/tools

DeepChem provides an open-source toolchain that facilitates the application of deep learning in drug discovery, quantum chemistry, and biology. It supports various molecular tasks such as property prediction, molecular generation, and offers extensive tutorials for users to learn and apply these techniques.

PennyLane is an open-source quantum software platform designed for quantum computing, quantum machine learning, and quantum chemistry. It provides tools for building quantum circuits and algorithms, with applications in molecular property prediction and simulations.

OpenFermion is an open-source Python package that focuses on compiling and analyzing quantum algorithms for simulating electronic structures. It includes data structures and tools for manipulating representations of fermionic systems, making it a valuable resource for quantum chemistry applications.

PySCF is a Python-based framework designed for quantum chemistry simulations, enabling users to perform various calculations related to molecular properties and behaviors. It supports density functional theory and other quantum mechanical methods, making it a valuable tool for researchers in computational chemistry.

Psi4 is an open-source quantum chemistry software package that performs efficient and high-accuracy simulations of molecular properties. It is designed for both users and developers, providing a flexible Python interface and supporting a wide range of molecular types.

CP2K is a quantum chemistry and solid state physics software package that enables atomistic simulations of various systems, including molecular and biological ones. It supports multiple modeling methods and can perform simulations such as molecular dynamics, energy minimization, and transition state optimization.

SchNetPack is a toolbox that facilitates the development and application of deep neural networks for predicting quantum-chemical properties and potential energy surfaces of molecules and materials. It includes features for training models on benchmark datasets and supports molecular dynamics simulations, making it a comprehensive tool for atomistic machine learning.

The xtb program package is designed for semiempirical quantum chemistry calculations, enabling users to perform molecular simulations and predict various molecular properties. It supports atomistic simulations and is applicable to a wide range of molecular types.

This repository contains demonstrations of quantum machine learning and quantum chemistry applications using the PennyLane library. It provides tutorials and implementations that showcase various techniques in quantum computing relevant to molecular research.

NWChem is an open-source software package designed for high-performance computational chemistry. It provides tools for electronic structure calculations and molecular simulations, enabling researchers to study molecular properties and dynamics effectively.

TorchANI 2.0 is an open-source library that facilitates the training and application of ANI-style neural network interatomic potentials. It supports molecular dynamics simulations and is designed for high-performance computation in quantum chemistry, making it a valuable tool for researchers in the field.

DFTK.jl is a density-functional toolkit designed for plane-wave density-functional theory (DFT) calculations. It provides routines for electronic structure calculations, enabling researchers to predict molecular properties and perform simulations in solid-state research.

DeepQMC is an open-source software suite that utilizes deep learning for variational optimization of molecular wave functions in quantum Monte Carlo simulations. It allows for the simulation of electronic ground and excited states, making it a valuable tool in the field of quantum chemistry.

cclib is a Python library designed to parse output files from various computational chemistry packages, enabling researchers to implement and utilize algorithms for analyzing molecular data in a platform-independent manner.

Psi4NumPy combines the Psi4 electronic structure package with NumPy to create an interactive environment for quantum chemistry education and development. It includes reference implementations of quantum chemical methods and tutorials for learning how to program these methods.

QMCPACK is an open-source software package that utilizes many-body ab initio Quantum Monte Carlo methods to compute the electronic structure of various molecular and solid-state systems. It is optimized for high-performance computing environments and supports GPU acceleration.

This repository serves as a directory for various Fortran codes related to computational chemistry and molecular dynamics. It includes tools for simulating molecular interactions and properties, making it a valuable resource for researchers in the field.

CREST (Conformer-Rotamer Ensemble Sampling Tool) automates the exploration of low-energy molecular chemical space using efficient force-field and semiempirical quantum mechanical methods. It provides capabilities for creating and analyzing molecular structure ensembles, making it a valuable tool for computational chemistry.

OpenFermion-Cirq provides quantum circuits designed for simulating quantum chemistry and materials. Although it is deprecated, its functionality is relevant for molecular simulations and understanding electronic structures in molecules.

SchNet is a deep learning architecture that provides insights into quantum-mechanical observables of atomistic systems. It is used for predicting properties such as total energy and forces in molecular systems, making it a valuable tool in computational chemistry.

gpu4pyscf is a plugin that enables GPU acceleration for the PySCF package, facilitating efficient quantum chemistry calculations such as SCF and DFT. It includes features for geometry optimization and supports various molecular types, enhancing the performance of molecular simulations.

quacc is a flexible platform designed for computational materials science and quantum chemistry, enabling users to run various workflows for molecular simulations and property predictions efficiently. It integrates with existing resources and provides a unified interface for managing computational tasks.

Qrisp is a high-level framework designed for quantum algorithm development, providing tools for intuitive programming and efficient implementation of quantum algorithms. It includes capabilities for simulating molecular systems, making it relevant for researchers in computational chemistry.

nablaDFT is a comprehensive dataset and benchmark designed for evaluating neural network potentials in molecular property prediction and Hamiltonian prediction. It includes a large collection of drug-like molecules with calculated electronic properties, making it a valuable resource for computational chemistry and machine learning applications in drug discovery.