Abstract
Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal–multibaric simulations. Here we show that the relative equilibrium between liquid gallium, α-Ga, β-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into α-Ga and β-Ga are studied. We find that the formation of metastable β-Ga is kinetically favored over the thermodinamically stable α-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga.
Highlights
Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist
We find that the obtained neural network (NN) force field can describe the structural and other related properties of α-Ga, β-Ga, Ga-II, and liquid gallium well
In this work, we have combined a number of state-of-the-art computational techniques in order to construct a NN force field for gallium, which has many complex bonding and structural properties
Summary
Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal–multibaric simulations. Micrometer-sized or submicrometer-size liquid gallium could be undercooled down to 150 K without solidification[4,9,10,11] In such scenario, the crystallization does not produce the stable α phase but mostly the β-Ga structure. As far as describing accurately the Ga interaction is concerned, an ab initio description is called for but running first-principles molecular dynamics (MD) is prohibitively expensive The solution to this conundrum has been first suggested by Behler and Parrinello[18] and consists in training a neural network (NN) on a large number of appropriately selected set of configurations. By comparing the nucleation properties of α-Ga and β-Ga, we find that the formation of metastable β-Ga is kinetically favored over the thermodynamically stable α-Ga above 174 K
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