D02-Data_Driven High_Entropy and Amorphous Materials

Symposium Introduction

High-entropy alloys and amorphous materials possess vast compositional and processing spaces, making conventional trial-and-error approaches inefficient for their design and optimization. Data science, machine learning, high-throughput experimentation, and computational modeling provide new opportunities to accelerate materials discovery and reveal composition–processing–structure–property relationships.

This symposium will focus on recent advances in the data-driven design, processing, characterization, and performance optimization of high-entropy and amorphous materials. It will cover emerging manufacturing technologies, multiscale simulations, and the behavior of materials under extreme conditions and demanding service environments. By bringing together researchers, engineers, and industrial practitioners, the symposium aims to promote interdisciplinary exchange, identify key scientific challenges, and strengthen collaboration between academia and industry.

Symposium Topics

1. Data-driven design of high-entropy and amorphous materials

2. Machine learning for materials-property prediction

3. High-throughput experimentation and materials genome engineering

4. Multiscale computational modeling and simulation

5. Additive manufacturing and advanced processing

6. Mechanical behavior under extreme conditions

7. Structural and functional properties

Symposium Organizers 

Organized by

University of Science and Technology Beijing

Supported by

TUniversity of Science and Technology Beijing; Xi’an Jiaotong University; The University of Tennessee, Knoxville; Dongguan Institute of Materials Science and Technology, CAS; National Tsing Hua University

Contact with

Name: Jiasheng Wang

Phone: +86-18035277089

Email: d202420018@xs.ustb.edu.cn

Institution: University of Science and Technology Beijing