Scymol is an open-source, cross-platform software package that runs and analyzes Molecular Dynamics (MD) simulations with customizable atomistic models. Scymol provides a convenient graphical user interface (GUI) and universal compatibility, with tools for creating or importing single molecules, mixtures, or crystalline systems, defining the equations and parameters that govern the atomic interactions, and choosing and customizing numerical methods that solve for particle trajectories. The results can be processed using a number of data analysis and plotting tools.
Scymol is designed to eliminate the limitations and restrictions shared by popular MD packages like LAMMPS, which do not have a GUI, or MedeA, which requires a costly license. Additionally, the source code is designed in Python or Fortran to enable users to easily introduce changes in functionalities and calculation methodologies. Scymol includes its own MD solver engine, unlike other GUI packages which rely on incorporating third-party solvers into their software. Scymol also offers data processing tools to set up the geometries of chemical systems or interpret results without the need to resort to other programs. Scymol computes thermodynamic (e.g., internal energy), mechanical (e.g., elastic modulus), and transport properties (e.g., viscosity) of atomistic systems.
Scymol is a completely free to use software package which offers scientists or researchers the opportunity to be quickly involved in the field of computational physics and chemical phenomena. Scymol is not finished yet; we are working hard to improve it in years to come.
The program is built so that users with little to no background in computational environments follow a series of simple steps to run MD from start to end without having to resort to other software packages.
Scymol is a new project that will be enhanced and upgraded. We have several objectives both short and long term:
• Implement semi-empirical and full Ab-Initio / Quantum Mechanics calculations to estimate electrostatic potentials, generate new force field parameters, and compute particle dynamics.
• Make Scymol user friendly for the simulation of fluid dynamics (not only molecular or atomistic dynamics).
• Allow Scymol to run on GPU instruction sets so that its solvers use the potential of GPU parallel computing.
• Convert the project into a cloud based solution so that jobs are submitted directly into powerful computers online.
• Implement machine learning algorithms to improve force field parameters.
• Implement machine learning algorithms to completely replace regular algebraic solvers with algorithms that are efficient, fast and accurate.