Manufacturing of metallic glass components: Processes, structures and properties

S Sohrabi, J Fu, L Li, Y Zhang, X Li, F Sun, J Ma… - Progress in Materials …, 2024 - Elsevier
Metallic glasses (MGs) are out-of-equilibrium metallic systems known for their unique
structural and functional properties arising from structural long-range disorder. Despite their …

Develo** novel amorphous alloys from the perspectives of entropy and shear bands

S Feng, ZQ Song, Y Zhang, Z Li, LM Wang… - Science China …, 2023 - Springer
Amorphous alloys are a new member of the amorphous material family. Due to the lack of
long-range ordering of atomic arrangement, amorphous alloys show unique properties …

Highly effective design of high GFA alloys with different metal-based and various components by machine learning

YC Tang, YF He, ZQ Fan, ZQ Wang… - Science China …, 2024 - Springer
The glass-forming ability (GFA) is of great significance for the development of novel
functional metal-based metallic glasses. In this study, seven popular machine learning (ML) …

Study on the structural origins of glass-forming ability and crystallization behaviors in NiNb binary alloys

W Lu, X Liu, H Jiang, J Tian, J Zhu, A Feng… - Materials …, 2024 - Elsevier
The limited glass forming ability (GFA) poses a pervasive challenge in different metallic
glass (MG) systems. This study systematically investigated the GFA of Nisingle bondNb …

Structural and dynamic heterogeneities in Cu50Zr50 and Ni50Zr50 Metallic Glasses

C Yang, M Sun - Journal of Non-Crystalline Solids, 2025 - Elsevier
Structural heterogeneity plays a key role in the dynamics and physical properties of metallic
glasses. To date, there has been a lack of an effective and general parameter to describe …

Deep-learning enabled atomic insights into the phase transitions and nanodomain topology of lead-free (K,Na)NbO3 ferroelectrics

X Zhang, B Li, J Zou, H Liu, B Xu, K Liu - Science China Materials, 2024 - Springer
Lead-free K x Na1− x NbO3 (KNN) perovskites have garnered increasing attention due to
their exceptional ferropiezoelectric properties, which are effectively tuned via polymorphic …

Toward Interpreting the Thermally Activated β Dynamics in Metallic Glass Using the Structural Constraint Neural Network

X Jiang, Z Tian, K Li, W Hu - The Journal of Physical Chemistry …, 2024 - ACS Publications
It is crucial to unravel the structural factors influencing the dynamics of the amorphous solids.
Deep learning aids in navigating these complexities, while transparency issues persist …

Machine learning-based prediction of mechanical properties of N-doped γ-graphdiyne

C Zhang, B Yang, Z Peng, S Chen - Science China Materials, 2024 - Springer
Nitrogen-doped γ-graphdiyne (N-GDY) has promising applications in energy, electronic
devices, and catalysis, but its properties vary significantly with the distribution of N-dopants …

Accurate prediction of magnetocaloric effect in NiMn-based Heusler alloys by prioritizing phase transitions through explainable machine learning

YC Tang, KY Cao, RN Ma, JB Wang, Y Zhang… - Rare Metals, 2024 - Springer
With the rapid development of artificial intelligence, magnetocaloric materials as well as
other materials are being developed with increased efficiency and enhanced performance …

Understanding dislocation velocity in TaW using explainable machine learning

A Kedharnath, R Kapoor, A Sarkar - Tungsten, 2024 - Springer
The present work calculated the velocity of edge dislocations in the Ta–W system using
molecular dynamics (MD) simulations and through machine learning (ML), identified the key …