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 …
Imperfections are not 0 K: free energy of point defects in crystals
Defects determine many important properties and applications of materials, ranging from
doping in semiconductors, to conductivity in mixed ionic–electronic conductors used in …
doping in semiconductors, to conductivity in mixed ionic–electronic conductors used in …
Machine learning paves the way for high entropy compounds exploration: challenges, progress, and outlook
Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high
entropy compounds (HECs), which have gained worldwide attention due to their vast …
entropy compounds (HECs), which have gained worldwide attention due to their vast …
Machine learning studies for magnetic compositionally complex alloys: A critical review
Soft magnetic alloys play a critical role in power conversion, magnetic sensing, magnetic
storage and electric actuating, which are fundamental components of modern technological …
storage and electric actuating, which are fundamental components of modern technological …
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
[HTML][HTML] A machine learning approach for accelerated design of magnesium alloys. Part A: Alloy data and property space
Typically, magnesium alloys have been designed using a so-called hill-climbing approach,
with rather incremental advances over the past century. Iterative and incremental alloy …
with rather incremental advances over the past century. Iterative and incremental alloy …
Interpretable hardness prediction of high-entropy alloys through ensemble learning
YF Zhang, W Ren, WL Wang, N Li, YX Zhang… - Journal of Alloys and …, 2023 - Elsevier
With the development of artificial intelligence, machine learning has a wide range of
applications in the field of materials. The sparsity of data on the mechanical properties of …
applications in the field of materials. The sparsity of data on the mechanical properties of …
A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks
Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions
of composition and temperature, is essential for understanding alloy properties and …
of composition and temperature, is essential for understanding alloy properties and …
[HTML][HTML] Prediction and design of high hardness high entropy alloy through machine learning
W Ren, YF Zhang, WL Wang, SJ Ding, N Li - Materials & Design, 2023 - Elsevier
Two data-driven machine learning (ML) models were proposed for the hardness prediction
of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs …
of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs …
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
Abstract Characterization of material structure with X-ray or neutron scattering using eg Pair
Distribution Function (PDF) analysis most often rely on refining a structure model against an …
Distribution Function (PDF) analysis most often rely on refining a structure model against an …