tildeMaterials informatics framework for ab initio data repositories
Stars: ✭ 19 (-40.62%)
Mutual labels: vasp, materials-science, ab-initio
atomate2atomate2 is a library of computational materials science workflows
Stars: ✭ 67 (+109.38%)
Mutual labels: vasp, materials-science
data-resources-for-materials-scienceA list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.
Stars: ✭ 81 (+153.13%)
Mutual labels: materials-science
pseudo dojoPython framework for generating and validating pseudo potentials
Stars: ✭ 32 (+0%)
Mutual labels: ab-initio
CatKitGeneral purpose tools for high-throughput catalysis
Stars: ✭ 48 (+50%)
Mutual labels: materials-science
tdmmsTwo-dimensional materials manufacturing system
Stars: ✭ 17 (-46.87%)
Mutual labels: materials-science
RosettaDesignRosettaDesign using PyRosetta
Stars: ✭ 19 (-40.62%)
Mutual labels: ab-initio
CrabNetPredict materials properties using only the composition information!
Stars: ✭ 57 (+78.13%)
Mutual labels: materials-science
lamipyComposite laminates engineering simulations in Python.
Stars: ✭ 32 (+0%)
Mutual labels: materials-science
matador⚗️ matador is an aggregator, manipulator and runner of first-principles calculations, written with a bent towards battery 🔋 electrode materials.
Stars: ✭ 23 (-28.12%)
Mutual labels: materials-science
uf3UF3: a python library for generating ultra-fast interatomic potentials
Stars: ✭ 19 (-40.62%)
Mutual labels: materials-science
thermo pwThermo_pw is a driver of quantum-ESPRESSO routines for the automatic computation of ab-initio material properties.
Stars: ✭ 34 (+6.25%)
Mutual labels: materials-science
pyGAPSA framework for processing adsorption data and isotherm fitting
Stars: ✭ 36 (+12.5%)
Mutual labels: materials-science
VASPBERRYBerry curvature and Chern number calculations with the output (WAVECAR) of VASP code
Stars: ✭ 26 (-18.75%)
Mutual labels: vasp
pytopomatPython Topological Materials (pytopomat) is a code for easy, high-throughput analysis of topological materials.
Stars: ✭ 19 (-40.62%)
Mutual labels: materials-science
phoebeA high-performance framework for solving phonon and electron Boltzmann equations
Stars: ✭ 33 (+3.13%)
Mutual labels: materials-science
vasprunquick analysis of vasp calculation
Stars: ✭ 20 (-37.5%)
Mutual labels: vasp
mamlPython for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
Stars: ✭ 174 (+443.75%)
Mutual labels: materials-science
snapRepository for spectral neighbor analysis potential (SNAP) model development.
Stars: ✭ 27 (-15.62%)
Mutual labels: materials-science