Daniele Dragoni^1,2*, Tom Daff³, Gabor Csanyi³, Nicola Marzari¹

1 Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2 Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, Via R. Cozzi 55, I-20125 Milano, Italy

3 Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

* Corresponding authors emails: daniele.dragoni@unimib.it

DOI10.24435/materialscloud:2017.0006/v1 [version v1]

Publication date: Jun 21, 2017

How to cite this record

Daniele Dragoni, Tom Daff, Gabor Csanyi, Nicola Marzari, Gaussian Approximation Potentials for iron from extended first-principles database (Data Download), Materials Cloud Archive 2017.0006/v1 (2017), https://doi.org/10.24435/materialscloud:2017.0006/v1

Description

Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are designed using functional forms driven by physical intuition and fitted to experimental or computational data. The moderate flexibility of these functional forms limits their ability to be systematically improved by increasing the fitting datasets; on the other hand, their qualitative description of the essential physical interactions ensures a modicum degree of transferability. Recently, a novel trend has emerged where potential-energy surfaces are represented by neural networks fitted on large numbers of first-principles calculations, thus maximizing flexibility but requiring extensive datasets to ensure transferability. Gaussian Approximation Potentials in particular are a novel class of potentials based on non-linear, non-parametric Gaussian-process regression. Here we generate a Gaussian Approximation model for the α-phase of iron training on energies, stresses and forces taken from first-principles molecular dynamics simulations of pristine and defected bulk systems, of surfaces and γ-surfaces with different crystallographic orientations.

Materials Cloud sections using this data

Files

File name	Size	Description
DB_bccFe_Dragoni.tar.gz MD5md5:64e7a4f996e56f90e80ef9b0e10f1a72	5.4 MiB	Training database for α-iron: the database contains atomic positions and cell vectors in Cartesian coordinates along with the corresponding energies (plus forces or stresses when available) obtained from first-principles calculations. Data are reported in a XYZ format. The database can be partitioned into 8 sub-databases covering different local atomic environments as discussed in the README.txt included in the download file. Approximately 100,000 environments are included. All ab-initio calculations are performed with the Quantum Espresso package using an ultrasoft pseudopotential from the PSlibrary which best reproduces all-electron equilibrium properties of the crystal. The choice of k-points sampling and cutoffs is crucial in order to generate a homogeneous and accurate database. These are selected as to ensure, across the entire database, a convergence of energy differences (per atom), forces and stresses within 1 meV, 0.01 eV/Ang and 0.1 GPa respectively. * DB1 - distorted primitive cells * DB2 - bulk vibrations * DB3 - monovacancy * DB4 - double vacancies * DB5 - tri-vacancies and "small" cluster-vacancies * DB6 - Self-interstitials * DB7 - Bulk-terminated surfaces (100, 110, 111, 211) * DB8 - gamma surfaces (110, 211)

License

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

No external references available for this Materials Cloud Archive record.

Keywords

Machine-learning α-iron Gaussian approximation potentials artificial neural networks interatomic potentials MARVEL