Artificial neural network correction for density-functional tight-binding molecular dynamics simulations
نویسندگان
چکیده
منابع مشابه
Tight-binding molecular dynamics simulations
We present the tight-binding molecular dynamics (TBMD) scheme and describe its numerical implementation in a serial FORTRAN-77 code. We discuss how to organize a typical simulation and to control the I/O. An analysis of the computational workload is also presented and discussed. Ó 1998 Elsevier Science B.V. All rights reserved.
متن کاملDensity functional tight binding.
This paper reviews the basic principles of the density-functional tight-binding (DFTB) method, which is based on density-functional theory as formulated by Hohenberg, Kohn and Sham (KS-DFT). DFTB consists of a series of models that are derived from a Taylor series expansion of the KS-DFT total energy. In the lowest order (DFTB1), densities and potentials are written as superpositions of atomic ...
متن کاملNonadiabatic dynamics within time-dependent density functional tight binding method.
A nonadiabatic molecular dynamics is implemented in the framework of the time-dependent density functional tight binding method (TDDFTB) combined with Tully's stochastic surface hopping algorithm. The applicability of our method to complex molecular systems is illustrated on the example of the ultrafast excited state dynamics of microsolvated adenine. Our results demonstrate that in the presenc...
متن کاملDensity-functional tight-binding for beginners
This article is a pedagogical introduction to density-functional tight-binding (DFTB) method. We derive it from the density-functional theory, give the details behind the tight-binding formalism, and give practical recipes for parametrization: how to calculate pseudo-atomic orbitals and matrix elements, and especially how to systematically fit the short-range repulsions. Our scope is neither to...
متن کاملEffect of Curvature on the Mechanical Properties of Graphene: A Density Functional Tight-binding Approach
Due to the high cost of experimental analyses, researchers used atomistic modeling methods for predicting the mechanical behavior of the materials in the fields of nanotechnology. In the pre-sent study the Self-Consistent Charge Density Functional Tight-Binding (SCC-DFTB) was used to calculate Young's moduli and average potential energy of the straight and curved graphenes with different curvat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: MRS Communications
سال: 2019
ISSN: 2159-6859,2159-6867
DOI: 10.1557/mrc.2019.80