SMACT
SMACT: Semiconducting Materials by Analogy and Chemical Theory - Published in JOSS (2019)
matbench-genmetrics
matbench-genmetrics: A Python library for benchmarking crystal structure generative models using time-based splits of Materials Project structures - Published in JOSS (2024)
LobsterPy
LobsterPy: A package to automatically analyze LOBSTER runs - Published in JOSS (2024)
xtal2png
xtal2png: A Python package for representing crystal structure as PNG files - Published in JOSS (2022)
nimCSO
nimCSO: A Nim package for Compositional Space Optimization - Published in JOSS (2024)
ShakeNBreak
ShakeNBreak: Navigating the defect configurational landscape - Published in JOSS (2022)
pysipfenn
Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
yascheduler
Yet another cloud computing scheduler for the high-throughput cloud scientific simulations
optimade
Isomorphic TypeScript / JavaScript client to aggregate all the official Optimade providers
pymatgen
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
https://github.com/tilde-lab/quantum_esperanto
Very fast parser for the XML logs produced with the VASP, Vienna Ab initio Simulation Package
https://github.com/sparks-baird/self-driving-lab-demo
Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.
https://github.com/bin-cao/bgolearn
[Materials & Design 2024 | NPJ com mat 2024] Offical implement of Bgolearn
https://github.com/wmd-group/pdyna
Python package to analyse the structural dynamics of perovskites
phd-dissertation
My "Efficient Materials Informatics between Rockets and Electrons" PhD Dissertation in Materials Science and Engineering, defended on May 20th 2024, concisely spanning 352 pages and 109 figures.
https://github.com/mpes-kit/fuller
Probabilistic machine learning for reconstruction and parametrization of electronic band sturcture from photoemission spectroscopy data
https://github.com/abinit/abipy
Open-source library for analyzing the results produced by ABINIT
https://github.com/exabyte-io/materials-designer
A standalone React.js/Redux based web application for the design and visualization of atomistic materials structures. Used at Mat3ra.com and can be deployed in standalone mode.
https://github.com/sparks-baird/mat_discover
A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
https://github.com/radonpy/radonpy
RadonPy is a Python library to automate physical property calculations for polymer informatics.
https://github.com/suncat-center/catlearn
A machine learning environment for atomic-scale modeling in surface science and catalysis.
https://github.com/sparks-baird/mp-time-split
Use time-splits for Materials Project entries for generative modeling benchmarking.
https://github.com/chiang-yuan/llamp
A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An agentic materials scientist powered by @materialsproject, @langchain-ai, and @openai
https://github.com/exabyte-io/wode.js
Workflow Definitions for Digital Materials/Chemistry R&D
https://github.com/cedergrouphub/s4
Solid-state synthesis science analyzer. Thermo, features, ML, and more.
https://github.com/exabyte-io/api-client
Python client for Exabyte RESTful API
https://github.com/sparks-baird/crabnet
Predict materials properties using only the composition information!
https://github.com/ncfrey/pumml
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
https://github.com/tilde-lab/tilde
Materials informatics framework for ab initio data repositories
https://github.com/mpes-kit/pesfit
Distributed multicomponent lineshape fitting routines and benchmarks for multidimensional spectroscopy and spectral imaging
https://github.com/juliamatsci/cbfv.jl
A simple composition-based feature vectorization utility in Julia
https://github.com/materialsvirtuallab/matpes
A foundational potential energy dataset for materials
ramanspectrumpredictor_qm9
Predict Raman spectra of organic molecules with our ML pipeline using RDKit descriptors and a QM9-style dataset. 🌟🔍 Explore the project on GitHub!
https://github.com/sekocha/pypolymlp
Generator of polynomial machine learning potentials
mpds-aiida
Automated computational workflows based on the MPDS data platform using the CRYSTAL first-principles engine
https://github.com/lamalab-org/structuregraph-helpers
Helpers for working with pymatgen structure graphs.
https://github.com/exabyte-io/esse
JSON schemas and examples representing structural data, characteristic properties, modeling workflows and related data about materials standardizing the diverse landscape of information
https://github.com/leseixas/blendpy
Computational toolkit for investigating thermodynamic models of alloys using first-principles calculations
head
Supporting code for the "Autonomous retrosynthesis of gold nanoparticles via spectral shape matching" paper. DOI : 10.1039/D2DD00025C
https://github.com/exabyte-io/api-examples
Example usage of Exabyte.io platform through its RESTful API: programmatically create materials and modeling workflows, execute simulations on the cloud, analyze data and build machine learning models
awesome-materials-informatics
Curated list of known efforts in materials informatics, i.e. in modern materials science
https://github.com/exabyte-io/made
Materials Design in Javascript (made.js). A JavaScript (Node) library allowing for the creation and manipulation of material structures from atoms up on the web.