Machine learning for structural materials
The development of structural materials with outstanding mechanical response has long
been sought for innumerable industrial, technological, and even biomedical applications …
been sought for innumerable industrial, technological, and even biomedical applications …
Explainable machine learning algorithms for predicting glass transition temperatures
Modern technologies demand the development of new glasses with unusual properties.
Most of the previous developments occurred by slow, expensive trial-and-error approaches …
Most of the previous developments occurred by slow, expensive trial-and-error approaches …
Discovery of novel quaternary bulk metallic glasses using a developed correlation-based neural network approach
M Samavatian, R Gholamipour… - Computational Materials …, 2021 - Elsevier
The immense space of composition-processing parameters leads to numerous trial-and-
error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle …
error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle …
Engineering of novel Fe-based bulk metallic glasses using a machine learning-based approach
TC Chen, R Rajiman, M Elveny, JWG Guerrero… - Arabian Journal for …, 2021 - Springer
A broad range of potential chemical compositions makes difficult design of novel bulk
metallic glasses (BMGs) without performing expensive experimentations. To overcome this …
metallic glasses (BMGs) without performing expensive experimentations. To overcome this …
[图书][B] Data-driven evolutionary modeling in materials technology
N Chakraborti - 2022 - taylorfrancis.com
Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are
used in learning and modeling especially with the advent of big data related problems. This …
used in learning and modeling especially with the advent of big data related problems. This …
Machine learning prediction of the critical cooling rate for metallic glasses from expanded datasets and elemental features
BT Afflerbach, C Francis, LE Schultz… - Chemistry of …, 2022 - ACS Publications
We use a random forest (RF) model to predict the critical cooling rate (RC) for glass
formation of various alloys from features of their constituent elements. The RF model was …
formation of various alloys from features of their constituent elements. The RF model was …
Which glass stability parameters can assess the glass‐forming ability of oxide systems?
Glass‐forming ability (GFA) is a property of utmost importance in glass science and
technology. In this paper, we used a statistical methodology—involving bootstrap sampling …
technology. In this paper, we used a statistical methodology—involving bootstrap sampling …
Evolutionary design of machine-learning-predicted bulk metallic glasses
RM Forrest, AL Greer - Digital Discovery, 2023 - pubs.rsc.org
The size of composition space means even coarse grid-based searches for interesting
alloys are infeasible unless heavily constrained, which requires prior knowledge and …
alloys are infeasible unless heavily constrained, which requires prior knowledge and …
Data-Driven Phase Selection, Property Prediction and Force-Field Development in Multi-Principal Element Alloys
Abstract Multi-Principal Element Alloys (MPEAs) have brought a paradigm shift in the alloy
design process and pose a significant challenge due to the astronomical and compositional …
design process and pose a significant challenge due to the astronomical and compositional …
Thermodynamic evaluation and experimental verification of the glass forming ability of Cu-Zr-based alloys
R Yuan, Z Yu, H Leng, K Chou - Journal of Non-Crystalline Solids, 2021 - Elsevier
The prediction of glass forming ability (GFA) of alloys is of vital importance for the
development of novel amorphous alloys. In this paper, the GFA of Cu-Zr-based alloys was …
development of novel amorphous alloys. In this paper, the GFA of Cu-Zr-based alloys was …