There are 30 repositories under materials-informatics topic.
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.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Curated list of known efforts in materials informatics, i.e. in modern materials science
Graph deep learning library for materials
A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
About JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ https://www.youtube.com/@dr_k_choudhary
RadonPy is a Python library to automate physical property calculations for polymer informatics.
Things that you should (and should not) do in your Materials Informatics research.
Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
Fork this repo for a quick start. If "Project Timeline" or "License" appeared on your nav bar, Look Below!
MACE foundation models (MP, OMAT, Matpes)
SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS
A machine learning environment for atomic-scale modeling in surface science and catalysis.
Predict materials properties using only the composition information!
Defect structure-searching employing chemically-guided bond distortions
Catalyst Micro-kinetic Analysis Package for automated creation of micro-kinetic models used in catalyst screening
Package to perform automatic bonding analysis with the program Lobster in the field of computational materials science and quantum chemistry
[Materials & Design 2024 | NPJ com mat 2024] Offical implement of Bgolearn
Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
General purpose tools for high-throughput catalysis
Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIPs). It offers a growing set of evaluation methods alongside powerful visualization and comparison tools.
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
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
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.
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
Graph-Aware Attention for Adaptive Dynamics in Transformers
A package for Covalent Organic Frameworks structure assembly based on specific building block, topology and functional groups based on the reticular approach
polyGNN is a Python library to automate ML model training for polymer informatics.
A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.