SchNetPack - Deep Neural Networks for Atomistic Systems
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Updated
Jul 28, 2026 - Python
SchNetPack - Deep Neural Networks for Atomistic Systems
Differentiable, Hardware Accelerated, Molecular Dynamics
macromolecular crystallography library and utilities
Public development project of the LAMMPS MD software package
Avogadro libraries provide 3D rendering, visualization, analysis and data processing useful in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.
Molsystem provides a general class for handling molecular and periodic systems
A deep learning package for many-body potential energy representation and molecular dynamics
Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.
Parsers and algorithms for computational chemistry logfiles
NequIP is a code for building E(3)-equivariant interatomic potentials
Python module for quantum chemistry
Semiempirical Extended Tight-Binding Program Package
Powerful, efficient particle trajectory analysis in scientific Python.
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.
Computational Crystallography Toolbox
Packmol - Initial configurations for molecular dynamics simulations
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