# Accurate representation of formation energies of crystalline alloys with many components

@article{Shapeev2017AccurateRO,
title={Accurate representation of formation energies of crystalline alloys with many components},
author={Alexander V. Shapeev},
journal={Computational Materials Science},
year={2017},
volume={139},
pages={26-30}
}
• A. Shapeev
• Published 11 December 2016
• Materials Science
• Computational Materials Science

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## References

SHOWING 1-10 OF 26 REFERENCES
Accuracy and transferability of Gaussian approximation potential models for tungsten
• Materials Science
• 2014
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a database of first-principles density
Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error
• Materials Science
• 2017
This work studied over one hundred different Hamiltonians and identified a heuristic, based on a normalized mean-squared displacement of atomic positions in a crystal, to determine if the effects of relaxation in CE data are too severe to build a reliable CE model.
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
This paper considers the problem of constructing interatomic potentials that approximate a given quantum-mechanical interaction model, and proposes a new class of systematically improvable potentials which are proposed, analyzed, and tested on an existing quantum- mechanical database.
Nanostructured High‐Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes
• Materials Science
• 2004
A new approach for the design of alloys is presented in this study. These high-entropy alloys with multi-principal elements were synthesized using well-developed processing technologies. Preliminary
Perspective: Machine learning potentials for atomistic simulations.
• J. Behler
• Materials Science
The Journal of chemical physics
• 2016
Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations, which are reviewed along with a discussion of their current applicability and limitations.
Learning to Predict Physical Properties using Sums of Separable Functions
• Mathematics, Computer Science
SIAM J. Sci. Comput.
• 2011
An algorithm for learning the function that maps a material structure to its value on some property, given the value of this function on several structures is presented, following the paradigm of separated representations.
Foundations and Practical Implementations of the Cluster Expansion
Different versions of the cluster expansion are explored using the Mo-Ta system as an example. One of the objectives of this work is to establish a clear distinction between phenomenological