip strategy has been used for high-throughput materials synthesis and characterization. How to avoid impurity phases in traditional solid state synthesis as well as liquid based synthesis is critical in high-throughput synthesis. I will discuss three different approaches to address this issue. I then will discuss the issue of high-throughput characterization. Inspired by life-science and astrophysics, I will discuss a recently developed an optical plasma resonance spectrum method to characterize the electrical transport properties and related applications. Over the past three decades, we have been working on the equilibrium phase diagrams. I will discuss recent progress in studying dynamic phase diagrams. Last, I will discuss the progress using neural networks to learn and predict materials properties.
Keywords: materials phase diagrams, impurity phases, optical plasma resonance spectrum, dynamic phase diagrams, neural networks
REFERENCES
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[2] Xiang, Gang Wang, Xiaokun Zhang, Yong Xiang, Hong Wang, Engineering, 1, 225 (2015)

[3] P. Zhang, H. Tang, C. Gu, H. Wang, G. Luo, Y. Lu, X.-D. Xiang, National Science Review 8, 4 (2021)