LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

2021/09/22 by Aidan P. Thompson, Hasan Metin Aktulga, H. Metin Aktulga +20 · 461 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Electron and X-Ray Spectroscopy Techniques #Machine Learning in Materials Science

paper · doi:10.1016/j.cpc.2021.108171

openalex publication_date 2021/09/22 · openalex created_date 2021/09/27 · openalex updated_date 2026/07/31

Abstract

Since the classical molecular dynamics simulator LAMMPS was released as an open source code in 2004, it has become a widely-used tool for particle-based modeling of materials at length scales ranging from atomic to mesoscale to continuum. Reasons for its popularity are that it provides a wide variety of particle interaction models for different materials, that it runs on any platform from a single CPU core to the largest supercomputers with accelerators, and that it gives users control over simulation details, either via the input script or by adding code for new interatomic potentials, constraints, diagnostics, or other features needed for their models. As a result, hundreds of people have contributed new capabilities to LAMMPS and it has grown from fifty thousand lines of code in 2004 to a million lines today. In this paper several of the fundamental algorithms used in LAMMPS are described along with the design strategies which have made it flexible for both users and developers. We also highlight some capabilities recently added to the code which were enabled by this flexibility, including dynamic load balancing, on-the-fly visualization, magnetic spin dynamics models, and quantum-accuracy machine learning interatomic potentials. Program Title: Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) CPC Library link to program files: https://doi.org/10.17632/cxbxs9btsv.1 Developer's repository link: https://github.com/lammps/lammps Licensing provisions: GPLv2 Programming language: C++, Python, C, Fortran Supplementary material: https://www.lammps.org Nature of problem: Many science applications in physics, chemistry, materials science, and related fields require parallel, scalable, and efficient generation of long, stable classical particle dynamics trajectories. Within this common problem definition, there lies a great diversity of use cases, distinguished by different particle interaction models, external constraints, as well as timescales and lengthscales ranging from atomic to mesoscale to macroscopic. Solution method: The LAMMPS code uses parallel spatial decomposition, distributed neighbor lists, and parallel FFTs for long-range Coulombic interactions [1]. The time integration algorithm is based on the Størmer-Verlet symplectic integrator [2], which provides better stability than higher-order non-symplectic methods. In addition, LAMMPS supports a wide range of interatomic potentials, constraints, diagnostics, software interfaces, and pre- and post-processing features. Additional comments including restrictions and unusual features: This paper serves as the definitive reference for the LAMMPS code. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117 (1995) 1–19. L. Verlet, Computer experiments on classical fluids: I. Thermodynamical properties of Lennard–Jones molecules, Phys. Rev. 159 (1967) 98–103.

Citations

• Electronegativity: The density functional viewpoint
• Correlations in the Motion of Atoms in Liquid Argon
• Aspherical particle models for molecular dynamics simulation

