Machine learning in concrete science: applications, challenges, and best practices
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …
human development. Despite conceptual and methodological progress in concrete science …
Advancing the mechanical performance of glasses: perspectives and challenges
Glasses are materials that lack a crystalline microstructure and long‐range atomic order.
Instead, they feature heterogeneity and disorder on superstructural scales, which have …
Instead, they feature heterogeneity and disorder on superstructural scales, which have …
Beyond the average: spatial and temporal fluctuations in oxide glass-forming systems
Atomic structure dictates the performance of all materials systems; the characteristic of
disordered materials is the significance of spatial and temporal fluctuations on composition …
disordered materials is the significance of spatial and temporal fluctuations on composition …
[HTML][HTML] Recent advances in utility of artificial intelligence towards multiscale colloidal based materials design
AA Moud - Colloid and Interface Science Communications, 2022 - Elsevier
Colloidal material design necessitates a collection of computer approaches ranging from
quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) …
quantum chemistry to molecular dynamics and continuum modeling. Machine learning (ML) …
[HTML][HTML] High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm
C Wang, X Wei, D Ren, X Wang, W Xu - Materials & Design, 2022 - Elsevier
Creep-oriented alloy design is a long-standing interesting topic in the field of metal structural
materials. However, the high cost for creep testing limits the development efficiency of new …
materials. However, the high cost for creep testing limits the development efficiency of new …
Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator
Many-body dynamics of atoms such as glass dynamics is generally governed by complex
(and sometimes unknown) physics laws. This challenges the construction of atom dynamics …
(and sometimes unknown) physics laws. This challenges the construction of atom dynamics …
Spatial clustering of microscopic dynamics governs the slip avalanche of sheared granular materials
Establishing quantifiable links between individual-particle dynamics and macroscopic
response of granular materials has been a longstanding challenge, with implications in …
response of granular materials has been a longstanding challenge, with implications in …
Predicting fracture propensity in amorphous alumina from its static structure using machine learning
Thin films of amorphous alumina (a-Al2O3) have recently been found to deform permanently
up to 100% elongation without fracture at room temperature. If the underlying ductile …
up to 100% elongation without fracture at room temperature. If the underlying ductile …
[HTML][HTML] Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine …
Q Chen, G Hu, J Wu - Case Studies in Construction Materials, 2024 - Elsevier
With the development of green, low-carbon, and sustainable economic systems, the issues
of high pollution and energy consumption in construction materials have become …
of high pollution and energy consumption in construction materials have become …
[HTML][HTML] Controlling factor for fracture resistance and ionic conduction in glassy lithium borophosphate electrolytes
Glasses are promising candidates as solid electrolytes for all-solid-state batteries due to
their isotropic ionic conduction, formability, as well as high chemical, thermal and …
their isotropic ionic conduction, formability, as well as high chemical, thermal and …