$ account openmm
OpenMM is a high-performance molecular dynamics simulation library that provides a unified API across CPU (with SIMD and JIT compilation via AsmJit) and GPU backends (CUDA, OpenCL, HIP). The codebase is structured into several layers: the public API (`openmmapi`) defines `System`, `Force`, `Integrator`, and `Context` classes; the OLLA layer abstracts kernel dispatch; platform modules (`reference`, `cpu`, `cuda`, `opencl`, `hip`) provide concrete kernel implementations; and plugins extend functionality with AMOEBA polarizable force fields, Drude oscillators, and ring-polymer MD. The project supports standard force fields (AMBER, CHARMM, GROMACS, Tinker), advanced algorithms (PME, GB/SA, custom forces via the Lepton expression parser), and features like metadynamics, replica exchange, checkpointing, and XML serialization. It is primarily a library for computational chemistry and biophysics, designed to be used through Python (with extensive application-level tools in `openmm.app`) or C++.
This repository provides the SPICE dataset, a collection of molecular structures and properties for machine learning and molecular simulation. It includes precomputed HDF5 files for amino acid ligands, the DES370K set of small organic molecules, dipeptides, ions, PubChem compounds, solvated amino acids, water clusters, and more. The accompanying Python scripts generate these datasets from raw SDF and PDB files, performing tasks such as molecular filtering, energy computation, and solvation. The repository is primarily a data resource rather than a software application, with scripts that are tightly coupled to the specific dataset generation pipeline. It lacks dependency pinning and formal testing, but the data files are directly usable for training and benchmarking molecular models.