Qb@ll (Qball)

Qb@ll (also written as Qball or qb@ll) is a first-principles molecular dynamics code developed at Lawrence Livermore National Laboratory. It computes electronic structure of atoms, molecules, solids, and liquids using Density Functional…

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Overview

Qb@ll (also written as Qball or qb@ll) is a first-principles molecular dynamics code developed at Lawrence Livermore National Laboratory. It computes electronic structure of atoms, molecules, solids, and liquids using Density Functional Theory with a plane-wave basis. Qb@ll is a fork of the Qbox code by François Gygi, optimized for high-performance computing including Real-Time TDDFT capabilities.

Reference Papers

Reference papers are not yet linked for this code.

Full Documentation

Official Resources

  • Homepage: https://computing.llnl.gov/projects/qball
  • Documentation: https://github.com/LLNL/qball/blob/master/README.md
  • Source Repository: https://github.com/LLNL/qball
  • License: GNU General Public License v3.0 (LLNL-CODE-635376)

Overview

Qb@ll (also written as Qball or qb@ll) is a first-principles molecular dynamics code developed at Lawrence Livermore National Laboratory. It computes electronic structure of atoms, molecules, solids, and liquids using Density Functional Theory with a plane-wave basis. Qb@ll is a fork of the Qbox code by François Gygi, optimized for high-performance computing including Real-Time TDDFT capabilities.

Scientific domain: Ab initio molecular dynamics, electronic structure, real-time electron dynamics
Target user community: HPC users at national labs and institutions needing scalable plane-wave DFT/TDDFT

Theoretical Methods

  • Density Functional Theory (DFT)
  • Plane-wave basis set
  • Norm-conserving pseudopotentials
  • DFT-GGA and Hybrid DFT functionals
  • Born-Oppenheimer molecular dynamics
  • Car-Parrinello molecular dynamics
  • Real-Time TDDFT (RT-TDDFT) - developed in Qb@ch branch
  • NVT simulations with stochastic thermostats

Capabilities

  • Ground-state DFT for all system types
  • First-principles molecular dynamics (FPMD)
  • Born-Oppenheimer MD with forces from DFT
  • Car-Parrinello MD for efficient dynamics
  • Real-Time TDDFT (via Qb@ch development)
  • Hybrid functionals support
  • Large-scale parallel execution
  • HPC optimized (designed for supercomputers)

Key Strengths

HPC Performance:

  • Designed for leadership-class supercomputers
  • Excellent parallel scaling
  • Blue Gene/Q optimized configurations
  • MPI + OpenMP hybrid parallelization

Qbox Foundation:

  • Fork of established Qbox code
  • Plane-wave accuracy
  • Proven algorithms
  • Active LLNL support

RT-TDDFT Development:

  • Qb@ch branch (UNC Chapel Hill) focuses on RT-TDDFT
  • Electron dynamics capabilities
  • Continued development beyond Qbox

Molecular Dynamics:

  • Both BO-MD and CP-MD
  • Various thermostats
  • Production-quality simulations

Inputs & Outputs

  • Input formats:

    • Qball input files (.i)
    • Coordinate files (.sys)
    • Pseudopotential files (.xml)
    • Example inputs in examples/ directory
  • Output data types:

    • Energies and forces
    • MD trajectories
    • Electronic structure data
    • Wavefunction outputs

Interfaces & Ecosystem

  • Build system: GNU Autotools (autoconf, automake)
  • Dependencies: BLAS, LAPACK, ScaLAPACK, FFTW
  • Parallelization: MPI + OpenMP
  • Platforms: Linux HPC clusters, Blue Gene systems

Advanced Features

RT-TDDFT (Qb@ch):

  • Real-time propagation
  • Strong-field dynamics
  • Continued development at UNC

HPC Optimization:

  • Vendor-specific optimizations (IBM ESSL)
  • Custom parallel strategies
  • Designed for 10,000+ cores

Performance Characteristics

  • Speed: Highly optimized for large core counts
  • Scaling: Excellent to thousands of MPI ranks
  • Accuracy: Plane-wave systematic convergence
  • Memory: Distributed memory model

Computational Cost

  • Ground state: Plane-wave standard scaling
  • MD: Efficient for large trajectories
  • RT-TDDFT: Time-step dependent
  • Parallelization: Near-linear scaling on HPC

Limitations & Known Constraints

  • Compilation: Requires HPC environment and libraries
  • Learning curve: HPC expertise helpful
  • Pseudopotentials: Requires compatible formats
  • Documentation: Technical focus, less beginner-friendly
  • INQ successor: LLNL now developing GPU-focused INQ code

Comparison with Other Codes

  • vs Qbox: Qb@ll fork with LLNL optimizations and Qb@ch RT-TDDFT
  • vs VASP: Both plane-wave; Qb@ll open-source, HPC optimized
  • vs QE: Qb@ll smaller codebase, HPC focus; QE broader features
  • vs SALMON: Both RT-TDDFT capable; Qb@ll also strong in MD
  • Unique strength: HPC optimization, combined MD + RT-TDDFT development

Application Areas

  • High-pressure physics
  • Warm dense matter
  • Extreme conditions simulations
  • Materials under shock
  • Large-scale molecular dynamics
  • Ultrafast dynamics (via Qb@ch)

Best Practices

  • Use example input files as templates
  • Configure for target HPC architecture
  • Consult LLNL documentation for optimization
  • Contact maintainers for support

Community and Support

  • Open-source GPL v3
  • LLNL development team
  • GitHub repository with 8 contributors
  • Contact: Erik Draeger, Xavier Andrade (LLNL)
  • Academic development at UNC (Qb@ch)

Verification & Sources

Primary sources:

  1. GitHub repository: https://github.com/LLNL/qball
  2. LLNL Computing: https://computing.llnl.gov/projects/qball
  3. Original Qbox: http://qboxcode.org/

Secondary sources:

  1. Qb@ch development at UNC Chapel Hill
  2. LLNL publications using Qb@ll

Confidence: VERIFIED

  • Repository: ACCESSIBLE (GitHub, LLNL)
  • License: GPL v3 (LLNL-CODE-635376)
  • Active: 8 contributors, ongoing development

Verification status: ✅ VERIFIED

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