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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +7004 +10.21105/joss.07004 + +OpenMD: A parallel molecular dynamics engine for complex +systems and interfaces + + + +https://orcid.org/0009-0006-3968-5088 + +Drisko +Cody R. + + + + +https://orcid.org/0000-0002-3573-9716 + +Bhattarai +Hemanta + + + + +https://orcid.org/0000-0001-8963-4103 + +Fennell +Christopher J. + + + + +https://orcid.org/0000-0002-1799-393X + +Stocker +Kelsey M. + + + + +https://orcid.org/0000-0003-4091-6059 + +II +Charles F. Vardeman + + + + +https://orcid.org/0000-0002-2935-3163 + +Gezelter +J. Daniel + + +* + + + +Department of Chemistry and Biochemistry, University of +Notre Dame, Notre Dame, IN, United States + + + + +Department of Physics, Goshen College, Goshen, IN, United +States + + + + +Department of Chemistry, Oklahoma State University, +Stillwater, OK, United States + + + + +Department of Biochemistry, Chemistry, Environment, and +Physics, Suffolk University, Boston, MA, United States + + + + +Center for Research Computing, University of Notre Dame, +Notre Dame, IN, United States + + + + +* E-mail: + + +25 +6 +2024 + +9 +103 +7004 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +C++ +Chemistry +Molecular Dynamics + + + + + + Summary +

Molecular dynamics (MD) simulations help bridge the gap between + quantum mechanical calculations, which trade computational resources + and system size for chemical accuracy, and continuum simulations, + which utilize bulk and surface material properties to make + predictions. MD provides the ability to study emergent properties and + dynamics for relatively large systems ( + + 106 + atoms) over a span of nano- to micro-seconds. MD has been used for + simulations of complex systems such as liquids, proteins, membranes, + nanoparticles, interfaces, and porous materials, and the + approximations used in MD simulations allow us to reach + experimentally-relevant time and length scales + (Allen + & Tildesley, 2017). A molecular dynamics engine leverages + classical equations of motion to evolve atomic or molecular + coordinates according to a well-defined potential energy function, + known as a force field, which is a function of those atomic + coordinates. There are a number of high quality molecular dynamics + engines that specialize in materials + (Thompson + et al., 2022) or biomolecular + (Brooks + et al., 2009; + Case + et al., 2023) simulations or are models of computational + efficiency + (Abraham + et al., 2015; + Eastman + et al., 2017). In this paper, we provide background on an open + source molecular dynamics engine, OpenMD, which + specializes in complex systems and interfaces and was just released + into version 3.1.

+

OpenMD is capable of efficiently simulating + a variety of complex systems using standard point-particle atoms, as + well as atoms with orientational degrees of freedom (e.g. point + multipoles and coarse-grained assemblies), and atoms with additional + degrees of freedom (e.g. fluctuating charges). It provides a test-bed + for new molecular simulation methodology, while being efficient and + easy to use. Liquids, proteins, zeolites, lipids, inorganic + nanomaterials, transition metals (bulk, flat interfaces, and + nanoparticles), alloys, solid-liquid interfaces, and a wide array of + other systems have all been simulated using this code. + OpenMD works on parallel computers using the + Message Passing Interface (MPI), and is packaged with trajectory + analysis and utility programs that are easy to use, extend, and + modify.

+

From the beginning, OpenMD has been an open + source project and has been maintained in accordance with the FAIR + (findable, accessible, interoperable, and reusable) principles for + research software + (Barker + et al., 2022). It uses a meta-data language that is tightly + integrated with input (.omd) and trajectory + (.dump) files, providing a standardized, + human-readable way to completely describe molecular systems and + simulation parameters. All data files are stamped with the code + revision that generated that data. This allows + OpenMD simulations to be easily reproduced, + modified, and reused by others. The + <MetaData> section of these files also + serve as a form of machine-actionable meta-data, clearly specifying + the composition of the molecular system in a manner that promotes + interoperability with other software tools.

