/tools

CatLearn is a machine learning environment focused on atomic-scale modeling for surface science and catalysis. It provides utilities for building and testing machine learning models, including Gaussian Processes for predicting molecular properties and optimizing atomic structures.

ShakeNBreak is a computational tool designed for defect structure-searching in solid materials, utilizing chemically-guided bond distortions to identify ground-state and metastable structures of point defects. It automates the generation of distorted structures and supports various geometry optimization codes, making it a valuable resource for materials design and analysis.

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.

CatKit is a collection of computational tools aimed at facilitating research in catalysis. It includes modules for generating various catalytic structures and automating workflows, making it useful for researchers in the field of molecular catalysis.

The NVIDIA ALCHEMI Toolkit-Ops is a collection of GPU-optimized primitives designed to accelerate atomistic simulations in computational chemistry and material science. It includes functionalities for neighbor list computation, dispersion corrections, and electrostatic interactions, making it suitable for high-performance molecular simulations.

MCMD is a Monte Carlo and Molecular Dynamics simulation package designed for studying gas sorption in crystalline materials. It provides various simulation capabilities, including energy and force computations, and supports multiple molecular models and periodic systems.

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.

LLaMP is a multimodal retrieval-augmented generation framework designed to enhance large language models with high-fidelity materials knowledge. It allows for dynamic interaction with materials databases, making it useful for applications in materials informatics and molecular property prediction.

JuLIP.jl is a Julia library that facilitates the rapid implementation and testing of interatomic potentials and molecular simulation algorithms. It allows users to create and manipulate atomic systems, making it a valuable tool for molecular dynamics and materials science.

Calphy is a Python library designed for automated free energy calculations using LAMMPS as the molecular dynamics driver. It enables efficient computation of thermodynamic properties and phase transitions, making it a valuable tool for molecular simulations.

suanPan is an open-source finite element analysis framework designed for efficient simulations in solid mechanics and engineering applications. It supports parallel and heterogeneous computing, making it suitable for complex simulations, including those in molecular dynamics.

PaddleMaterials is an end-to-end toolkit based on the PaddlePaddle deep learning framework, aimed at facilitating the discovery and development of new materials. It supports various tasks such as property prediction and structure generation, making it a valuable resource for researchers in materials science.

LangSim is an application that leverages Large Language Models to assist in computational materials science by providing a natural language interface for querying scientific simulation codes and calculating physical properties. It aims to simplify the interaction with complex simulation tools, making them more accessible to users.

AI4S-agent-tools is a collection of agent-ready tools designed for scientific research, focusing on materials science and chemistry. It includes functionalities for deep learning simulations, materials screening, and reaction calculations, making it a valuable resource for molecular and computational chemistry applications.

The `kinisi` package is designed to quantify uncertainty in atomic and molecular displacements, enhancing the understanding of diffusion in materials through molecular dynamics simulations.

Chemeleon is a text-guided diffusion model designed for generating crystal structures based on natural language descriptions or specified chemical compositions. It aids in material discovery by allowing users to explore and create crystal structures, making it a valuable tool in computational chemistry.

pwtools is a Python package designed for pre- and postprocessing of atomistic calculations, primarily focused on software like Quantum Espresso, CPMD, CP2K, and LAMMPS. It includes powerful parsers and data types for managing calculation data, facilitating molecular simulations and analyses.

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.

MOFid is a system designed for the rapid identification and analysis of metal-organic frameworks. It provides tools for researchers to analyze these materials, which are significant in various applications including catalysis and gas storage.

Surfaxe is a Python package designed to automate the generation of surface slabs and facilitate density functional theory (DFT) calculations for surface properties. It provides tools for data processing, analysis, and visualization of results related to surface and bulk calculations in computational chemistry.

Nequix is a framework for training foundation models for materials science, focusing on interatomic potentials and phonon fine-tuning. It allows users to perform molecular simulations and property predictions using pre-trained models and custom datasets.

pyCOFBuilder is a Python package designed for the automated assembly of Covalent Organic Framework structures using a reticular approach. It allows users to generate a variety of COF structures based on specified building blocks and functional groups, facilitating high-throughput molecular design.

DiffCSP-PP is an implementation for generating crystal structures constrained by space groups. It includes functionalities for training models on datasets and evaluating crystal structure predictions, making it a useful tool for molecular design and generation in materials science.

MOFDiff is a diffusion model designed for generating coarse-grained structures of metal-organic frameworks (MOFs). It includes functionalities for optimizing these structures based on specific properties and simulating their behavior in gas adsorption scenarios.