Q6 VS Molly.jl

Compare Q6 vs Molly.jl and see what are their differences.

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Q6 Molly.jl
1 1
31 366
- 2.5%
3.2 9.4
7 months ago 8 days ago
Fortran Julia
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Q6

Posts with mentions or reviews of Q6. We have used some of these posts to build our list of alternatives and similar projects.
  • EVB(Empirical Valence Bond) in Gromacs?
    1 project | /r/comp_chem | 12 May 2021
    That being said, reading just the first papers that showed up on google, it looks like Q6 is an opensource software that is based on Gromacs and has a similar input/output regime as Gromacs that is capable of doing EVB. Repo is here: https://github.com/qusers/Q6

Molly.jl

Posts with mentions or reviews of Molly.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2020-12-21.

What are some alternatives?

When comparing Q6 and Molly.jl you can also consider the following projects:

awesome-python-chemistry - A curated list of Python packages related to chemistry

Agents.jl - Agent-based modeling framework in Julia

xtb - Semiempirical Extended Tight-Binding Program Package

NBodySimulator.jl - A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

nwchem - NWChem: Open Source High-Performance Computational Chemistry

DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

simple-dftd3 - reimplementation of the DFT-D3 program

DynamicalBilliards.jl - An easy-to-use, modular, extendable and absurdly fast Julia package for dynamical billiards in two dimensions.