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Modeling the Ga/As binary system across temperatures and compositions from first principles

Giulio Imbalzano1*, Michele Ceriotti1*

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

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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.


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.

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File name Size Description
44.1 MiB The file contains the potentials, the database of structures created, examples to run MD simulations and a chemiscope visualization
940 Bytes A brief description of the content of the zip file


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Journal reference (The paper in which the material and the results obtained with it are presented.)


Molecular dynamics simulation Gallium arsenide Neural network potential GaAs SNSF MARVEL

Version history:

2021.226 (version v2) Dec 20, 2021 DOI10.24435/materialscloud:tb-dq
2021.95 (version v1) [This version] Jun 28, 2021 DOI10.24435/materialscloud:pr-mg