Search

Finite-temperature properties such as thermodynamic state functions can be obtained for a range of crystalline materials by combining density functional theory (DFT) with lattice-dynamics approaches. Despite the usefulness of such first-principles predictions, their results must be carefully checked for accuracy, especially in cases where the DFT description of the material itself is nontrivial. Here, we investigate a prototypical layered semiconductor, namely, germanium selenide (GeSe) by dispersion-corrected DET, lattice-dynamics computations, and a thermodynamic framework that relies on the quasiharmonic approximation (QHA). We study phonon band structures, their evolution under pressure, and finite-temperature thermodynamic state functions. Besides the layered orthorhombic structure, this analysis includes the high-temperature cubic (rocksalt-type) polymorph of GeSe, which is shown to exhibit imaginary vibrational modes but emerging dynamic stability under increasing external pressure. The effect of these imaginary vibrational modes on the QHA and hence on computed thermochemical properties is critically evaluated. First-principles thermodynamics correctly predict a high-temperature transition from the orthorhombic to the cubic structure, albeit the transition temperature is severely underestimated. This simple compound allows us to address important methodological questions regarding the QHA treatment of crystalline solids. [ABSTRACT FROM AUTHOR]

We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp³ fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp³ content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp³ fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power. [ABSTRACT FROM AUTHOR]

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery. [ABSTRACT FROM AUTHOR]

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical potential. Exemplary applications of the GAP model to surfaces of "diamondlike" tetrahedral amorphous carbon (ta-C) are presented, including an estimate of the amorphous material's surface energy and simulations of high-temperature surface reconstructions ("graphitization"). The presented interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures. [ABSTRACT FROM AUTHOR]