+
+ + Statement of need +

OpenMD builds on the foundations of the + Object-Oriented Parallel Simulation Engine + (OOPSE) program + (Meineke + et al., 2005), rewritten in modern C++. It differs from other + contemporary molecular dynamics engines in its focus on complex + interfaces. A number of features unique to + OpenMD facilitate the study of this problem + space, including efficient non-equilibrium algorithms, non-periodic + simulations, metal polarizability models, and real space + electrostatics. The first such feature is Reverse Non-Equilibrium + Molecular Dynamics (RNEMD), a family of algorithms which impose a + non-physical flux on a system and use linear response theory to + compute transport properties as the system approaches steady state. + The goal of RNEMD methods is to calculate the relevant transport + property ( + + λ) + that connects the flux ( + + 𝐉) + and driving force ( + + X) + according to the generalized equation, + + + 𝐉=λX.

+

OpenMD is also capable of performing + condensed phase simulations without the use of periodic boundary + conditions. To do so, an external pressure and temperature bath is + applied to atoms comprising the system’s convex hull, rather than the + interior region. This method, the Langevin Hull, allows for constant + pressure, temperature, or isobaric-isothermal (NPT) simulations of + explicitly non-periodic molecular systems. Other major developments + are the inclusion of advanced real-space electrostatics for point + multipoles, and polarizable force fields using fluctuating charges or + fluctuating electron densities.

+ + Reverse Non-Equilibrium Molecular Dynamics +

RNEMD methods impose a non-physical (heat, momentum, or particle) + flux between different regions of the simulation. In response, the + system develops a temperature, velocity, or concentration gradient + between the two regions, and the linear coefficient connecting + applied flux and measured gradient is a transport property of the + material. Since the amount of the applied flux is known exactly in + RNEMD, and the measurement of gradients is generally + straightforward, imposed-flux methods typically take shorter + simulation times to obtain converged results for transport + properties, when compared with equilibrium MD or forward-NEMD + approaches. If an interface lies between the two regions, these + methods can also provide interfacial transport + coefficients by mapping any spatial discontinuities in + concentration, velocity, or temperature with the applied flux + (Drisko + & Gezelter, 2024; + Kuang + & Gezelter, 2012; + Stocker + & Gezelter, 2014).

+

Non-equilibrium molecular dynamics is a well-developed area of + research, and OpenMD supports many different + RNEMD algorithms. The first is the original “swapping” approach by + Müller-Plathe + (Müller-Plathe, + 1997, + 1999). + Here, the entire momentum vectors of two particles in separate slabs + may be exchanged to generate a thermal flux. Occasionally, non-ideal + Maxwell-Boltzmann distributions will develop in velocity profiles + using this approach + (Tenney + & Maginn, 2010). OpenMD also + introduces a number of new algorithms which extend the capabilities + of RNEMD.

+

Rather than using momentum exchanges between individual particles + in each region, the Non-Isotropic Velocity Scaling (NIVS) algorithm + applies velocity scaling to all of the selected particles in both + regions + (Kuang + & Gezelter, 2010). NIVS was shown to be very effective at + computing thermal conductivities, but is not suitable for imposing a + momentum flux or for computing shear viscosities. However, + simultaneous velocity shearing and scaling (VSS) exchanges between + the two regions remove all of these limitations + (Kuang + & Gezelter, 2012). The VSS-RNEMD method yields a simple + set of equations which satisfy energy and linear momentum + conservation constraints, while simultaneously imposing a desired + flux between the two regions. The VSS approach is versatile in that + it may be used to implement both thermal and shear transport either + separately or simultaneously. OpenMD is also + capable of leveraging the VSS method in non-periodic + simulations, in which the regions have been generalized to + concentric regions of space + (Stocker + & Gezelter, 2014), allowing for simulations of heat + transport away from nanostructures. In the following section, we + explore the algorithm that makes non-periodic boundary simulations + possible in OpenMD.

+

Another novel RNEMD algorithm allows for particle positions to be + swapped between RNEMD regions resulting in an applied + particle flux. The scaled particle flux (SPF) + method accurately calculates diffusion coefficients while + maintaining energy and linear momentum constraints, and can map the + temperature dependence of diffusion when used in tandem with a + thermal flux in VSS-RNEMD. SPF-RNEMD has also been applied to + interfacial systems of nanoporous graphene in a molecular fluid. In + this case, permeabilities were computed by imposing a molecular flux + between regions on opposite sides of the membrane and measuring both + the hydraulic and osmotic pressure that develops as a result of this + flux + (Drisko + & Gezelter, 2024).

