NCPac (Network Characterization Package)

NCPac is an analysis package for the output positional files from molecular dynamics, Monte Carlo and other types of particle based simulations.  The package handles both single frame and multiple frame (dynamic) .xyz trajectory files and calculates a host of information including,

• Surface particle locations, packing classification, curvature and output for visualization
• For nanoparticles, statistics on surface layer shape and geometry
• Particle cluster populations and optional filtering (useful for deposition simulations)
• Distribution function such as partial pair correlation (code handles different particle type such as elements), partial bond angle distribution functions
• Partial coordination histograms, bond length statistics, bond type fractions
• Bulk packing classification (FCC, HCP etc), spherical harmonic q6 analysis for ordering analysis, Lindemann Index for dynamical phase state information
• Fractal dimension, signature cell fitting and search

Some example collaborative applications includes feature extraction from large nanoparticle datasets for machine learning applications,

• A. Barnard, G. Opletal, Gold Nanoparticle Data Set. CSIRO. Data Collection (2019)

• A. Barnard, G. Opletal, Nickel Nanoparticle Data Set. CSIRO. Data Collection (2020)

• A. Barnard, G. Opletal, Copper Nanoparticle Data Set. CSIRO. Data Collection (2020)

• A. Barnard, G. Opletal, Ruthenium Nanoparticle Data Set. CSIRO. Data Collection (2020)

Surface and bulk packing characterization of laser heated nanorods,

• G. Opletal, G. Grochola, Yu Hang Chui, I. K. Snook, S. P. Russo, Stability and Transformation of Heated Gold Nanorods, J. Phys. Chem. C 115(11), 4375-4380 (2011)

Order parameter analysis of hard sphere systems predicting precursor regions of ordering prior to crystallization,

• T. Schilling, H. J. Schöpe, M. Oettel, G. Opletal, I. Snook, Precursor-mediated crystallization process in suspensions of hard spheres, Phys. Rev. Lett. 105, 025701 (2010)

Analysis of bonding in models of lithium ion battery electrolyte chemistry,

• A. Budi, A. Basile, G. Opletal, A. F. Hollenkamp, A. S. Best, R. J. Rees, A. I. Bhatt, A. P. Mullane, S. P. Rus

  

  so, Study of the Initial Stage of Solid Electrolyte Interphase Formation upon Chemical Reaction of Lithium Metal and N-Methyl-N-Propyl-Pyrrolidinium-Bis(Fluorosulfonyl)Lmide, J. Phys. Chem. C 116, 19789-19797 (2012)

Analysis of the bonding in aluminium oxide models which are used in Josephson junction construction applicable to quantum computing,

• M. J. Cyster, J. S. Smith, N. Vogt, G. Opletal, S. P. Russo, J. H. Cole, Simulating the fabrication of aluminium oxide tunnel junctions, npj Quantum Information, (2020)

Feature extraction from metallic nanoparticle for machine learning application looking at catalytic application tuning,

• A. J. Parker, G. Opletal, A. S. Barnard, Classification of Platinum Nanoparticle Catalysts using Machine Learning, J. Appl. Phys. 128, 014301 (2020)

This Fortran software is currently under development.

Collaborative interest and beta testing should contact the Principle Developer.

For more information, contact the Principal Developer, Dr George Opletal.