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• FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
• Phase behavior and dynamics of active Brownian particles in an alignment field
• Charging and discharging a supercapacitor in molecular simulations
• Surface stratification determines the interfacial water structure of simple electrolyte solutions
• Graph atomic cluster expansion for foundational machine learning interatomic potentials
• Trefoil polymers from a knotted synthon
• A foundation model for atomistic materials chemistry
• Atomistic mechanisms of oxidation and chlorine corrosion in Ni-based superalloys: The role of boron and light interstitial segregation
• Water structuring at stacked graphene interfaces unveiled by machine-learning molecular dynamics
• Twist-angle transferable continuum model and second flat Chern band in twisted MoTe2 and WSe2
• Intermittent Viscoelastic Turbulence in Strongly Coupled Plasmas
• Bridging Simulation and Silicon: A Study of RISC-V Hardware and FireSim Simulation
• Interface and Thermophysical Properties of R32 Refrigerant
• How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces
• Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials
• Vibrational Fingerprints of Strained Polymers: A Spectroscopic Pathway to Mechanical State Prediction
• A universal machine learning model for the electronic density of states
• Inverse Design of Amorphous Materials with Targeted Properties
• Using molecular dynamics to investigate the driving force for graphene auto-kirigami
• Benchmarking thermostat algorithms in molecular dynamics simulations of a binary Lennard-Jones glass-former model
• Spectral Analysis of Light Interstitial Segregation Energies in Ni: The Role of Local Cr Coordination for Boron and Carbon
• Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
• Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials
• Achieving DFT accuracy in short range ordering and stacking fault energy using moment tensor potential for CoCrFeNi and CoCrNi
• Amorphization-Mediated Si-I to Si-V Phase Transition and Reversible Amorphous-Si-V Phase Memory in Silicon Nanoparticles
• Fine Particle Percolation Dynamics in Porous Media
• Physical embedding machine learning force fields for organic systems
• Generative Quasi-Continuum Modeling of Confined Fluids at the Nanoscale
• Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems
• Electronic Fluctuations and Ionic Dynamics in Molten Silver Iodide
• Ions leaving no tracks
• 3D Mapping of Defects and Moiré Corrugations via Electron Ptychography Atomic Coordinate Retrieval
• Towards High-Performance and Portable Molecular Docking on CPUs through Vectorization
• Non-linear jog-dragging effect on the mobility law of edge dislocations in face-centered cubic nickel
• Transitional patterns on a spherical surface: from scars to domain defects of mixed lattices
• Ionic glass formers show an inverted relation between fragility and relaxation broadness
• Glassy interphases reinforce elastomeric nanocomposites by enhancing percolation-driven volume expansion under strain
• A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids
• Low-rank matrix and tensor approximations: advancing efficiency of machine-learning interatomic potentials
• Shape spectra of elastic shells with surface-adsorbed semiflexible polymers
• Decomposition of low-angle grain boundaries
• Medium-range structural order in amorphous arsenic
• Confinement Reveals Hidden Splay-Bend Order in Twist-Bend Nematics
• When Energy and Information Revolutions Meet 2D Janus
• Hybrid Monte Carlo Metadynamics (hybridMC-MetaD)
• Jetting with gels: Soft microgel networks stabilize and extend nozzle-free water jets
• Intermolecular Interactions between Polyethylene, Water, and Potential Antistatic and Slip Additives: a Molecular Dynamics Study
• Decoding local framework dynamics in the ultra-small pore MOF MIL-120(Al) CO2 sorbent with Machine Learned Potentials
• Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
• Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
• Data for Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction
• Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields
• Kinetic pathways of coesite densification from metadynamics
• Ultrafast Solvent Dynamics Drives the Formation of the Hydrated Electron in Photoexcited Water
• Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
• Striking Similarities in Dynamics and Vibrations of 2D Quasicrystals and Supercooled Liquids
• Asymmetric stress engineering of dense dislocations in brittle superconductors for strong vortex pinning
• Evaluating Moment Tensor Potential in Ag-Cu Alloy: Accuracy, Transferability, and Phase Diagram Fidelity
• GridFF: Efficient Simulation of Organic Molecules on Rigid Substrates
• Polymer translocation through extended patterned pores in two dimensions: scaling of the total translocation time
• Physics-Informed ML Exploration of Structure-Transport Relationships in Hard Carbon
• When wall slip wins over shear flow: A temperature-dependent Eyring slip law and a thermal multiscale model for diamond-like carbon lubricated by a polyalphaolefin oil
• Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification
• Visualizing Poloidal Orientation in DNA Minicircles
• Atomistic mechanisms of phase transitions in all-temperature barocaloric material KPF₆
• Order-Disorder Transitions and Thermal Pathways in Frustrated 2D Colloidal Crystals
• Dislocation-mediated short-range order evolution during thermomechanical processing
• Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water
