# Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys

@article{JafaryZadeh2019ApplyingAM, title={Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys}, author={Mehdi Jafary-Zadeh and Khoong Hong Khoo and Robert Laskowski and Paulo Sergio Branicio and Alexander V. Shapeev}, journal={Journal of Alloys and Compounds}, year={2019} }

## 25 Citations

Lattice dynamics simulation using machine learning interatomic potentials

- Physics, Materials Science
- 2020

Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials

- Materials Science
- 2021

Navigating the Complex Compositional Landscape of High-Entropy Alloys

- Materials Science
- 2020

High-entropy alloys, which exist in the high-dimensional composition space, provide enormous unique opportunities for realizing unprecedented structural and functional properties. A fundamental…

Machine learning for interatomic potential models.

- Computer ScienceThe Journal of chemical physics
- 2020

An overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression are included.

Frontiers in atomistic simulations of high entropy alloys

- Materials ScienceJournal of Applied Physics
- 2020

The field of atomistic simulations of multicomponent materials and high entropy alloys is progressing rapidly, with challenging problems stimulating new creative solutions. In this Perspective, we…

Composition design of high-entropy alloys with deep sets learning

- Materials Sciencenpj Computational Materials
- 2022

High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by…

The MLIP package: moment tensor potentials with MPI and active learning

- Computer ScienceMach. Learn. Sci. Technol.
- 2021

This paper illustrates how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, and how expanding theTraining set changes the error of predictions.

Making thermal rate constant calculations reliable using best practices: case study of OH + HBr $\to$ Br + H$_2$O

- Chemistry
- 2022

In the present work we apply the combination of Moment Tensor Potential (MTP) and Ring Polymer Molecular Dynamics (RPMD) to the calculation of the thermal rate constants of the OH + HBr → Br + H 2 O…

Machine learning for alloys

- Materials ScienceNature Reviews Materials
- 2021

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 of analysis,…

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