+
+ + Langevin Hull +

In many molecular simulations, systems have near-uniform + compressibility, and OpenMD implements a + range of integrators to sample the isothermal-isobaric (NPT) + ensemble using the Nosé-Hoover-Andersen equations of motion. These + integrators implement various forms of affine scaling to provide + isotropic (NPTi) or fully-flexible (NPTf) scaling motions of the + periodic box + (Andersen, + 1980; + Hoover, + 1986; + Sturgeon + & Laird, 2000). Additional constant pressure integrators + use restricted affine scaling to enforce constant surface area + (NPAT), constant surface tension (NPγT), or even orthorhombic box + geometries (NPTxyz). For systems comprising materials of different + compressibilities, such as a solvated nanoparticle, scaled + coordinate transformations may cause numerical instability or poor + volume sampling depending on the strength of the applied barostat. + Users may also wish to represent systems without the explicit + periodicity required by box-scaling constant pressure methods. For + example, proteins may be in artificially crowded environments if + periodic box simulations are required.

+

To address these problems, OpenMD + implements a method called the Langevin Hull which samples the + isothermal-isobaric (NPT) ensemble for non-periodic systems + (Vardeman + et al., 2011). The method, based on Langevin dynamics, + couples an external bath at constant pressure, temperature, and + effective solvent viscosity to the atomic sites on a + dynamically-computed convex hull. This convex hull is defined as the + set of facets that have no concave corners at an atomic site + (Edelsbrunner + & Mücke, 1994). The hull is computed using Delaunay + triangulation between coplanar neighbors + (Delaunay, + 1934; + Lee + & Schachter, 1980). These computations are performed by + the external Qhull library + (Barber + et al., 1996), and are computed each time step, allowing + molecules to move freely between the inner region and outer hull. + Atoms in the interior evolve according to Newtonian mechanics. The + equations of motion for sites on the hull, + + + mi𝐯̇i(t)=iU+𝐅iext=iU+f13(𝐧̂fPAfΞf(t)(13i=1𝐯i)+𝐑f(t)) + include additional forces from the external bath. Each facet + + + f + on the convex hull has a contribution from a pressure bath acting in + proportion to the facet’s surface area and in the direction of the + surface normal + + (𝐧̂fPAf). + The facets of the hull are also in contact with an implicit solvent + which provides a drag on the velocity of the facet according to an + approximate resistance tensor, + + (Ξf𝐯f) + and which also kicks the facet via a Gaussian random force + + + (𝐑f).

+

Computation of a convex hull is + + 𝒪(NlogN) + for sequential machines and remains the performance bottleneck for + parallelization. In parallel, the global convex hull is computed + using the union of sites from all local (processor-specific) hulls. + Testing and validation for this method were carried out on three + unique systems, a gold nanoparticle and an SPC/E water droplet (both + with uniform compressibilities), and a gold nanoparticle solvated in + SPC/E water (non-uniform compressibility), shown in Fig. + [fig:LHull]. This + method was shown to work well across all test systems + (Vardeman + et al., 2011), and remains the preferred method of simulating + nanoparticles in the isothermal-isobaric (NPT) ensemble with + OpenMD.

+ +

A Langevin Hull surrounds explicit water solvating a + gold nanoparticle. The Langevin Hull imposes an external pressure + and temperature bath and maintains isobaric-isothermal conditions + without periodic boundaries. (Image created with the help of + Dr. Kristina Davis from the Notre Dame Center for Research + Computing.)

+ +
+
+ + Real Space Electrostatics +

Electrostatic interactions are one of the most important + intramolecular forces and are present in all but the most basic of + molecular simulations. These interactions are also long ranged, and + are typically the most computationally expensive. As a result, + significant effort has gone into to balancing the accuracy and + efficiency of these calculations. OpenMD + implements a number of techniques which are generally classified + according to how solvent molecules are incorporated into the systems + of interest. Implicit methods, which exclude solvent molecules from + the simulation, offer computational efficiency at the cost of + accuracy. One example would be the use of a reaction field + (Onsager, + 1936) for electrostatics coupled with Langevin dynamics to + include the hydrodynamic effects of the solvent. Explicit methods + which include all solvent molecules directly are the most widely + used with OpenMD. Explicit electrostatic + methods can further be classified as either Real + Space or Ewald-based methods.

