Research progress on liquid-solid transition under synchrotron radiation X-ray and simulation

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Authors: Hanmei Chen, Pengfei Yu, Qiulin Bi, Jialiang Liu, Junting Li, Mingxu Xia, Xiujun Han
Cited by: 1
References: 164

Abstract

The structures of metallic melts are of utmost significance for understanding liquid properties, atomic dynamics, and solidification behaviors, including the formation of solidified microstructures. In recent years, with the continuous advancement of experimental techniques and numerical simulation methods, researchers have obtained deeper insights into the microscopic details of the liquid structure, the structure evolution, and the correlations between the liquid structure and the solidified microstructure. This article reviews the main experimental techniques and simulation methods employed in the study of metallic melt structures, as well as the pioneering findings in the field. High-energy synchrotron X-ray diffraction and in situ X-ray imaging techniques and their applications in elucidating the liquid structure and its evolution during solidification are introduced. Special attention is given to the development of synchrotron equipment. Simulation methods for analyzing melt structures include classical molecular dynamics (MD), ab initio molecular dynamics (AIMD), reversed Monte Carlo (RMC), and machine learning (ML), all of which have been applied to predict the atomic structures of metallic melts. The most recent progress in machine learning potentials or force fields is also introduced. The article also discusses future research directions, including the integration of high-resolution imaging with high-energy X-ray diffraction techniques, the application of artificial intelligence-assisted simulations to reduce computational costs, and the investigation of external factors such as pressure and cooling rates on solidification behavior. By combining advanced experimental and computational approaches, research on metallic melt structures is expected to move toward a more comprehensive and in-depth understanding, opening new opportunities for breakthroughs in metallurgy and materials science.

Fields of Study & Subjects

Solidification and crystal growth phenomena Machine Learning in Materials Science Material Dynamics and Properties

Publication details

Journal / source
Microstructures
Publisher
OAE Publishing Inc.
Volume
6
Issue
1
Publication date
12 January 2026