Settling the matter of the role of vibrations in the stability of high-entropy carbides
High-entropy ceramics are attracting significant interest due to their exceptional chemical
stability and physical properties. While configurational entropy descriptors have been …
stability and physical properties. While configurational entropy descriptors have been …
LModeA-nano: A pymol plugin for calculating bond strength in solids, surfaces, and molecules via local vibrational mode analysis
The analysis of chemical bonding in crystal structures and surfaces is an important research
topic in theoretical chemistry. In this work, we present a PyMOL plugin, named LModeA …
topic in theoretical chemistry. In this work, we present a PyMOL plugin, named LModeA …
[HTML][HTML] A machine learning framework for elastic constants predictions in multi-principal element alloys
N Linton, DS Aidhy - APL Machine Learning, 2023 - pubs.aip.org
On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in
alloy design due to large compositional space, whereas on the other, they have presented …
alloy design due to large compositional space, whereas on the other, they have presented …
[HTML][HTML] Charge-density based evaluation and prediction of stacking fault energies in Ni alloys from DFT and machine learning
A combination of high strength and high ductility has been observed in multi-principal
element alloys due to twin formation attributed to low stacking fault energy (SFE). In the …
element alloys due to twin formation attributed to low stacking fault energy (SFE). In the …
Rapid prediction of phonon structure and properties using the atomistic line graph neural network (ALIGNN)
The phonon density of states (DOS) summarizes the lattice vibrational modes supported by
a structure and gives access to rich information about the material's stability, thermodynamic …
a structure and gives access to rich information about the material's stability, thermodynamic …
Machine learning based methodology to predict point defect energies in multi-principal element alloys
Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many
principal elements randomly distributed on a crystal lattice. The random presence of many …
principal elements randomly distributed on a crystal lattice. The random presence of many …
Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys
A descriptor-less machine learning (ML) model based only on charge density images
extracted from density functional theory (DFT) is developed to predict stacking fault energies …
extracted from density functional theory (DFT) is developed to predict stacking fault energies …
Low-Cost Vibrational Free Energies in Solid Solutions with Machine Learning Force Fields
The rational design of alloys and solid solutions relies on accurate computational
predictions of phase diagrams. The cluster expansion method has proven to be a valuable …
predictions of phase diagrams. The cluster expansion method has proven to be a valuable …
[HTML][HTML] Elastic constants from charge density distribution in FCC high-entropy alloys using CNN and DFT
H Mirzaee, R Soltanmohammadi, N Linton… - APL Machine …, 2024 - pubs.aip.org
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 …
Support vector machine-based phase prediction of multi-principal element alloys
NH Chau, M Kubo, LV Hai… - Vietnam Journal of …, 2023 - World Scientific
Designing new materials with desired properties is a complex and time-consuming process.
One of the most challenging factors of the design process is the huge search space of …
One of the most challenging factors of the design process is the huge search space of …