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1.
J Chem Inf Model ; 64(13): 5108-5126, 2024 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-38926930

RESUMO

Generating simulation-ready molecular models for the LAMMPS molecular dynamics (MD) simulation software package is a difficult task and impedes the more widespread and efficient use of MD in materials design and development. Fixed-bond force fields generally require manual assignment of atom types, bonded interactions, charges, and simulation domain sizes. A new LAMMPS pre- and postprocessing toolkit (LUNAR) is presented that efficiently builds molecular systems for LAMMPS. LUNAR automatically assigns atom types, generates bonded interactions, assigns charges, and provides initial configuration methods to generate large molecular systems. LUNAR can also incorporate chemical reactivity into simulations by facilitating the use of the REACTER protocol. Additionally, LUNAR provides postprocessing for free volume calculations, cure characterization calculations, and property predictions from LAMMPS thermodynamic outputs. LUNAR has been validated via building and simulation of pure epoxy and cyanate ester polymer systems with a comparison of the corresponding predicted structures and properties to benchmark values, including experimental results from the literature. LUNAR provides the tools for the computationally driven development of next-generation composite materials in the Integrated Computational Materials Engineering (ICME) and Materials Genome Initiative (MGI) frameworks. LUNAR is written in Python with the usage of NumPy and can be used via a graphical user interface, a command line interface, or an integrated design environment. LUNAR is freely available via GitHub.


Assuntos
Simulação de Dinâmica Molecular , Software , Termodinâmica , Polímeros/química , Automação
2.
J Phys Chem B ; 128(17): 4255-4265, 2024 May 02.
Artigo em Inglês | MEDLINE | ID: mdl-38648370

RESUMO

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.

3.
ACS Appl Eng Mater ; 1(10): 2555-2566, 2023 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-37915552

RESUMO

Glassy carbon (GC) material derived from pyrolyzed furan resin was modeled by using reactive molecular dynamics (MD) simulations. The MD polymerization simulation protocols to cure the furan resin precursor material are validated via comparison of the predicted density and Young's modulus with experimental values. The MD pyrolysis simulations protocols to pyrolyze the furan resin precursor is validated by comparison of calculated density, Young's modulus, carbon content, sp2 carbon content, the in-plane crystallite size, out-of-plane crystallite stacking height, and interplanar crystallite spacing with experimental results from the literature for furan resin derived GC. The modeling methodology established in this work can provide a powerful tool for the modeling-driven design of next-generation carbon-carbon composite precursor chemistries for thermal protection systems and other high-temperature applications.

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