Abhivega's Stars
chroma-core/chroma
the AI-native open-source embedding database
Dao-AILab/flash-attention
Fast and memory-efficient exact attention
rougier/scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
srush/GPU-Puzzles
Solve puzzles. Learn CUDA.
nlpxucan/WizardLM
LLMs build upon Evol Insturct: WizardLM, WizardCoder, WizardMath
garrettj403/SciencePlots
Matplotlib styles for scientific plotting
PyO3/maturin
Build and publish crates with pyo3, cffi and uniffi bindings as well as rust binaries as python packages
ryanhaining/cppitertools
Implementation of python itertools and builtin iteration functions for C++17
google-deepmind/materials_discovery
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
JuDFTteam/best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
phonopy/phonopy
Phonon code
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
gregneagle/relocatable-python
A tool for building standalone relocatable Python.framework bundles
dojeda/poetry2conda
Convert pyproject.toml to environment.yaml
theochem/tinydft
A minimalistic atomic Density Functional Theory (DFT) code
janosh/matbench-discovery
An evaluation framework for machine learning models simulating high-throughput materials discovery.
SMTG-Bham/ShakeNBreak
Defect structure-searching employing chemically-guided bond distortions
uf3/uf3
UF3: a python library for generating ultra-fast interatomic potentials
sp8rks/MSE2001python
Files for the MSE2001 course Introduction to Python for Materials Engineers
mattmcdermott/novel-materials-screening
Screening the Materials Project for novel materials to be synthesized by the autonomous laboratory (A-Lab).
pylada/pylada-light
A physics computational framework for python and ipython
PhasesResearchLab/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.
sirmarcel/glp
tools for graph-based machine-learning potentials in jax
antoniuk1/SynthNN
niklasschmitz/ad-kernels
Code for the paper "Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields"
ENCCS/gpubootcamp
This repository consists for gpu bootcamp material for HPC and AI
MarDiehl/latex-skeleton
Collected latex code to write a paper
sirmarcel/comms
python communications
httk/rsttools
ReStructuredText (ReST) HTML slide generator for reveal.js