LAMMPS: Open-Source, High-Performance, and High-Fidelity Molecular Dynamics Code for Simulations of Chemical and Physical Processes of Materials


Sandia National Laboratories (SNL)

Capability Expert

Reese Jones, Xiaowang Zhou, Steven Plimpton, Aidan Thompson


Computational Tools and Modeling

Node Readiness Category

2: High-Temperature Electrolysis (HTE)
1: Low-Temperature Electrolysis (LTE)
2: Photoelectrochemical (PEC)
2: Solar Thermochemical (STCH)
2: Hybrid Thermochemical (HT)


LAMMPS is a classical molecular dynamics (MD) code widely used within the physics and materials science communities. It has potentials to simulate: solid-state materials (metals, semiconductors, oxides, glasses), soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model individual atoms or molecules up to systems on the meso and continuum scales. Many methods to model processes and include the effects of the environment are available, for example grand canonical schemes for the interaction with gas reservoirs. Heat/phonon transport, finite temperature, finite size, steric, electrostatics, and other fundamental phenomena are all represented explicitly and accurately. The atomic data can be coarse-grained to connect with higher level theory and expectations. LAMMPS runs efficiently on single processor to exascale parallel computers. The code is designed to be easy to extend with new functionality and is distributed as open source under the terms of the GPL.

Specific uses relevant to HydroGen-AWSM include:

  • Simulation of electrode-electrolyte interactions and the diffuse and double layers
  • High throughput screening of electrolytes based on transport and other properties
  • Calculation of energy barriers for water to dissociate on a metal surface, calculation of diffusion coefficients of hydrogen in materials
  • Simulation of adsorption and coverage of gas or dissolved species on surfaces
  • Calculation of energies and structures of defects (e.g., dislocations, grain boundaries, interfaces) within solid materials
  • Modeling the transition from the close-packed ions near electrodes to the diffuse bulk region in electrochemical double layers
  • Simulating the interplay between nanoconfinement, surface and transport effects in pores and fissures/channels
  • Modeling of extrinsic energy sources interacting with the intrinsic physics of MD, such as laser illumination exciting electronic states that ultimately convert to lattice heating.

Capability Bounds‎

LAMMPS is designed to run on a variety of platforms from single processor, small machines to massively parallel computers, including advanced many-core and CPU/GPU architectures. Classical molecular dynamics is generally limited to small time scales but can resolve many equilibrium and transport processes over relevant length-scales and can leverage techniques such as parallel replica dynamics to extend accessible timescales.

Unique Aspects‎

LAMMPS is a true community code with tremendous and varied capability developed over 20 years by more than a hundred contributors. LAMMPS provides an unrivalled range of different interatomic potentials enabling the simulation of a wide variety of materials. These potentials range from simple pair potentials, to the common potentials for organic and biological systems (CHARMM and AMBER) to polarizable core-shell models and complex many-body potentials such as COMB and ReaXFF, which enables modeling of a variety of fluid phase reactive chemistries, and bond order potentials, which enable high fidelity simulations of alloys and compounds. LAMMPS also provides interfaces to other atomistic modeling software such as KIM, QUIP and Quantum ESPRESSO.


Our methods are published with the LAMMPS open source code with a GPL license, see the LAMMPS Molecular Dynamics Simulator.


Modeling atomistic interactions of complex material systems (such as corrosion or surface oxygen exchange) will generate a deeper, more fundamental understanding of water-splitting material behavior. This information can be used to derive and test novel material formulations leading to discovery, or analyze failure modes in materials and test mitigation strategies.


Figure 1. MD simulation of oxidation of an aluminum layer on an Fe-Ni-Co substrate, showing the formation of a pinhole.

Figure 2. MD simulation of interaction between a dislocation in bulk Si and surface SiO2, showing dislocation motion under the stress created by the surface oxides.

Figure 3. MD simulation of graphene growth on Cu, showing the self-assembly of the graphene structure from random carbon adatoms.

Figure 4. MD simulation of 2H→H2 chemical reaction, showing that hydrogen atoms correctly form the H2 gas at room temperature.

Figure 5. Fluid electrolyte interacting dynamically with a solid electrode. Mesh shown is used to represent smooth fields such as the electric potential and concentration gradient.

Figure 6. Spatial resolution of ionic conductivity in a channel via partitioned Green-Kubo method.


S. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J Comp Phys, 117, 1-19 (1995).

X. W. Zhou, H. N. G. Wadley, J. S. Filhol, and M. N. Neurock, "Modified charge transfer-embedded atom method potential for metal metal oxide systems", Phys. Rev. B, 69, 035402 (2004).

X. W. Zhou, H. N. G. Wadley, and D. X. Wang, "Transient hole formation during the growth of thin metal oxide layers", Comp. Mater. Sci., 39, 794 (2007).

X. W. Zhou, D. K. Ward, and M. E. Foster, "An Analytical Bond-Order Potential for Carbon", J. Comp. Chem., 36, 1719 (2015).

X. W. Zhou, D. K. Ward, M. Foster, and J. A. Zimmerman, "An analytical bond-order potential for the copper-hydrogen binary system", J. Mater. Sci., 50, 2859 (2015).

R. E. Jones, D. K. Ward, J. A. Templeton, Spatial resolution of the electrical conductance of ionic fluids using a Green-Kubo method, J. Chem. Phys., 141, 184110, (2014).

F. Rizzi, R. E. Jones, B. J. Debusschere, O. M. Knio. Uncertainty quantification in MD simulations of concentration driven ionic flow through a silica nanopore. Part I: sensitivity to physical parameters of the pore. J. Chem. Phys., 138:194104, (2013).

F. Rizzi, R. E. Jones, B. J. Debusschere, O. M. Knio. Uncertainty quantification in MD simulations of concentration driven ionic flow through a silica nanopore. Part II: uncertain potential parameters. J. Chem. Phys., 138:194105, (2013).

M. Salloum, K. Sargsyan, R. Jones, B. Debusschere, H. N. Najm, and H. Adalsteinsson. A Stochastic Multiscale Coupling Scheme to Account for Sampling Noise in Atomistic-to-Continuum Simulations. Multiscale Model. Simul. 550–584, (2012).

J. A. Templeton, R. E. Jones, J. W. Lee, J. A. Zimmerman, and B. M. Wong. A long-range electric field solver for molecular dynamics based on atomistic-to-continuum modeling. J. Chem. Theo. Comp., 7(6):1736–1749, (2011).