Reverse Dependencies of ase

The following projects have a declared dependency on ase:

• ipyoptimade — Jupyter client for searching structures through OPTIMADE API
• ipyvasp — A processing tool for VASP DFT input/output processing in Jupyter Notebook.
• janus-core — Tools for machine learnt interatomic potentials
• jaxmd-tools — no summary
• jittoku — Useful scripts for QE etc.
• kgcnn — General Base Layers for Graph Convolutions with Keras
• kgrid — Reciprocal space sampling for atomistic crystal structures
• KinBot — Automated reaction kinetics for gas-phase species
• kinisi — Efficient estimation of diffusion processes from molecular dynamics.
• kmcos — kMC modeling on steroids
• kultools — Utility package for atomistic simulations using ASE and VASP
• lammpsinputbuilder — LammpsInputBuilder (or LIB) is a Python library designed to generate Lammps inputs from a molecular model, a forcefield, and a high level definition of a simulation workflow.
• LatticeFinder — This program is designed to give the lattice constants for a 3D crystal lattice or a 2D system, such as graphene (in development).
• lawaf — A library for constructing Lattice and other Wannier functions
• LEAFeatures — Local Enviroment-induced Atomic Features (LEAF)
• libdescriptor — Fully differentiable descriptors for atomistic systems
• lightshow — A one-stop-shop for computational spectroscopy
• load-atoms — Large Open Access Datasets for Atomistic Materials Science (LOAD-AtoMS)
• m3gnet — Materials Graph with Three-body Interactions
• mace-models — Access pre-trained MACE models
• mace-torch — no summary
• macrodensity — no summary
• madas — The MAterials DAta Similarity framework.
• Martinoid — This module was inspired by martinize (http://cgmartini.nl/index.php/tools2/proteins-and-bilayers/204-martinize) and has been created to perform automatic topology building of peptoids within the MARTINI forcefield (v2.1) in the GROMACS program.
• masci_tools — masci-tools is a collection of tools for materials science.
• mat3ra-api-examples — Mat3ra API Examples
• mat3ra-made — MAterials DEfinitions and/or MAterials DEsign library.
• matador-db — MATerial and Atomic Databases Of Refined structures.
• matbench-discovery — A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures
• matcalc — Calculators for materials properties from the potential energy surface.
• matdb — Welcome to MatDB, a material science database designed to be light weight and portable.
• materialist — no summary
• materials-learning-algorithms — Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
• materials-visualization — Routines to visualize atomic models in jupyter
• matgl — MatGL is a framework for graph deep learning for materials science.
• matgraphdb — Welcome to MatGraphDB, a powerful Python package designed to interface with primary and graph databases for advanced material analysis.
• matid — MatID is a Python package for identifying and analyzing atomistic systems based on their structure.
• matminer — matminer is a library that contains tools for data mining in Materials Science
• matscipy — Generic Python Materials Science tools
• mattersim — MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.
• MAZE-sim — Multiscale Zeolite Atomic simulation Environment (MAZE)
• mbGDML — Create, use, and analyze machine learning potentials within the many-body expansion framework
• MCPoly — Useful tools for Computational Chemistry for polymers
• MDANSE — MDANSE Core package - Molecular Dynamics trajectory handling and analysis code
• mddatasetbuilder — A script to generate molecular dynamics (MD) datasets for machine learning from given LAMMPS trajectories automatically.
• mdinterface — mdinterface: A package for building interface systems in Molecular Dynamics simulations.
• mechviz — MechViz -- Python-based toolkit for the analysis and visualization of mechanical properties of materials
• mff — Gaussian process regression to extract non-parametric 2- and 3- body force fields.
• miko-analyzer — Analyzing tool for deep learning based chemical research.
• mincepy-sci — Plugins to enable common scientific and machine learning type to be saved by mincePy
• minflow — A basic package for a customized workflow using quantum espresso or abinit.
• minimulti — Mini Extendable framework of multi Hamiltonian
• mir-flare — Fast Learning of Atomistic Rare Events
• mir-pysampling — no summary
• mkite-core — distributed computing for high-throughput materials simulation
• mkite-db — mkite: distributed computing platform for high-throughput materials simulations
• mkits — multi-DFT codes assistant program.
• ml4chem — Machine learning for chemistry and materials.
• mlacs — Machine-Learning Assisted Canonical Sampling
• mlcalcdriver — A package to drive atomic calculations using machine learned models.
• mlip-arena — Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics
• mlipx — Machine-Learned Interatomic Potential eXploration
• MLWC — MMLWC (machine learning Wannier center)
• mofchecker — Perform sanity checks for MOFs.
• mofdscribe — Ecosystem for digital reticular chemistry
• moffragmentor — Splits MOFs into metal nodes and linkers.
• mofpy — Python package to handle MOF structures and perform various analysis.
• mofstructure — A Python tool to deconstruct MOFs into building units and compute porosity. The code removes guests from MOFs and all porous systems, computes SMILES strings and InChIKeys of all building units.
• moftransformer — moftransformer
• mofun — Find and replace functional groups in any given periodic structure.
• Molara — A visualisation tool for chemical structures.
• molbar — Molecular Barcode (MolBar): Molecular Identifier for Organic and Inorganic Molecules
• molecular-builder — Package for building molecular systems
• MolParse — Package for parsing, writing, and modifying molecular structure files
• molsaic — Python package for performing cluster-cutout calculations
• moyopy — Python binding of Moyo
• mpdd-alignn — A version of the NIST-JARVIS ALIGNN optimized in terms of model performance and to some extent reliability, for large-scale deployments over the MPDD infrastructure by Phases Research Lab.
• mpinterfaces — High throughput analysis of interfaces using VASP and Materials Project tools
• musConv — Generate supercell and check supercell convergence with SCF forces
• muse-xtal — no summary
• muspinsim — Full quantum simulation of muon experiments
• nano-net — Python framework for tight-binding computations
• narupatools — Tools and extensions for Narupa
• NepTrain — NEP自动训练
• nepx — Python interface for NEP
• nequip — NequIP is an open-source code for building E(3)-equivariant interatomic potentials.
• nfflr — neural force field learning toolkit
• NISP — The Nanocluster Interpolation Scheme Program (NISP) is designed to perform an interpolation scheme that gives idea of the types of perfect, closed-shell and open-shell clusters for a cluster at selected sizes.
• nnpackage — NNPackage - Deep Neural Networks for Atomistic Systems (based on SchNetPack)
• nomad-normalizer-plugin-system — System normalizer plugin for NOMAD.
• nomodeco — a python package for the determination of optimal internal coordinate systems for molecular structures
• nvcs — nglview wrapper for crystal structure
• NXSP-to-xyz — A Python tool
• occuprob — A tool for calculating occupation probabilities and ensemble-averaged properties via the superposition approximation.
• oganesson — oganesson enables rapid AI workflows for material science and chemistry
• ogreinterface — A Python library used to generate and optimize epitaxial inorganic interface structures.
• openfermion — The electronic structure package for quantum computers.
• OpenMechanochem — python module for mechanochemical simulations
• openqdc — ML ready Quantum Mechanical datasets
• optimade — Tools for implementing and consuming OPTIMADE APIs.