+

The default electrostatics summation method used in + OpenMD is a real space, damped-shifted force + (DSF) model + (Fennell + & Gezelter, 2006) which extends and combines the standard + shifted potentials of Wolf et al. + (1999) + and the damped potentials of Zahn et al. + (2002). + The potential due to the damped-shifted force has the form: + + i} U_\mathrm{DSF}(q_i, q_j, r_{ij}) + \sum_i U_i^\mathrm{self}(q_i) \right]]]> + UCoulomb=14πϵ0[ij>iUDSF(qi,qj,rij)+iUiself(qi)] + where the damped shifted force potential, + + R_c + \end{cases}]]> + UDSF(qi,qj,rij)={qiqj[f(rij)f(Rc)f(Rc)(rijRc)],rijRc0,rij>Rc + cuts off smoothly as + + rijRc, + and the Coulombic kernel is damped using a complementary error + function, + + f(r)=erfc(αr)r. + The shifted potential term can be thought of as the interactions of + charges with neutralizing charges on the surface of the cutoff + sphere. The damping parameter + + (α) + can be specified directly in OpenMD, or is + set by default from the cutoff radius, + + + α=0.4250.02Rc, + where the code switches to an undamped kernel for + + 21.25]]> + Rc>21.25 + Å. The self potential represents the interaction of each charge with + its own neutralizing charge on the cutoff sphere, and this term + resembles the self-interaction in the Ewald sum, + + + Uiself(qi)=(erfc(αRc)Rc+απ1/2)qi2.

+

DSF offers an attractive compromise between the computational + efficiency of Real Space methods + ( + + 𝒪(N)) + and the accuracy of the full Ewald sum + (Fennell + & Gezelter, 2006). The DSF method has also been extended + for use with point dipoles and quadrupoles as Gradient + Shifted and Taylor Shifted potentials + (Lamichhane, + Gezelter, et al., 2014) and has been validated for potentials + and atomic forces + (Lamichhane, + Newman, et al., 2014), as well as dielectric properties + (Lamichhane + et al., 2016), against the full Ewald sum. Note that the + Ewald method was extended to point multipoles up to quadrupolar + order + (Smith, + 1982, + 1998), and + this has also been implemented in OpenMD. The + Shifted Force potential generalizes most directly + as the Gradient Shifted potential for multipoles, + and these are the default electrostatic summation methods in + OpenMD.

+
+ + Fluctuating Charges and Densities +

One way to include the effects of polarizability in molecular + simulations is to use electronegativity equalization + (Rappé + & Goddard, 1991) or fluctuating charges on atomic sites + (Rick + et al., 1994). OpenMD makes it + relatively easy to add extended variables (e.g. charges) on sites to + support these methods. In general, the equations of motion are + derived from an extended Lagrangian, + + + =i=1N[12mi𝐫̇i2+12Mqq̇i2]U({𝐫},{q})λ(i=1NqiQ) + where the potential depends on both atomic coordinates and the + dynamic fluctuating charges on each site. The final term in Eq. + 5 constrains + the total charge on the system to a fixed value, and + + + Mq + is a fictitious charge mass that governs the speed of propagation of + the extended variables.

+

A relatively new model for simulating bulk metals, the density + readjusting embedded atom method (DR-EAM), has also been implemented + (Bhattarai + et al., 2019). DR-EAM allows fluctuating densities within the + framework of the Embedded Atom Method (EAM) + (Daw + & Baskes, 1984), by adding an additional degree of + freedom, the charge for each atomic site. The total configurational + potential energy, + + U, + as a function of both instantaneous positions, + + + {𝐫}, + and partial charges + + {q}: + + + UDREAM({𝐫},{q})=iFi[ρi]+12ijiϕij(rij,qi,qj)+12ijiqiqjJ(rij)+iUiself(qi) + where the cost of embedding atom + + i + in a total valence density of + + ρi + is computed using the embedding functional, + + + Fi[ρi]. + + + ϕij + is the pair potential between atoms + + i + and + + j, + and + + J(rij) + is the Coulomb integral (which can be computed using the DSF + approximation above). Lastly, + + Uself + is an additional self potential, modeled as a sixth-order polynomial + parameterized by electron affinities and ionization potentials for a + wide range of metals, + + + Uiself(qi)=n=16anqin. + The contribution to the local density at site + + + i + depends on the instantaneous partial charges on all other atoms, + + + ρi=ji(1qjNj)fj(rij) + with + + Nj + as the valency count for atom + + j. + Modifications to the pair potential used in DR-EAM are also + supported.

