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initqp avatar

initqp/somd

0
View on GitHub↗
17 stars·2 forks·Python·AGPL-3.0·9 views

Somd

SOMD is an ab-initio molecular dynamics (AIMD) package designed for the SIESTA DFT code. The SOMD code provides some common functionalities to perform standard Born-Oppenheimer molecular dynamics (BOMD) simulations, and contains a simple wrapper to the Neuroevolution Potential (NEP) package. The…

Features

  • Molecular Dynamics - Molecular dynamics package optimized for SIESTA DFT code.

Star history

Star history chart for initqp/somdStar history chart for initqp/somd

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does initqp/somd do?

SOMD is an ab-initio molecular dynamics (AIMD) package designed for the SIESTA DFT code. The SOMD code provides some common functionalities to perform standard Born-Oppenheimer molecular dynamics (BOMD) simulations, and contains a simple wrapper to the Neuroevolution Potential (NEP) package. The…

What are the main features of initqp/somd?

The main features of initqp/somd are: Molecular Dynamics.

Which projects share features with initqp/somd?

Projects with overlapping indexed features include: lammps/lammps — This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements… chiang-yuan/muse — Muse (Mixture builder for simulation environments) is a Python package for rapidly building amorphous solids and… deepmodeling/dmff — DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable… fitsnap/fitsnap — A Python package for machine learning potentials with LAMMPS. icams/lammps-user-pace — You could get the supported version of LAMMPS from GitHub repository. jax-md/jax-md — Quickstart | Reference docs | Paper | NeurIPS 2020.

Projects sharing features with Somd

These projects share indexed features with Somd. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • lammps/lammpslammps avatar

    lammps/lammps

    2,783View on GitHub↗

    This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v

    C++kokkoslammpsmolecular-dynamics
    View on GitHub↗2,783
  • deepmodeling/dmffdeepmodeling avatar

    deepmodeling/DMFF

    197View on GitHub↗

    DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable implementation of molecular force field models. This project aims to establish an extensible codebase to minimize the efforts in force field parameterization, and to ease the force and…

    Python
    View on GitHub↗197
  • fitsnap/fitsnapFitSNAP avatar

    FitSNAP/FitSNAP

    186View on GitHub↗

    A Python package for machine learning potentials with LAMMPS.

    Python
    View on GitHub↗186
  • chiang-yuan/musechiang-yuan avatar

    chiang-yuan/muse

    10View on GitHub↗

    Muse (Mixture builder for simulation environments) is a Python package for rapidly building amorphous solids and liquid mixtures from relaxed solid-state structures on Materials Project. It uses Packmol for packing molecules into simulation cells and supports density equilibration through…

    Python
    View on GitHub↗10
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