Type Label Framework for Bonded Force Fields in LAMMPS.

New functionality is added to the LAMMPS molecular simulation package, which increases the versatility with which LAMMPS can interface with supporting software and manipulate information associated with bonded force fields. We introduce the "type label" framework that allows atom types and their higher-order interactions (bonds, angles, dihedrals, and impropers) to be represented in terms of the standard atom type strings of a bonded force field. Type labels increase the human readability of input files, enable bonded force fields to be supported by the OpenKIM repository, simplify the creation of reaction templates for the REACTER protocol, and increase compatibility with external visualization tools, such as VMD and OVITO. An introductory primer on the forms and use of bonded force fields is provided to motivate this new functionality and serve as an entry point for LAMMPS and OpenKIM users unfamiliar with bonded force fields. The type label framework has the potential to streamline modeling workflows that use LAMMPS by increasing the portability of software, files, and scripts for preprocessing, running, and postprocessing a molecular simulation.

[1]  W. Im,et al.  Accurate Force Fields for Atomistic Simulations of Oxides, Hydroxides, and Organic Hybrid Materials up to the Micrometer Scale. , 2023, Journal of chemical theory and computation.

[2]  H. Heinz,et al.  Accurate and Ultrafast Simulation of Molecular Recognition and Assembly on Metal Surfaces in Four Dimensions. , 2023, ACS nano.

[3]  S. Urata,et al.  New Atomistic Insights on the Chemical Mechanical Polishing of Silica Glass with Ceria Nanoparticles , 2023, Langmuir : the ACS journal of surfaces and colloids.

[4]  J. Schroers,et al.  aflow++: A C++ framework for autonomous materials design , 2022, Computational Materials Science.

[5]  A. Calzolari,et al.  aflow.org: A Web Ecosystem of Databases, Software and Tools , 2022, Computational Materials Science.

[6]  B. Natarajan,et al.  Solid with infused reactive liquid (SWIRL): A novel liquid-based separation approach for effective CO2 capture , 2022, Science advances.

[7]  Nathan R. Kern,et al.  CHARMM-GUI Nanomaterial Modeler for Modeling and Simulation of Nanomaterial Systems. , 2021, Journal of chemical theory and computation.

[8]  Steven J. Plimpton,et al.  LAMMPS - A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales , 2021, Computer Physics Communications.

[9]  Samuel M. Blau,et al.  Insight into SEI Growth in Li-Ion Batteries using Molecular Dynamics and Accelerated Chemical Reactions , 2021, The Journal of Physical Chemistry C.

[10]  Yu Huang,et al.  Direct correlation of oxygen adsorption on platinum-electrolyte interfaces with the activity in the oxygen reduction reaction , 2021, Science Advances.

[11]  G. Odegard,et al.  Interfacial modeling of flattened CNT composites with cyanate ester and PEEK polymers , 2021 .

[12]  C McCabe,et al.  Open‐source molecular modeling software in chemical engineering focusing on the Molecular Simulation Design Framework , 2020, AIChE Journal.

[13]  Jacob R. Gissinger,et al.  REACTER: A Heuristic Method for Reactive Molecular Dynamics , 2020 .

[14]  Yi Wang,et al.  Scalable molecular dynamics on CPU and GPU architectures with NAMD. , 2020, The Journal of chemical physics.

[15]  T. Ohara,et al.  Construction of polydisperse polymer model and investigation of heat conduction: A molecular dynamics study of linear and branched polyethylenimine , 2019, Polymer.

[16]  J. Miao,et al.  Understanding Chemical Bonding in Alloys and the Representation in Atomistic Simulations , 2018, The Journal of Physical Chemistry C.

[17]  Tariq Jamil,et al.  Insight into induced charges at metal surfaces and biointerfaces using a polarizable Lennard–Jones potential , 2018, Nature Communications.

[18]  Jacob R. Gissinger,et al.  Carbon Nanotube Dispersion in Solvents and Polymer Solutions: Mechanisms, Assembly, and Preferences. , 2017, ACS nano.

[19]  Jacob R. Gissinger,et al.  Chemical Reactions in Classical Molecular Dynamics. , 2017, Polymer.

[20]  Michael Walter,et al.  The atomic simulation environment-a Python library for working with atoms. , 2017, Journal of physics. Condensed matter : an Institute of Physics journal.

[21]  Peter G. Boyd,et al.  Force-Field Prediction of Materials Properties in Metal-Organic Frameworks , 2016, The journal of physical chemistry letters.

[22]  J. Behler.  Perspective: Machine learning potentials for atomistic simulations. , 2016, The Journal of chemical physics.

[23]  John E. Stone,et al.  TopoGromacs: Automated Topology Conversion from CHARMM to GROMACS within VMD , 2016, J. Chem. Inf. Model..

[24]  Alexander D. MacKerell,et al.  An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications , 2016, Chemical reviews.

[25]  H. Heinz,et al.  Accurate Force Field Parameters and pH Resolved Surface Models for Hydroxyapatite to Understand Structure, Mechanics, Hydration, and Biological Interfaces , 2015, 1512.00122.

