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Manufacturing of metallic glass components: Processes, structures and properties
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 …
structural and functional properties arising from structural long-range disorder. Despite their …
Develo** novel amorphous alloys from the perspectives of entropy and shear bands
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 …
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) …
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
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 …
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 …
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 …
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 …
Deep learning aids in navigating these complexities, while transparency issues persist …
Machine learning-based prediction of mechanical properties of N-doped γ-graphdiyne
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 …
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
With the rapid development of artificial intelligence, magnetocaloric materials as well as
other materials are being developed with increased efficiency and enhanced performance …
other materials are being developed with increased efficiency and enhanced performance …
Understanding dislocation velocity in TaW using explainable machine learning
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 …
molecular dynamics (MD) simulations and through machine learning (ML), identified the key …