Giulio Imbalzano^1*, Michele Ceriotti^1*

1 Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland

* Corresponding authors emails: giulio.imbalzano@epfl.ch, michele.ceriotti@epfl.ch

DOI10.24435/materialscloud:pr-mg [version v1]

Publication date: Jun 28, 2021

How to cite this record

Giulio Imbalzano, Michele Ceriotti, Modeling the Ga/As binary system across temperatures and compositions from first principles, Materials Cloud Archive 2021.95 (2021), doi: 10.24435/materialscloud:pr-mg.

Description

Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the Ga/As system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably determine the uncertainty induced by the limitations of the machine-learning model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling.

Materials Cloud sections using this data

Files

File name	Size	Description
gaas-data.zip MD5md5:6bff02119b875ce2ab436d319894a872	44.1 MiB	The file contains the potentials, the database of structures created, examples to run MD simulations and a chemiscope visualization
README.txt MD5md5:a651c74a5d5bdd9287f6cedf461d4490	940 Bytes	A brief description of the content of the zip file

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

Keywords

Molecular dynamics simulation Gallium arsenide Neural network potential GaAs SNSF MARVEL