scholarly journals Density-Functional Tight-Binding Parameters for Bulk Zirconium: A Case Study for Repulsive Potentials

2021 ◽  
Vol 125 (10) ◽  
pp. 2184-2196
Author(s):  
Aulia Sukma Hutama ◽  
Chien-pin Chou ◽  
Yoshifumi Nishimura ◽  
Henryk A. Witek ◽  
Stephan Irle
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
Binding Parameters ◽  
Repulsive Potentials

scholarly journals Development of Density Functional Tight-Binding Parameters Using Relative Energy Fitting and Particle Swarm Optimization

2020 ◽  
Vol 16 (3) ◽  
pp. 1469-1481
Author(s):  
Néstor F. Aguirre ◽  
Amanda Morgenstern ◽  
M. J. Cawkwell ◽  
Enrique R. Batista ◽  
Ping Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Density Functional ◽  
Tight Binding ◽  
Particle Swarm ◽  
Relative Energy ◽  
Binding Parameters ◽  
Swarm Optimization

Electronic transport properties in the stable phase of a cumulene/B7/cumulene molecular bridge investigated using density functional theory and a tight-binding method

2019 ◽  
Vol 43 (42) ◽  
pp. 16515-16523 ◽  
Author(s):  
Mohammad Qasemnazhand ◽  
Farhad Khoeini ◽  
Sima Shekarforoush
Keyword(s):  
Density Functional Theory ◽  
Electronic Transport ◽  
Density Functional ◽  
Tight Binding ◽  
Stable Phase ◽  
Binding Parameters ◽  
Functional Theory ◽  
Tight Binding Method ◽  
Homo Lumo ◽  
Molecular Bridge

In this study, we first obtain the single-band tight-binding parameters of a B7 cluster in terms of matching the HOMO–LUMO levels obtained from density functional theory (DFT).

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

2019 ◽  
Author(s):  
Chiara Panosetti ◽  
Artur Engelmann ◽  
Lydia Nemec ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Gaussian Process ◽  
Density Functional ◽  
Functional Form ◽  
Tight Binding ◽  
Gaussian Process Regression ◽  
Direct Access ◽  
Interatomic Potentials ◽  
Global Parameter ◽  
Repulsive Potentials ◽  
The Cost

The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scales that are unfeasible with first principles DFT. At the same time (and in contrast to empirical interatomic potentials and force-fields), DFTB still offers direct access to electronic properties such as the band-structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter-sets could be routinely adjusted for a given project. While fairly robust and transferable parameterization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR). The use of GPR circumvents the need for non-linear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen and oxygen. Overall, the new approach removes focus from the choice of functional form and parameterization procedure, in favour of a data-driven philosophy.

scholarly journals Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

2018 ◽  
Vol 14 (5) ◽  
pp. 2341-2352 ◽  
Author(s):  
Julian J. Kranz ◽  
Maximilian Kubillus ◽  
Raghunathan Ramakrishnan ◽  
O. Anatole von Lilienfeld ◽  
Marcus Elstner
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Tight Binding ◽  
Unsupervised Machine Learning ◽  
Repulsive Potentials

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

2020 ◽  
Vol 16 (4) ◽  
pp. 2181-2191 ◽  
Author(s):  
Chiara Panosetti ◽  
Artur Engelmann ◽  
Lydia Nemec ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Gaussian Process ◽  
Density Functional ◽  
Tight Binding ◽  
Gaussian Process Regression ◽  
Repulsive Potentials

Self-Consistent-Charge Density-Functional Tight-Binding Parameters for Cd–X (X = S, Se, Te) Compounds and Their Interaction with H, O, C, and N

2011 ◽  
Vol 7 (7) ◽  
pp. 2262-2276 ◽  
Author(s):  
Sunandan Sarkar ◽  
Sougata Pal ◽  
Pranab Sarkar ◽  
A. L. Rosa ◽  
Th. Frauenheim
Keyword(s):  
Charge Density ◽  
Density Functional ◽  
Tight Binding ◽  
Binding Parameters ◽  
Self Consistent ◽  
C And N

scholarly journals Ab Initio Calculation of Tight-Binding Parameters

1997 ◽  
Vol 491 ◽  
Author(s):  
A. K. Mcmahan ◽  
J. E. Klepeis
Keyword(s):  
Ab Initio ◽  
Density Functional ◽  
Local Density ◽  
Tight Binding ◽  
Matrix Elements ◽  
Binding Parameters ◽  
Minimal Basis ◽  
The Common ◽  
Local Density Functional ◽  
Overlap Matrix

ABSTRACTWe calculate ab initio values of tight-binding parameters for the /-electron metal Ce and various phases of Si, from local-density functional one-electron Hamiltonian and overlap matrix elements. Our approach allows us to unambiguously test the validity of the common minimal basis and two-center approximations as well as to determine the degree of transferability of both nonorthogonal and orthogonal hopping parameters in the cases considered.

Metadynamics combined with auxiliary density functional and density functional tight-binding methods: alanine dipeptide as a case study

2017 ◽  
Vol 23 (3) ◽  
Author(s):  
Jerome Cuny ◽  
Kseniia Korchagina ◽  
Chemseddine Menakbi ◽  
Tzonka Mineva
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
Alanine Dipeptide

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

2019 ◽  
Author(s):  
Chiara Panosetti ◽  
Artur Engelmann ◽  
Lydia Nemec ◽  
Karsten Reuter ◽  
Johannes T. Margraf
Keyword(s):  
Gaussian Process ◽  
Density Functional ◽  
Functional Form ◽  
Tight Binding ◽  
Gaussian Process Regression ◽  
Direct Access ◽  
Interatomic Potentials ◽  
Global Parameter ◽  
Repulsive Potentials ◽  
The Cost

The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scales that are unfeasible with first principles DFT. At the same time (and in contrast to empirical interatomic potentials and force-fields), DFTB still offers direct access to electronic properties such as the band-structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter-sets could be routinely adjusted for a given project. While fairly robust and transferable parameterization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR). The use of GPR circumvents the need for non-linear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen and oxygen. Overall, the new approach removes focus from the choice of functional form and parameterization procedure, in favour of a data-driven philosophy.

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