DMFF (Deep Modeling Force Field) is a JAX-based Python library for differentiable molecular force field parameterization and energy/force evaluation. It supports classical force fields (bonded, Lennard-Jones, Coulomb), advanced polarizable multipole models (ADMP with PME, Thole damping, Slater damping), and machine learning potentials (sGNN, EANN). The library provides a generator pattern for constructing differentiable energy functions from XML force field definitions, an operator pattern for atom typing and virtual site placement, and a parameter management system (ParamSet) that integrates with JAX's automatic differentiation for gradient-based optimization. It also includes an OpenMM plugin (C++/CUDA) that can load trained JAX models via TensorFlow for production molecular dynamics simulations. The architecture is modular: the core `dmff` package contains subpackages for ADMP (polarizable multipole), classical forces, sGNN/EANN, common utilities (neighbor lists, constants), and an API layer (Hamiltonian, topology, parameter sets). The backend includes a C++ OpenMM plugin with Reference and CUDA platforms, serialization support, and a TensorFlow export path via `jax2tf`. The library is designed for research in force field development, enabling parameter fitting, free energy calculations (MBAR), and integration with machine learning models. While primarily a software library, it has strong research characteristics: it is accompanied by a published paper, includes example workflows for water models and PEG systems, and provides reference data for validation. The codebase demonstrates high craftsmanship in areas like PME implementation, subgraph neural network construction, and differentiable programming patterns, though some parts (e.g., the OpenMM plugin's TensorFlow dependency) show potential drift risks.
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.
This repository is a curated 'awesome list' (bibliography) of research papers, datasets, and software tools at the intersection of artificial intelligence and molecular dynamics/conformational analysis. It contains no executable code, data files, or notebooks—only a single README.md that organizes hundreds of references by topic (e.g., neural network potentials, force fields, generative models for protein conformations, AlphaFold-based methods). The repo serves as a comprehensive literature index for researchers in computational chemistry and structural biology, but it is not a functional software project or data pipeline. No raw data, transformation scripts, or analytical models are present; reproducibility is not applicable.