Literature DB >> 33574347

Bond order redefinition needed to reduce inherent noise in molecular dynamics simulations.

Ibnu Syuhada1, Nikodemus Umbu Janga Hauwali2, Ahmad Rosikhin2, Euis Sustini2, Fatimah Arofiati Noor3, Toto Winata4.   

Abstract

In this work, we present the bond order redefinition needed to reduce the inherent noise in order to enhance the accuracy of molecular dynamics simulations. We propose defining the bond order as a fraction of energy distribution. It happens due to the character of the material in nature, which tries to maintain its environment. To show the necessity, we developed a factory empirical interatomic potential (FEIP) for carbon that implements the redefinition with a short-range interaction approach. FEIP has been shown to enhance the accuracy of the calculation of lattice constants, cohesive energy, elastic properties, and phonons compared to experimental data, and can even be compared to other potentials with the long-range interaction approach. The enhancements due to FEIP can reduce the inherent noise, then provide a better prediction of the energy based on the behaviour of the atomic environment. FEIP can also transform simple two-body interactions into many-body interactions, which is useful for enhancing accuracy. Due to implementing the bond order redefinition, FEIP offers faster calculations than other complex interatomic potentials.

Entities:  

Year:  2021        PMID: 33574347     DOI: 10.1038/s41598-020-80217-0

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  12 in total

1.  Modeling of Covalent Bonding in Solids by Inversion of Cohesive Energy Curves.

Authors: 
Journal:  Phys Rev Lett       Date:  1996-11-18       Impact factor: 9.161

2.  New many-body potential for the bond order.

Authors: 
Journal:  Phys Rev Lett       Date:  1989-11-27       Impact factor: 9.161

3.  Simulation of the Elastic and Ultimate Tensile Properties of Diamond, Graphene, Carbon Nanotubes, and Amorphous Carbon Using a Revised ReaxFF Parametrization.

Authors:  Benjamin D Jensen; Kristopher E Wise; Gregory M Odegard
Journal:  J Phys Chem A       Date:  2015-09-02       Impact factor: 2.781

4.  Superior thermal conductivity of single-layer graphene.

Authors:  Alexander A Balandin; Suchismita Ghosh; Wenzhong Bao; Irene Calizo; Desalegne Teweldebrhan; Feng Miao; Chun Ning Lau
Journal:  Nano Lett       Date:  2008-02-20       Impact factor: 11.189

5.  The effect of time step, thermostat, and strain rate on ReaxFF simulations of mechanical failure in diamond, graphene, and carbon nanotube.

Authors:  Benjamin D Jensen; Kristopher E Wise; Gregory M Odegard
Journal:  J Comput Chem       Date:  2015-06-12       Impact factor: 3.376

6.  Real-time imaging of adatom-promoted graphene growth on nickel.

Authors:  Laerte L Patera; Federico Bianchini; Cristina Africh; Carlo Dri; German Soldano; Marcelo M Mariscal; Maria Peressi; Giovanni Comelli
Journal:  Science       Date:  2018-03-16       Impact factor: 47.728

7.  Magnetic bond-order potential for iron.

Authors:  M Mrovec; D Nguyen-Manh; C Elsässer; P Gumbsch
Journal:  Phys Rev Lett       Date:  2011-06-14       Impact factor: 9.161

8.  ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation.

Authors:  Kimberly Chenoweth; Adri C T van Duin; William A Goddard
Journal:  J Phys Chem A       Date:  2008-01-16       Impact factor: 2.781

9.  Comparing graphene growth on Cu(111) versus oxidized Cu(111).

Authors:  Stefano Gottardi; Kathrin Müller; Luca Bignardi; Juan Carlos Moreno-López; Tuan Anh Pham; Oleksii Ivashenko; Mikhail Yablonskikh; Alexei Barinov; Jonas Björk; Petra Rudolf; Meike Stöhr
Journal:  Nano Lett       Date:  2015-01-29       Impact factor: 11.189

10.  Machine learning coarse grained models for water.

Authors:  Henry Chan; Mathew J Cherukara; Badri Narayanan; Troy D Loeffler; Chris Benmore; Stephen K Gray; Subramanian K R S Sankaranarayanan
Journal:  Nat Commun       Date:  2019-01-22       Impact factor: 14.919

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