Machine learning for alloys
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …
data-science-inspired work. The dawn of computational databases has made the integration …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
Recent applications of machine learning in alloy design: A review
The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …
[HTML][HTML] A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys
There is a pressing need to shorten the development period for new materials possessing
desired properties. For example, bulk metallic glasses (BMGs) are a unique class of alloy …
desired properties. For example, bulk metallic glasses (BMGs) are a unique class of alloy …
A machine learning approach for engineering bulk metallic glass alloys
Bulk metallic glasses (BMGs) are a unique class of materials that are gaining traction in a
wide variety of applications due to their attractive physical properties. One limitation to the …
wide variety of applications due to their attractive physical properties. One limitation to the …
General trends in core–shell preferences for bimetallic nanoparticles
Surface segregation phenomena dictate core–shell preference of bimetallic nanoparticles
and thus play a crucial role in the nanoparticle synthesis and applications. Although it is …
and thus play a crucial role in the nanoparticle synthesis and applications. Although it is …
Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning
High entropy alloys and amorphous metallic alloys represent two distinct classes of
advanced alloy materials, each with unique structural characteristics. Their emergence has …
advanced alloy materials, each with unique structural characteristics. Their emergence has …
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 …
Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications
JY Zhang, ZQ Zhou, ZB Zhang, MH Park, Q Yu… - Materials …, 2022 - iopscience.iop.org
Metallic glasses (MGs) or amorphous alloys are an important engineering material that has
a history of research of about 80–90 years. While different fast cooling methods were …
a history of research of about 80–90 years. While different fast cooling methods were …
A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys
Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength
(UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established …
(UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established …