+

DR-EAM was shown to perform well for bulk metals, metal surfaces, + and alloys; and most importantly, it retains the strengths of the + unmodified EAM in modeling bulk elastic constants and surface + energies + (Bhattarai + et al., 2019). DR-EAM has similar performance to the + unmodified EAM in that both approaches require a double-pass through + the force loop, once to compute local densities and again to compute + forces and energies. We note that the infrastructure required to + implement DR-EAM is a superset of what is required for common + fluc-q potentials like the TIP4P-FQ model for water + (Rick + et al., 1994).

+
+
+ + Acknowledgements +

We would like to acknowledge the contributions of Matthew A. + Meineke and Teng Lin to the original OOPSE + code. Contributions to the OpenMD codebase have + also come from: Patrick B. Louden, Joseph R. Michalka, James M. Marr, + Anderson D.S. Duraes, Suzanne M. Neidhart, Shenyu Kuang, Madan + Lamichhane, Xiuquan Sun, Sydney A. Shavalier, Benjamin M. Harless, + Veronica Freund, Minh Nhat Pham, Chunlei Li, Kyle Daily, Alexander + Mazanek, and Yang Zheng. Development of OpenMD + was carried out with support from the National Science Foundation + under grants CHE-0848243, CHE-1362211, CHE-1663773, and + CHE-191954648.

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ed9494d81cd7277f0fd1e49ec92149ac1ae0806e Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Fri, 1 Nov 2024 15:23:11 +0000 Subject: [PATCH 2/4] Creating 10.21105.joss.07004.crossref.xml --- joss.07004/10.21105.joss.07004.crossref.xml | 693 ++++++++++++++++++++ 1 file changed, 693 insertions(+) create mode 100644 joss.07004/10.21105.joss.07004.crossref.xml diff --git a/joss.07004/10.21105.joss.07004.crossref.xml b/joss.07004/10.21105.joss.07004.crossref.xml new file mode 100644 index 0000000000..8f89623ab6 --- /dev/null +++ b/joss.07004/10.21105.joss.07004.crossref.xml @@ -0,0 +1,693 @@ + + + + 20241101152228-bafdccce90220e87877a681c7c9a5e4efdaefbff + 20241101152228 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 11 + 2024 + + + 9 + + 103 + + + + OpenMD: A parallel molecular dynamics engine for +complex systems and interfaces + + + + Cody R. + Drisko + + Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, United States + + https://orcid.org/0009-0006-3968-5088 + + + Hemanta + Bhattarai + + Department of Physics, Goshen College, Goshen, IN, United States + + https://orcid.org/0000-0002-3573-9716 + + + Christopher J. + Fennell + + Department of Chemistry, Oklahoma State University, Stillwater, OK, United States + + https://orcid.org/0000-0001-8963-4103 + + + Kelsey M. + Stocker + + Department of Biochemistry, Chemistry, Environment, and Physics, Suffolk University, Boston, MA, United States + + https://orcid.org/0000-0002-1799-393X + + + Charles F. Vardeman + II + + Center for Research Computing, University of Notre Dame, Notre Dame, IN, United States + + https://orcid.org/0000-0003-4091-6059 + + + J. Daniel + Gezelter + + Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, United States + + https://orcid.org/0000-0002-2935-3163 + + + + 11 + 01 + 2024 + + + 7004 + + + 10.21105/joss.07004 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.13953130 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/7004 + + + + 10.21105/joss.07004 + https://joss.theoj.org/papers/10.21105/joss.07004 + + + https://joss.theoj.org/papers/10.21105/joss.07004.pdf + + + + + + AmberTools + Case + Journal of Chemical Information and +Modeling + 20 + 63 + 10.1021/acs.jcim.3c01153 + 2023 + Case, D. A., Aktulga, H. M., Belfon, +K., Cerutti, D. S., Cisneros, G. 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