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Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

Nataliya Lopanitsyna1*, Chiheb Ben Mahmoud1*, Michele Ceriotti1

1 Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Corresponding authors emails: nataliya.lopanitsyna@epfl.ch, chiheb.benmahmoud@epfl.ch
DOI10.24435/materialscloud:vk-qd [version v1]

Publication date: Nov 16, 2021

How to cite this record

Nataliya Lopanitsyna, Chiheb Ben Mahmoud, Michele Ceriotti, Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime, Materials Cloud Archive 2021.192 (2021), doi: 10.24435/materialscloud:vk-qd.


Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions. Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling. We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost. We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy. The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.

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File name Size Description
4.8 MiB Zipped TAR archive with all the necessary files to run a simulation with a neural network potential for Ni or train it from scratch


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Preprint where the neural network potential trained on the data is discussed)


machine learning materials science EPFL metal alloys molecular dynamics simulation neural network potentials electronic density of states MARVEL/DD2 SNSF Cosmo

Version history:

2021.192 (version v1) [This version] Nov 16, 2021 DOI10.24435/materialscloud:vk-qd