Reverse Dependencies of pymatgen

The following projects have a declared dependency on pymatgen:

• abics — ab-Initio Configuration Sampling tool kit
• abinitostudio — A studio for first-principles calculations.
• ai2-kit — no summary
• aicon — This is the AICON module.
• aiida-abinit — The AiiDA plugin for ABINIT.
• aiida-aimall — A plugin to interface AIMAll with AiiDA
• aiida-common-workflows — Package that defines common interfaces for workflows that are implemented in AiiDA for various quantum engines.
• aiida-core — AiiDA is a workflow manager for computational science with a strong focus on provenance, performance and extensibility.
• aiida-gaussian — AiiDA plugin for the Gaussian quantum chemistry software.
• aiida-kkr — AiiDA plugin for the JuKKR codes
• aim2dat — Automated Ab-Initio Materials Modeling and Data Analysis Toolkit: Python library for pre-, post-processing and data management of ab-initio high-throughput workflows for computational materials science.
• aimsgb — aimsgb is a python library for generatng the atomic coordinates of periodic grain boundaries.Copyright © 2018 The Regents of the University of California.All Rights Reserved. See more in Copyright.
• amdnet — Structure motif-centric learning framework for inorganic crystalline systems.
• amof — A python package to analyze Molecular Dynamics (MD) trajectories of amorphous Metal-Organic Frameworks (MOFs).
• amp-flow — DeepMD-kit integration with Parsl workflow tools to accelerate development of Deep Potentials
• amset — AMSET is a tool to calculate carrier transport properties from ab initio calculation data
• apex-flow — Alloy Properties EXplorer using simulations
• asr — ASE recipes for calculating material properties
• atom2vec — A python implement of Atom2Vec: a simple way to describe atoms for machine learning
• atomate — atomate has implementations of FireWorks workflows for Materials Science
• atomate2 — atomate2 is a library of materials science workflows
• atomate2-turbomole — The atomate2-turbomole package is a workflow package for Turbomole
• auglichem — Data augmentation of molecules and crystals.
• autocat — Tools for automated generation of catalyst structures and sequential learning
• automatminer — automated machine learning for materials science
• average-minimum-distance — Descriptors of crystals based on geometry (isometry invariants).
• band-cal-parallel — HamGNN tool
• BayesOpt4dftu — no summary
• bgnet — bgnet
• bsym — A Basic Symmetry Module
• BuckFit — Modular potential fitting code for classical MD buckingham potentials
• BVLain — The Bond valence site energy calculator
• CASTING — A continuous action space tree search for inverse design (CASTING)
• catflow — Analyzing tool for deep learning based chemical research.
• cavd — Crystal structure Analysis by Voronoi Decomposition
• CCNB — no summary
• chemcoord — Python module for dealing with chemical coordinates.
• chgnet — Pretrained Universal Neural Network Potential for Charge-informed Atomistic Modeling
• chic-lib — A set of tools for coarse-graining and back-mapping frameworks.
• cif-retriever — CIF file retriever form pymatgen
• comgen — explore chemical compositions
• compare-geoms — This package compares a list of molecular geometries to the geometries in large datasets.
• crescendo — Machine learning made easy
• crystal-functions — Functions to be used with the CRYSTAL code.
• crystal-toolkit — no summary
• CRYSTALpytools — Python tools for the CRYSTAL code developed and mantained by the CRYSTAL code developers.
• custodian — A simple JIT job management framework in Python.
• czone — An open source python package for generating nanoscale+ atomic scenes
• dcdftbmd-tools — A Toolkit for handling DCDFTBMD input/output
• dftfit — Ab-Initio Molecular Dynamics Potential Development
• dockonsurf — Code to systematically find the most stable geometry for molecules on surfaces
• doped — Python package to setup, process and analyse solid-state defect calculations with VASP
• dpdata — Manipulating data formats of DeePMD-kit, VASP, QE, PWmat, and LAMMPS, etc.
• dpgen — DP-GEN: The deep potential generator
• dsenum — Derivative structure enumerator for multilattice
• dspawpy — Tools for dspaw
• elastool — Elastic tool for zero and finite-temperature elastic constants and mechanical properties calculations
• element-coder — Encode chemical elements numerically and decode numerical representations of elements.
• ElementEmbeddings — Element Embeddings
• emmet — Emmet is a builder framework for the Materials Project
• emmet-core — Core Emmet Library
• env-suite — A suite of tools for including enviroment effects in first principal calculations
• express-py — EXcellent PRoperty Extractor and Serializer.
• ffonons — A Python package for benchmarking phonon predictions from ML force fields
• fplore — FPLO run evaluation
• galore — Broadening and weighting for simulated spectra
• gbml — GBM-Locfit: A GBM framework using Locfit
• gemdat — Generalized Molecular Dynamics Analysis Tool
• gridrdf — Grouped representation of interatomic distances
• gt4sd — Generative Toolkit for Scientific Discovery (GT4SD).
• hgmd — Automatic calculation script for VASP calculation
• hiperccat — tools for automating job creation and management for DFT calculations
• htflow-utils — utility functions and classes for SurfFlow and TriboFlow
• htsct — High-throughput computing tools
• hzdplugins — plugins for my own research
• ifermi — Fermi surface plotting tool from DFT output
• incawrapper — General Repository for Omics Data Handling tools
• ions — A python library for studying percolation in solids
• ipyatom — a package primarily for interfacing ase/pymatgen with ipyvolume/matplotlib
• kgcnn — General Base Layers for Graph Convolutions with Keras
• kinisi — Efficient estimation of diffusion processes from molecular dynamics.
• lightshow — A one-stop-shop for writing computational spectroscopy input files
• lobsterpy — Package for automatic bonding analysis with Lobster/VASP
• m3gnet — Materials Graph with Three-body Interactions
• maml — maml is a machine learning library for materials science.
• mastml — MAterials Simulation Toolkit - Machine Learning
• 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
• matbench-genmetrics — Generative materials benchmarking metrics, inspired by CDVAE.
• matcalc — Calculators for materials properties from the potential energy surface.
• matgl — MatGL is a framework for graph deep learning for materials science.
• matminer — matminer is a library that contains tools for data mining in Materials Science
• mdgo — A codebase for MD simulation setup and results analysis.
• mechviz — MechViz -- Python-based toolkit for the analysis and visualization of mechanical properties of materials
• megnet — MatErials Graph Networks for machine learning of molecules and crystals.
• MEGNetSparse — no summary
• mep — Minimal energy path tools for atomistic systems
• miko-analyzer — Analyzing tool for deep learning based chemical research.