Differentiable, Hardware Accelerated, Molecular Dynamics
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Updated
Jul 26, 2026 - Jupyter Notebook
Differentiable, Hardware Accelerated, Molecular Dynamics
macromolecular crystallography library and utilities
SchNetPack - Deep Neural Networks for Atomistic Systems
Avogadro libraries provide 3D rendering, visualization, analysis and data processing useful in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.
A deep learning package for many-body potential energy representation and molecular dynamics
Molsystem provides a general class for handling molecular and periodic systems
Public development project of the LAMMPS MD software package
Parsers and algorithms for computational chemistry logfiles
Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.
Powerful, efficient particle trajectory analysis in scientific Python.
Semiempirical Extended Tight-Binding Program Package
NequIP is a code for building E(3)-equivariant interatomic potentials
Python module for quantum chemistry
Packmol - Initial configurations for molecular dynamics simulations
Computational Crystallography Toolbox
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
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