• pylimer-tools: A Python Package for Generating and Analyzing Bead-Spring Polymer Networks
• Phonon interference effects in GaAs-GaP superlattice nanowires
• Secondary finite-size effects and multi-barrier free energy landscapes in molecular simulations of hindered ion transport
• Physical Signatures of Supercritical Fluid Boundaries
• Unveiling the Puzzle of Brittleness in Single Crystal Iridium
• VASPilot: MCP-Facilitated Multi-Agent Intelligence for Autonomous VASP Simulations
• ToPolyAgent: AI agents for coarse-grained bead-spring topological polymer simulations
• Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
• Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential
• TorchSim: An efficient atomistic simulation engine in PyTorch
• Advancing Material Modeling in Hydrocodes Beyond Equations of State
• Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
• Diffusion in a d-dimensional rough potential
• Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
• Machine learning potential for predicting thermal conductivity of θ-phase and amorphous Tantalum Nitride
• Pseudo-grand canonical molecular dynamics via volumetrically controlled osmotic pressure
• Comparative molecular dynamics simulations of charged solid-liquid interfaces with different water models
• Efforts in Modeling the Mechanics and Chemistry of Energetic Materials Across Scales
• Learning the action for long-time-step simulations of molecular dynamics
• Etching-to-deposition transition in SiO₂/Si₃N₄ using CH_xF_y ion-based plasma etching: An atomistic study with neural network potentials
• Arrested Ostwald Ripening in Non-Equilibrium Systems
• Deriving effective electrode-ion interactions from free-energy profiles at electrochemical interfaces
• Transverse Self-Propulsion Enhances the Aggregation of Active Dumbbells
• Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
• Molecular dynamics of liquid–electrode interface by integrating <scp>Coulomb</scp> interaction into universal neural network potential
• Influence of Dispersity on the Relaxation of Entangled Polymers from Molecular Dynamics Simulations
• Defect migration in supercrystalline nanocomposites
• Tensor Networks for Liquids in Heterogeneous Systems
• Dynamically phase-separated states in driven binary dusty plasma
• Modulation of Non-equilibrium Structures of Active Dipolar Particles by an External Field
• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
• Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations
• PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes
• Silver-Based Self-Organized Resistive Switching Nanoparticle Networks with Neural-Like Spiking Behavior: Implications for Neuromorphic Computing
• Liquid water under vibrational strong coupling: An extended cavity Born–Oppenheimer molecular dynamics study
• Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles. [europepmc]
• Cohesin and CTCF control the dynamics of chromosome folding. [europepmc]
• Detecting and quantifying liquid-liquid phase separation in living cells by model-free calibrated half-bleaching. [europepmc]
• A Guide to In Silico Drug Design. [europepmc]
• Non-polar ether-based electrolyte solutions for stable high-voltage non-aqueous lithium metal batteries. [europepmc]
• Diversity of platinum-sites at platinum/fullerene interface accelerates alkaline hydrogen evolution. [europepmc]
• Role of Strong Localized vs Weak Distributed Interactions in Disordered Protein Phase Separation. [europepmc]
• Enabling selective zinc-ion intercalation by a eutectic electrolyte for practical anodeless zinc batteries. [europepmc]
• Realistic phase diagram of water from "first principles" data-driven quantum simulations. [europepmc]
• Ultra-long-range interactions between active regulatory elements. [europepmc]
• MBX: A many-body energy and force calculator for data-driven many-body simulations. [europepmc]
• DeePMD-kit v2: A software package for deep potential models. [europepmc]
• First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. [europepmc]
• Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation. [europepmc]
• Neighbor List Artifacts in Molecular Dynamics Simulations. [europepmc]
• A complex network of interdomain interactions underlies the conformational ensemble of monomeric TDP-43 and modulates its phase behavior. [europepmc]
• Surface stratification determines the interfacial water structure of simple electrolyte solutions. [europepmc]
• Direct prediction of intrinsically disordered protein conformational properties from sequence. [europepmc]
• Sequence-dependent material properties of biomolecular condensates and their relation to dilute phase conformations. [europepmc]
• Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. [europepmc]
• Biomolecular condensates form spatially inhomogeneous network fluids. [europepmc]
• CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed. [europepmc]
• Introductory Tutorials for Simulating Protein Dynamics with GROMACS. [europepmc]
• General-purpose machine-learned potential for 16 elemental metals and their alloys. [europepmc]
• Microenvironmental modulation breaks intrinsic pH limitations of nanozymes to boost their activities. [europepmc]

Related

• A Set of Tutorials for the LAMMPS Simulation Package
• Molecular Dynamics Simulations of Active Matter using LAMMPS
• LAMMPS-KOKKOS: Performance Portable Molecular Dynamics Across Exascale Architectures
• ls1 mardyn: The massively parallel molecular dynamics code for large\n systems
• GPT-4 as an interface between researchers and computational software: improving usability and reproducibility