Accelerating the exploration of high‐entropy alloys: synergistic effects of integrating computational simulation and experiments
High‐entropy alloys (HEAs) are novel materials composed of multiple elements with nearly
equal concentrations and they exhibit exceptional properties such as high strength, ductility …
equal concentrations and they exhibit exceptional properties such as high strength, ductility …
Chemical randomness, lattice distortion and the wide distributions in the atomic level properties in high entropy alloys
DS Aidhy - Computational Materials Science, 2024 - Elsevier
High entropy alloys (HEAs) consist of multiple elements present in large proportions that are
randomly distributed on a crystal lattice. On the one hand, the presence of multiple elements …
randomly distributed on a crystal lattice. On the one hand, the presence of multiple elements …
Local charge distortion due to Cr in Ni-based concentrated alloys
J Fischer, DS Aidhy - Acta Materialia, 2024 - Elsevier
Due to the presence of multiple elements consisting of a range of atomic radii, local lattice
distortion (LLD) is commonly observed in concentrated (and high entropy) alloys. However …
distortion (LLD) is commonly observed in concentrated (and high entropy) alloys. However …
[HTML][HTML] Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT
While high-entropy alloys (HEAs) present exponentially large compositional space for alloy
design, they also create enormous computational challenges to trace the compositional …
design, they also create enormous computational challenges to trace the compositional …
Exploring a general convolutional neural network-based prediction model for critical casting diameter of metallic glasses
J Hu, S Yang, J Mao, C Shi, G Wang, Y Liu… - Journal of Alloys and …, 2023 - Elsevier
Metallic glasses (MGs) as emerging amorphous materials have attracted considerable
interest due to their excellent mechanical, physical, and chemical properties. However, the …
interest due to their excellent mechanical, physical, and chemical properties. However, the …
Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning
Stacking fault energies (SFEs) are key parameters to understand the deformation
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …
Lean CNNs for Map** Electron Charge Density Fields to Material Properties
This work introduces a lean CNN (convolutional neural network) framework, with a
drastically reduced number of fittable parameters (< 81K) compared to the benchmarks in …
drastically reduced number of fittable parameters (< 81K) compared to the benchmarks in …
Accelerating Generalized Stacking Fault Energy Prediction by Combining Friedel Model, Ab Initio Calculation and Machine Learning
A Linda, MF Akhtar, S Pathak, S Bhowmick - Ab Initio Calculation and … - papers.ssrn.com
Stacking fault energies (SFEs) are key parameters to understand the deformation
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …
mechanisms in metals and alloys, and prior knowledge of SFEs from ab initio calculations is …