[26]  R. Colaço,et al.  From lime to silica and alumina: systematic modeling of cement clinkers using a general force-field. , 2015, Physical chemistry chemical physics : PCCP.

[27]  W. L. Jorgensen,et al.  Improved Peptide and Protein Torsional Energetics with the OPLS-AA Force Field , 2015, Journal of chemical theory and computation.

[28]  J. Pfaendtner,et al.  The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids. , 2015, The journal of physical chemistry. B.

[29]  Vikas Varshney,et al.  Force Field and a Surface Model Database for Silica to Simulate Interfacial Properties in Atomic Resolution , 2014 .

[30]  Jing Huang,et al.  CHARMM36 all‐atom additive protein force field: Validation based on comparison to NMR data , 2013, J. Comput. Chem..

[31]  Pengyu Y. Ren,et al.  The Polarizable Atomic Multipole-based AMOEBA Force Field for Proteins. , 2013, Journal of chemical theory and computation.

[32]  R. K. Mishra,et al.  Thermodynamically consistent force fields for the assembly of inorganic, organic, and biological nanostructures: the INTERFACE force field. , 2013, Langmuir : the ACS journal of surfaces and colloids.

[33]  Marco Buongiorno Nardelli,et al.  AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations , 2012 .

[34]  A. V. van Duin,et al.  Confined water dissociation in microporous defective silicates: mechanism, dipole distribution, and impact on substrate properties. , 2012, Journal of the American Chemical Society.

[35]  James P. Sethna,et al.  The potential of atomistic simulations and the knowledgebase of interatomic models , 2011 .

[36]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[37]  Richard A. Vaia,et al.  Accurate Simulation of Surfaces and Interfaces of Face-Centered Cubic Metals Using 12−6 and 9−6 Lennard-Jones Potentials , 2008 .

[38]  Taehoon Kim,et al.  CHARMM‐GUI: A web‐based graphical user interface for CHARMM , 2008, J. Comput. Chem..

[39]  Walter Kob,et al.  New fitting scheme to obtain effective potential from Car-Parrinello molecular-dynamics simulations: Application to silica , 2008, 0802.2421.

[40]  Teruaki Motooka,et al.  Interatomic potential for Si–O systems using Tersoff parameterization , 2007 .

[41]  A. Matthews,et al.  A simple transferable interatomic potential model for binary oxides applied to bulk α-Al2O3 and the (0 0 0 1) α-Al2O3 surface , 2006 .

[42]  U. Suter,et al.  Atomic Charges for Classical Simulations of Polar Systems , 2004 .

[43]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[44]  Ming-Jing Hwang,et al.  Derivation of class II force fields. VIII. Derivation of a general quantum mechanical force field for organic compounds , 2001, J. Comput. Chem..

[45]  William L. Jorgensen,et al.  Gas‐phase and liquid‐state properties of esters, nitriles, and nitro compounds with the OPLS‐AA force field , 2001, J. Comput. Chem..

[46]  A. V. Duin,et al.  ReaxFF: A Reactive Force Field for Hydrocarbons , 2001 .

[47]  H. Sun,et al.  COMPASS: An ab Initio Force-Field Optimized for Condensed-Phase ApplicationsOverview with Details on Alkane and Benzene Compounds , 1998 .

[48]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[49]  T. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[50]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[51]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[52]  J. Sauer,et al.  MOLECULAR MECHANICS POTENTIAL FOR SILICA AND ZEOLITE CATALYSTS BASED ON AB INITIO CALCULATIONS 2 , 1995 .

[53]  A. Nakano,et al.  First sharp diffraction peak and intermediate-range order in amorphous silica: finite-size effects in molecular dynamics simulations , 1994 .

[54]  Arnold T. Hagler,et al.  An ab Initio CFF93 All-Atom Force Field for Polycarbonates , 1994 .

[55]  D Fincham,et al.  Shell model simulations by adiabatic dynamics , 1993 .

[56]  S. L. Mayo,et al.  DREIDING: A generic force field for molecular simulations , 1990 .

[57]  T. W. Smith,et al.  Thermal expansion, Gruneisen functions and static lattice properties of quartz , 1982 .

[58]  R. K. Mishra,et al.  Force field for calcium sulfate minerals to predict structural, hydration, and interfacial properties , 2021 .

[59]  A. Stukowski.  Modelling and Simulation in Materials Science and Engineering Visualization and analysis of atomistic simulation data with OVITO – the Open Visualization Tool , 2009 .

[60]  D. Osguthorpe,et al.  Structure and energetics of ligand binding to proteins: Escherichia coli dihydrofolate reductase‐trimethoprim, a drug‐receptor system , 1988, Proteins.

[61]  R W Hockney,et al.  Computer Simulation Using Particles , 1966 .

[62]  P. P. Ewald.  Die Berechnung optischer und elektrostatischer Gitterpotentiale , 1921 .