/tools

g-xTB is a development version of a general-purpose semiempirical quantum mechanical method that approximates molecular properties. It supports geometry optimization and numerical gradient calculations, making it useful for various molecular simulations and analyses.

The xtb-python repository offers a Python API for the extended tight binding (xtb) program, facilitating access to quantum chemistry calculations. It allows users to perform molecular simulations and analyses without requiring a separate installation of the xtb software.

AQME (Automated Quantum Mechanical Environments) offers workflows for generating molecular conformers and creating quantum mechanical input files. It also facilitates the post-processing of quantum mechanical output and the generation of molecular descriptors, making it a valuable tool for computational chemistry.

DQC is a differentiable quantum chemistry package that supports density functional theory and Hartree-Fock calculations. It enables applications such as learning exchange-correlation functionals and basis optimization, making it a valuable tool for molecular property prediction and simulations.

Auto-QChem is an automated workflow designed for the generation and storage of density functional theory (DFT) calculations specifically for organic molecules. It facilitates the prediction of molecular properties by streamlining the computational chemistry process.

dxtb is a framework that implements the Extended Tight-Binding methods in PyTorch, allowing for efficient computation of molecular properties like energy and forces. It integrates machine learning with quantum chemistry, enabling automatic differentiation and facilitating molecular simulations.

OpenFermion-PySCF is a plugin that allows the electronic structure package PySCF to interface with OpenFermion, facilitating quantum simulations of fermionic systems. It is designed for researchers working in quantum chemistry and molecular simulations.

Alchemy is a repository that offers tools for predicting molecular properties using graph neural networks. It includes a dataset for benchmarking AI models in quantum chemistry and provides implementations for various models like SchNet and MGCN.

LobsterPy is a Python package that automates the analysis of bonding information from LOBSTER outputs, providing features for machine learning studies. It allows users to generate plots and summaries of chemical bonding, making it a useful tool in computational materials science.

GradDFT is a JAX-based library that enables the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques. It allows for the training of neural functionals and performing DFT simulations, making it a valuable tool for predicting molecular properties and conducting quantum chemistry simulations.

ByteQC is a high-performance quantum chemistry package that supports various methods for quantum simulations, including mean-field calculations and many-body methods. It is optimized for GPU usage, enabling efficient simulations of complex molecular systems.

AIMNet is a neural network potential that accurately predicts chemical properties using an atoms-in-molecules approach. It provides a framework for molecular dynamics simulations and benchmarks for evaluating performance in property prediction.

DelFTa is an open-source toolbox designed for predicting quantum-mechanical properties of drug-like molecules using machine learning techniques. It employs advanced models to approximate DFT reference values and supports various molecular file formats for easy integration into workflows.

Psikit is a wrapper library for Psi4 and RDKit that facilitates quantum chemistry calculations, including the prediction of molecular properties such as HOMO and LUMO energies, structure optimization, and charge calculations. It is designed to assist in molecular design and cheminformatics tasks.

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.

QCSchema is a schema designed to standardize the representation of quantum chemistry data, allowing for easier integration and manipulation of data from various quantum chemistry packages. It aims to streamline workflows by providing a consistent format for output variables, making it easier to work with complex quantum chemistry computations.

NixOS-QChem is a repository that facilitates the integration of quantum chemistry software packages into the NixOS environment, enabling users to perform high-performance computing tasks related to molecular simulations and calculations. It supports various quantum chemistry tools, making it suitable for researchers in computational chemistry.

DeePTB is a deep learning package that accelerates ab initio electronic structure simulations, providing accurate predictions for large systems. It features components for local environment dependent Slater-Koster tight-binding and equivariant neural networks for quantum operators, making it suitable for a variety of molecular simulations.

The block2 code provides an efficient implementation of DMRG for quantum chemistry, enabling advanced simulations of molecular systems. It supports various features for electronic structure calculations and is designed for high performance and scalability.

OpenFermion-Psi4 is a plugin that allows the Psi4 electronic structure package to interface with OpenFermion, facilitating quantum simulations of fermionic systems. It is designed to support quantum chemistry applications, particularly in the context of molecular simulations.

MLIP Arena is a benchmark platform designed to evaluate machine learning interatomic potentials (MLIPs) beyond conventional error metrics. It aims to improve the predictive accuracy and efficiency of molecular modeling by assessing the physical soundness of MLIPs in real-world scenarios.

DeepDFT is an implementation of a model that predicts charge density around molecular structures. It is trained on datasets containing quantum mechanical density files and can be used to predict electron density for new molecular configurations.

Grid is a Python library that facilitates numerical integration, interpolation, and differentiation, primarily aimed at supporting density-functional theory calculations in quantum chemistry. It is designed to assist researchers in the quantum chemistry community with various computational tasks related to molecular properties.

The ai2-kit is a command line and Python toolkit that integrates artificial intelligence with ab initio methods for complex chemistry systems research. It includes various tools for predicting molecular properties, conducting simulations, and developing automated workflows in computational chemistry.