/tools

The `easyunfold` package simplifies the process of obtaining effective band structures from supercells, particularly in the context of defects and disorder in materials. It automates the generation of k-points and the unfolding of band structures, making it a useful tool for electronic structure analysis in computational chemistry.

The `load-atoms` package is designed for loading open access datasets related to atomistic materials science. It facilitates the downloading and manipulation of datasets, making it useful for researchers in computational chemistry and materials science.

GREA is a source code repository for a method that enhances graph neural networks for predicting molecular properties, particularly in the context of polymers. It includes implementations for various molecular datasets and provides tools for conducting experiments on these datasets.

LLM4Mol is a repository that explores the application of large language models in molecular design and protein research. It serves as a hub for studies and techniques that leverage AI to advance understanding in molecular properties and material science.

Jrystal is a JAX-based framework designed for differentiable density functional theory calculations, enabling efficient optimization workflows for quantum properties of materials. It supports solid-state calculations and is optimized for GPU performance, making it suitable for advanced electronic structure computations.

dZiner is an AI framework designed for the rational inverse design of materials, allowing for the generation and assessment of new molecular candidates based on user-defined properties and constraints. It incorporates a human-in-the-loop approach to refine designs and improve chemical feasibility.

APEX (Alloy Property EXplorer) is a Python package that facilitates the calculation of various alloy properties through cloud-native workflows. It integrates multiple computational approaches, including molecular dynamics and first-principles calculations, to streamline the process of alloy property testing.

MOF-ChemUnity is a knowledge graph that unifies computational and experimental data for over 15,000 metal-organic frameworks. It allows users to query and retrieve information about MOFs, including their properties and applications, enhancing the understanding and research of these materials.

pyWannier90 is a Python interface for the Wannier90 package, enabling the construction of maximally-localized Wannier functions from wave functions obtained via PySCF or VASP. It facilitates analysis of electronic properties in solid-state materials, contributing to molecular simulations and quantum chemistry research.

This repository contains models for spectral neighbor analysis potential (SNAP) used in molecular simulations. It includes force field parameters and training data, facilitating the development and application of these models in computational chemistry.

ASE.jl offers Julia bindings for the Atomic Simulation Environment, enabling users to perform molecular simulations and calculations. It facilitates the generation of molecular structures and integrates with the JuLIP framework for enhanced functionality.

Build Your Agent is an open-source initiative that provides a collection of intelligent agents designed for scientific research, focusing on materials property prediction and drug discovery. It aims to facilitate the development and deployment of AI-powered tools for various research workflows.

Uni-MOF is a transformer-based framework designed for high-accuracy predictions of gas adsorption in metal-organic frameworks (MOFs). It utilizes a large dataset of MOF structures to learn representations and predict various properties, making it a valuable tool in computational chemistry and materials science.

`allegro-pol` is an extension of the `nequip` framework designed for predicting the electric response of materials, including properties like polarization and polarizability, using machine learning models. It provides tools for training and processing data related to these predictions.

LeMat-GenBench is a comprehensive benchmarking framework designed to evaluate crystal generative models across various metrics such as validity, diversity, and stability. It facilitates the assessment of material generation models, making it relevant for molecular design and optimization in computational chemistry.

AGF-phonon-transport is a Matlab implementation of the Atomistic Green's function method for calculating phonon transmission in materials. It is designed to simulate heat conduction in solids using interatomic potentials, making it relevant for molecular dynamics studies.

GPUGA is a tool designed for empirical potential fitting using a genetic algorithm, optimized for GPU performance. It allows for efficient modeling of molecular interactions, particularly in the context of materials like diamond silicon.

V2DB is a database and tool for generating and predicting properties of novel two-dimensional materials through virtual screening. It utilizes machine learning models to predict various material properties, facilitating the discovery of stable and functional 2D materials.

The cnt-gaff repository contains tools for building atomistic structures of carbon nanotubes and assigning generalized Amber force field parameters. It includes scripts for generating CNTs with functional groups and setting up simulations for molecular dynamics studies.

The mofstructure package is designed for the manipulation of metal-organic frameworks (MOFs) and other porous materials. It allows users to compute geometric properties, remove unbound guest molecules, and deconstruct MOFs into building units while providing cheminformatics data such as SMILES and InChI identifiers.

This repository contains a tool for optimizing crystal structures using machine learning models. It allows users to perform structure optimization and single point evaluations, making it useful for researchers in materials science and computational chemistry.

The repository contains data and tools for predicting molecular properties of polymers using a graph representation. It includes datasets of computed electron affinities and ionization potentials, as well as instructions for reproducing the associated calculations.

Reciprocal Space Attention (RSA) is a machine learning framework designed to capture long-range interactions in molecular systems by utilizing a Fourier domain approach. It enhances existing interatomic potentials by addressing the limitations of local and semi-local models, making it suitable for various chemical and materials systems.

PhyNEO is a hybrid force field development workflow that enhances traditional physics-based methods with neural networks to achieve accurate molecular simulations. It focuses on generating force fields for organic molecules and polymers, facilitating molecular dynamics simulations and analysis of properties like conductivity and diffusion coefficients.