Toward force fields for atomistic simulations of iridium-containing complexes

2013 ◽  
Vol 35 (1) ◽  
pp. 18-29 ◽  
Author(s):  
Franziska D. Hofmann ◽  
Michael Devereux ◽  
Andreas Pfaltz ◽  
Markus Meuwly
Keyword(s):  
Force Fields ◽  
Atomistic Simulations

scholarly journals Active Learning for Robust, High-Complexity Reactive Atomistic Simulations

2020 ◽  
Author(s):  
Rebecca Lindsey ◽  
Laurence E. Fried ◽  
Nir Goldman ◽  
Sorin Bastea
Keyword(s):  
Active Learning ◽  
Density Functional ◽  
Force Fields ◽  
Atomistic Simulations ◽  
Management Approach ◽  
Linear Combinations ◽  
Functional Theory ◽  
Automated Generation ◽  
Reactive Force Fields ◽  
Polynomial Expansions

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here we present an active learning approach based on cluster analysis and Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. Use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training repository management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory (DFT) in terms of structure, dynamics, and speciation.

Liquid Crystal Properties of then-Alkyl-cyanobiphenyl Series from Atomistic Simulations withAb InitioDerived Force Fields

2007 ◽  
Vol 111 (9) ◽  
pp. 2130-2137 ◽  
Author(s):  
Ivo Cacelli ◽  
Luca De Gaetani ◽  
Giacomo Prampolini ◽  
Alessandro Tani
Keyword(s):  
Liquid Crystal ◽  
Force Fields ◽  
Atomistic Simulations ◽  
Crystal Properties

scholarly journals Active Learning for Robust, High-Complexity Reactive Atomistic Simulations

2020 ◽  
Author(s):  
Rebecca Lindsey ◽  
Laurence E. Fried ◽  
Nir Goldman ◽  
Sorin Bastea
Keyword(s):  
Active Learning ◽  
Density Functional ◽  
Force Fields ◽  
Atomistic Simulations ◽  
Management Approach ◽  
Linear Combinations ◽  
Functional Theory ◽  
Automated Generation ◽  
Reactive Force Fields ◽  
Polynomial Expansions

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here we present an active learning approach based on cluster analysis and Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. Use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training repository management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory (DFT) in terms of structure, dynamics, and speciation.

scholarly journals A molecular dynamics study of water-soluble polymers: analysis of force fields from atomistic simulations

2018 ◽  
Vol 45 (4-5) ◽  
pp. 310-321 ◽  
Author(s):  
Shalini J. Rukmani ◽  
Grit Kupgan ◽  
Dylan M. Anstine ◽  
Coray M. Colina
Keyword(s):  
Molecular Dynamics ◽  
Force Fields ◽  
Water Soluble ◽  
Atomistic Simulations ◽  
Water Soluble Polymers ◽  
Soluble Polymers

Use of the Magnetostatic Analogue In Electromagnetic Lens Engineering

1970 ◽  
Vol 28 ◽  
pp. 360-361
Author(s):  
John W. Coleman
Keyword(s):  
Boundary Conditions ◽  
High Performance ◽  
Force Fields ◽  
Optical Design ◽  
Direct Conversion ◽  
Design Engineering ◽  
Design Data ◽  
Scalar Potentials ◽  
Geometric Elements ◽  
At Will

In the design engineering of high performance electromagnetic lenses, the direct conversion of electron optical design data into drawings for reliable hardware is oftentimes difficult, especially in terms of how to mount parts to each other, how to tolerance dimensions, and how to specify finishes. An answer to this is in the use of magnetostatic analytics, corresponding to boundary conditions for the optical design. With such models, the magnetostatic force on a test pole along the axis may be examined, and in this way one may obtain priority listings for holding dimensions, relieving stresses, etc..The development of magnetostatic models most easily proceeds from the derivation of scalar potentials of separate geometric elements. These potentials can then be conbined at will because of the superposition characteristic of conservative force fields.

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