#Materials-science
Showing 39 of 39 repositories tagged #materials-science, ranked by stars
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
A deep learning package for many-body potential energy representation and molecular dynamics
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.
Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
NequIP is a code for building E(3)-equivariant interatomic potentials
Data mining for materials science
Multidimensional data analysis
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
Graph deep learning library for materials
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Curated list of known efforts in materials informatics, i.e. in modern materials science
Allegro is a code for building highly scalable E(3)-equivariant interatomic potentials
DScribe is a python package for creating machine learning descriptors for atomistic systems.
Cross platform, open source application for the processing, visualization, and analysis of 3D tomography data
Interactive browser visualizations for materials science: crystal structures/molecules, trajectories, convex hulls, phase diagrams, Fermi surfaces, bands+DOS, Brillouin zones, etc.
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
atomate2 is a library of computational materials science workflows
Scientific analysis of nanoscale materials imaging data
RadonPy is a Python library to automate physical property calculations for polymer informatics.
Matbench: Benchmarks for materials science property prediction
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.
An interactive structure/property explorer for materials and molecules
A Highly Opinionated List of Open Source Materials Informatics Resources
Home for GSAS-II: crystallographic and diffraction-based structural characterization of materials
Olympus: a benchmarking framework for noisy optimization and experiment planning
LLM-powered agents for scientific research automation
Simplifying the discovery and usage of machine-learning ready datasets in materials science and chemistry
Unsupervised learning of atomic scale dynamics from molecular dynamics.
Tools for the creation of reaction energy diagrams
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.
A tool for finding optimized SQS structures tool written in C++
HyperSpy Jupyter Notebooks demos
A user interface for the hyperspy package. https://hyperspy.org/hyperspyUI
MatID is a python package for identifying and analyzing atomistic systems based on their structure. MatID is designed to help researchers in the automated analysis and labeling of atomistic datasets.
MatAgent: A generative framework for interpretable and targeted inorganic materials design using diffusion-based generation, property prediction, and LLM-driven reasoning.
Benchmarking Large Language Models for Materials Science Tools