/tools

NequIP is an open-source framework for constructing E(3)-equivariant interatomic potentials, enabling accurate molecular simulations. It supports multi-GPU training and integrates with existing molecular dynamics software, making it a valuable tool for researchers in computational chemistry and materials science.

The 'orb-models' repository offers pretrained forcefield models designed for atomic simulations, enabling users to predict molecular properties and perform molecular dynamics simulations. It supports various molecular systems, including metal complexes and biomolecules, and allows for fine-tuning with custom datasets.

Allegro is an open-source code that implements an E(3)-equivariant deep learning interatomic potential, enabling highly scalable and accurate molecular simulations. It integrates with the NequIP framework and supports LAMMPS for efficient atomistic simulations.

The Open Forcefield Toolkit is a Python toolkit designed for the development and application of molecular mechanics force fields, specifically utilizing the SMIRNOFF format. It provides tools for creating and manipulating molecular systems, facilitating molecular simulations and dynamics.

The openmmforcefields repository offers CHARMM and AMBER force fields for use with OpenMM, enabling the parameterization of biomolecular systems and small molecules. It supports various force fields and provides tools for generating parameters for small molecules, making it a valuable resource for molecular simulations.

Moltemplate is a cross-platform text-based molecule builder that prepares simulations for LAMMPS, supporting both coarse-grained and all-atom models. It facilitates the creation of molecular models using various force fields and integrates with other molecular modeling tools.

TensorMol is a Python-based tool that integrates TensorFlow with molecular simulations, allowing for the training of neural network force fields. It supports various simulation types, including molecular dynamics and Monte Carlo methods, and is capable of optimizing molecular geometries and predicting dynamic properties.

ASE_ANI is a prototype interface for the ANI-1x and ANI-1ccx neural network potentials, enabling predictions of molecular properties and facilitating molecular dynamics simulations. It is designed for use within the Atomic Simulation Environment (ASE) and supports various applications in computational chemistry.

SO3LR is a pretrained machine-learned force field for molecular simulations that integrates neural network-based interactions with universal pairwise force fields. It supports various molecular dynamics simulations and geometry optimizations, making it a valuable tool for computational chemistry applications.

fftool is a Python tool that facilitates the creation of force field input files for molecular dynamics simulations. It supports systems composed of molecules, ions, or extended materials and integrates with various molecular dynamics packages like LAMMPS, OpenMM, and GROMACS.

Neural-Network-Models-for-Chemistry is a repository that provides a variety of neural network models aimed at advancing computational chemistry. It includes methods for quantum chemistry, force fields, and molecular simulations, facilitating the prediction of molecular properties and behaviors.

Time Machine is a high-performance differentiable molecular dynamics and forcefield engine that allows for the computation of derivatives of observables with respect to forcefield parameters. It supports various functional forms for molecular simulations and is designed for optimizing forcefield parameters.

sGDML is a reference implementation of the Symmetric Gradient Domain Machine Learning model, aimed at constructing accurate molecular force fields. It facilitates atomistic simulations and predictions of molecular properties, making it a valuable tool in computational chemistry.

Tinker is a comprehensive software package for molecular mechanics and dynamics, offering various algorithms and support for multiple force fields. It is designed for simulating large molecular systems, including biopolymers, and includes features for optimization and free energy calculations.

BAMBOO is an AI-driven machine learning force field designed for precise and efficient simulations of lithium battery electrolytes. It provides tools for training models and running molecular dynamics simulations, along with datasets for training and validation.

Foyer is an open-source Python package that facilitates atom-typing and the application of force fields in molecular simulations. It generates input files for various simulation engines and aims to improve reproducibility in computational research.

MLFF is a repository for training and developing machine learned force fields using the SO3krates transformer. It allows users to perform molecular dynamics simulations and evaluate molecular properties through trained models.

QUBEKit is a toolkit for deriving bespoke force fields from quantum mechanical calculations, enabling users to automate the parameterization of molecular mechanics. It supports various molecular types and provides functionalities for both single and bulk analyses.

OpenFF Interchange is a project designed for storing and manipulating parametrized chemical systems, facilitating interoperability between different molecular simulation software. It provides APIs for exporting molecular data to common formats, supporting various force fields and chemical topologies.

LigParGen is an automatic parameter generator for small organic molecules using OPLS-AA force fields. It accepts multiple molecular formats and produces output files compatible with various molecular dynamics software, facilitating molecular simulations and parameterization.

BALL is a Biochemical Algorithms Library that provides tools for molecular simulations, cheminformatics, and drug design. It supports various biochemical computations and is useful for researchers in computational biology and chemistry.

This repository contains OPLS-AA force field parameters for ionic liquids, enabling accurate molecular simulations. It includes both unscaled and scaled parameters, along with a tutorial for constructing and simulating ionic liquid systems using GROMACS.

The CL&P force field repository offers a set of parameters for simulating ionic liquids using molecular dynamics software like LAMMPS, DL_POLY, and GROMACS. It includes input files and tools for building molecular configurations, making it a valuable resource for researchers in computational chemistry and materials science.

Grappa is a machine learned molecular mechanics force field that utilizes graph neural networks to predict bonded parameters for molecular simulations. It integrates with GROMACS and OpenMM, allowing users to parametrize systems and train custom models using various molecular datasets.