Abstract: Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors—atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors—using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Abstract: Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors—atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors—using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Abstract: In the current work, the issue of re-parameterization of Tersoff empirical potential model was addressed for 2D nanomaterial ‘germanene’ to be applied in molecular dynamics simulation based studies. The well-known chi-square minimization procedure was used to optimize the original Tersoff potential parameters. Many properties of germanene were extracted using both original and optimized Tersoff potentials and they compared with the corresponding density functional theory data. According to the results, the optimized Tersoff potential provides a significant improvement in many structural, thermodynamic, mechanical, and thermal properties of geramanene.

Abstract: A semi-empirical interatomic potential for germanium has been developed based on the modified embedded-atom method (MEAM) formalism. The new potential describes various fundamental physical properties of germanium: elastic, structural, point defect, surface, thermal properties (except melting point), etc., in better agreement with experimental data or first principles calculations than any other empirical potential ever developed. When compared to the previously developed MEAM Ge potential [M.I. Baskes, J.S. Nelson, A.F. Wright, Phys. Rev. B 40 (1989) 6085], certain improvements are made in descriptions of surface relaxations, point defects, thermal expansion and amorphous structure. The potential has the same formalism as already developed MEAM potentials for bcc, fcc and hcp elements, and can be easily extended to describe various metal–silicon multi-component systems.

Abstract: Based on the idea that bonding energies of many substances can be modeled by pairwise interactions moderated by the local environment, we propose a new universal interatomic potential for tetrahedrally bonded materials. We obtain two basic relationships linking equilibrium interatomic distances and cohesive energies to the coordination number for a large range of phases of silicon. The relationships are also valid for germanium and carbon, covering, in the latter case, double and triple carbon-carbon bonds, where π bonding is important. Based on these ideas we discuss the construction of the universal interatomic potential for these three substances. This potential, which uses very few parameters, should be useful, particularly for surface studies.

Abstract: A general form is proposed for an empirical interatomic potential for multicomponent systems. This form interpolates between potentials for the respective elements to treat heteronuclear bonds. The approach is applied to C-Si and Si-Ge systems. In particular, the properties of SiC and its defects are well described.

Abstract: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_xSe_1-x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge-dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

Abstract: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_xSe_1-x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge-dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

Abstract: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_xSe_1-x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge-dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.