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We provide a methodology for generating interatomic potentials for use in classical molecular dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high energy…

Materials Science · Physics 2009-12-03 Pratyush Tiwary , Axel van de Walle , Niels Grønbech-Jensen

We develop an empirical potential for silicon which represents a considerable improvement over existing models in describing local bonding for bulk defects and disordered phases. The model consists of two- and three-body interactions with…

Materials Science · Physics 2016-08-31 Joao F. Justo , Martin Z. Bazant , Efthimios Kaxiras , V. V. Bulatov , Sidney Yip

Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it takes a long time due to the large amount of calculation.…

Materials Science · Physics 2024-02-28 Hyun Gyu Park , Soohaeng Yoo Willow , D. ChangMo Yang , Chang Woo Myung

We have developed the quasiparticle self-consistent GW (QSGW) method based on a recently developed mixed basis all-electron full-potential method (the PMT method), which uses the augmented plane waves (APWs) and the highly localized…

Materials Science · Physics 2014-08-18 Takao Kotani

Routine investigations of plasmonic phenomena at the quantum level present a formidable computational challenge due to the large system sizes and ultrafast timescales involved. This Feature Article highlights the use of density functional…

Mesoscale and Nanoscale Physics · Physics 2025-06-19 Nikhil S. Chellam , Subhajyoti Chaudhuri , Abhisek Ghosal , Sajal K. Giri , George C. Schatz

We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density…

Materials Science · Physics 2020-10-06 Fabian L. Thiemann , Patrick Rowe , Erich A. Müller , Angelos Michaelides

The coefficients of interatomic potential of simple form Exp-6 for neon are obtained. Repulsive part is calculated ab-initio in the Hartree-Fock approximation using the basis of atomic orbitals orthogonalized exactly on different lattice…

Chemical Physics · Physics 2007-05-23 Yu. V. Eremeichenkova , L. S. Metlov , A. F. Morozov

We study the tight-binding dispersion of the recently discovered superconductor K2Cr3As3, obtained from Wannier projection of Density Functional Theory (DFT) results. In order to establish quantitatively the actual degree of quasi-one…

The applicability of the density functional based tight binding (DFTB) method to the description of hydrogen bond dynamics and infrared spectroscopy is addressed for the exemplary protic ionic liquid triethylammonium nitrate. Potential…

Chemical Physics · Physics 2017-01-04 Tobias Zentel , Oliver Kühn

In the band theory, first-principles calculations, the tight-binding method and the effective $k\cdot p$ model are usually employed to investigate the electronic structure of condensed matters. The effective $k\cdot p$ model has a compact…

Materials Science · Physics 2021-06-25 Guohui Zhan , Minji Shi , Zhilong Yang , Haijun Zhang

First-principles alloy theory, formulated within the exact muffin-tin orbitals method in combination with the coherent-potential approximation, is used to study the mechanical properties of ferromagnetic body-centered cubic (bcc)…

We present an efficient approach to precisely simulate tight binding models with optical lattices, based on programmable digital-micromirror-device (DMD) techniques. Our approach consists of a subroutine of Wegner-flow enabled precise…

Quantum Gases · Physics 2020-10-20 Xingze Qiu , Jie Zou , Xiaodong Qi , Xiaopeng Li

Maximally localized Wannier functions (MLWFs) based on Kohn-Sham band-structures provide a systematic way to construct realistic, materials specific tight-binding models for further theoretical analysis. Here, we construct MLWFs for the Mn…

Strongly Correlated Electrons · Physics 2013-05-29 Roman Kovacik , Claude Ederer

To analyze the electronic band structure of a two-dimensional (2D) crystal under a commensurate perpendicular magnetic field, tight-binding (TB) Hamiltonians are typically constructed using a magnetic unit cell (MUC), which is composed of…

Mesoscale and Nanoscale Physics · Physics 2024-10-17 Seungwon Jung

We develop a method to fit high-temperature Gibbs free energy data for the development of interatomic potentials for atomic systems. The approach is based on Hamiltonian thermodynamic integration, enabling the identification of suitable…

Materials Science · Physics 2026-03-04 Liangrui Wei , Yang Sun

Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and…

Materials Science · Physics 2025-05-20 Jiantao Wang , Peitao Liu , Heyu Zhu , Mingfeng Liu , Hui Ma , Yun Chen , Yan Sun , Xing-Qiu Chen

We present an accurate ab initio tight-binding model, capable of describing the dynamics of Dirac points in tunable honeycomb optical lattices following a recent experimental realization [L. Tarruell et al., Nature 483, 302 (2012)]. Our…

The parameters of many-body potentials for Co, Nb and Zr metals, based on the embedded-atom method, have been systematically derived. The analytical potential scheme allows us to reproduce correctly the cohesive energies and structural…

Materials Science · Physics 2014-05-02 Pascal Thibaudeau , Julian Gale

Amorphous silicon nitride (a-SiN) is a material which has found wide application due to its excellent mechanical and electrical properties. Despite the significant effort devoted in understanding how the microscopic structure influences the…

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the…

Computational Physics · Physics 2020-08-06 Anh Tran , Julien Tranchida , Tim Wildey , Aidan P. Thompson