[HTML][HTML] Current application status of multi-scale simulation and machine learning in research on high-entropy alloys
High-entropy alloys (HEAs) have garnered significant attention across various fields owing
to their unique design incorporating multi-principal elements and remarkable …
to their unique design incorporating multi-principal elements and remarkable …
Review on applications of artificial neural networks to develop high entropy alloys: A state-of-the-art technique
Compared to conventional alloys, multicomponent high-entropy alloys (HEAs) have
received considerable attention in recent years owing to their exceptional phase stability …
received considerable attention in recent years owing to their exceptional phase stability …
Large language models as molecular design engines
The design of small molecules is crucial for technological applications ranging from drug
discovery to energy storage. Due to the vast design space available to modern synthetic …
discovery to energy storage. Due to the vast design space available to modern synthetic …
[HTML][HTML] Enhancing the mechanical properties of high-entropy alloys through severe plastic deformation: A review
High-entropy alloys (HEAs) are one of the breakthroughs in the past decade in alloy
development that have the potential to exhibit outstanding physical, mechanical, and …
development that have the potential to exhibit outstanding physical, mechanical, and …
Tribo-informatics analysis of in-situ TiC reinforced ZA27 alloy: Microstructural insights and wear performance modeling by machine learning
KA Sheikh, MM Khan - Tribology International, 2024 - Elsevier
The study examines the performance of ZA27 alloy reinforced with in-situ TiC under high-
stress abrasive wear conditions. The ZA27+ 10 wt% TiC composite demonstrates superior …
stress abrasive wear conditions. The ZA27+ 10 wt% TiC composite demonstrates superior …
[HTML][HTML] Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning
Data-driven modelling, such as machine learning (ML), has great potential to streamline the
complexity involved in designing new alloys. However, such powerful predictive models …
complexity involved in designing new alloys. However, such powerful predictive models …
[HTML][HTML] Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization
X Mi, L Dai, X **g, J She, B Holmedal, A Tang… - Journal of Magnesium …, 2024 - Elsevier
Magnesium (Mg), being the lightest structural metal, holds immense potential for widespread
applications in various fields. The development of high-performance and cost-effective Mg …
applications in various fields. The development of high-performance and cost-effective Mg …
Recent machine learning-driven investigations into high entropy alloys: a comprehensive review
The exploration of high entropy alloys (HEAs) primarily relies on trial-and-error experiments
and multiscale modelling, which are time-consuming and resource-intensive. Recently …
and multiscale modelling, which are time-consuming and resource-intensive. Recently …
[HTML][HTML] 3D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters
Fine tuning of the material properties requires many trials and errors during the synthesis.
The metal nanoparticles undergo several stages of reduction, clustering, coalescence and …
The metal nanoparticles undergo several stages of reduction, clustering, coalescence and …
Revolutionising inverse design of magnesium alloys through generative adversarial networks
The utility of machine learning (ML) techniques in materials science has accelerated
materials design and discovery. However, the accuracy of ML models-particularly deep …
materials design and discovery. However, the accuracy of ML models-particularly deep …