DeePMD-kit is a comprehensive, production-grade deep learning package for many-body interatomic potential energy representation and molecular dynamics simulation. It provides a unified framework for training and deploying machine learning interatomic potentials (MLIPs) across multiple backends: TensorFlow, PyTorch, JAX, and PaddlePaddle, with a backend-agnostic core (dpmodel) that ensures numerical consistency. The package supports a wide range of descriptor architectures (SeA, SeR, SeT, DPA1/2/3/4, attention-based, hybrid) and fitting targets (energy, force, virial, dipole, polarizability, density of states, spin, property). It integrates tightly with LAMMPS via custom pair styles and plugins, and also supports ASE, i-PI, GROMACS, and other MD engines. The codebase is highly modular, with a plugin registration system for models, descriptors, and losses, and offers extensive serialization capabilities for model export and compression. Pretrained models (DPA-3, DPA-4) are available for direct download and fine-tuning. The project is actively maintained with a large community, comprehensive test suites, and continuous integration across CPU and GPU platforms.
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++.
The Allegro package implements the Allegro E(3)-equivariant machine learning interatomic potential as an extension for the NequIP framework. It provides a neural network architecture that predicts energies and forces in atomic systems while being invariant to translations, rotations, and reflections. The model is built using a combination of scalar and tensor feature tracks, interacting through tensor product layers, and is designed to be strictly local with a configurable cutoff radius. The architecture centers around the `AllegroModel` function, which constructs a graph neural network. Input atomic data (positions, types, and optionally cell) is processed through embedding layers to generate initial scalar and tensor features. These features then pass through multiple Allegro layers, each consisting of tensor product operations (via the `Contracter` module) and mixing layers (MLPs). The final features are reduced to per-atom energies, summed to get the total energy, and forces and stresses are computed via automatic differentiation if requested. Key entry points include the `AllegroModel` function and the package's entry point for NequIP integration. The package relies on external dependencies such as NequIP, PyTorch, and e3nn, with optional accelerations via NVIDIA's CuEquivariance library or custom Triton kernels for GPU performance. The codebase demonstrates strong engineering practices, including performance optimizations for the tensor product bottleneck (through both the base `Contracter` implementation and GPU-accelerated variants), a clear modular structure, and comprehensive testing. Reusable components such as the `Contracter` and `MakeWeightedChannels` modules showcase efficient tensor product handling and feature weighting strategies that could be adapted to other equivariant neural network architectures with minimal modification.
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 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.