Pinned Repositories
fairchem
FAIR Chemistry's library of machine learning methods for chemistry
cgcnn
Crystal graph convolutional neural networks for predicting material properties.
imatgen
image-based generative model for inverse design of solid state materials
MOF-CGCNN
We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two major modifications were made to the original CGCNN algorithm.The new pooling method mainly depends on the SBUs to describe the local chemical environment around the metal sites. Considering certain adsorbates dominated by cage window sites, we incorporated certain intrinsic structural features, e.g., PLD, LPD, φ, and AV, to the CGCNN algorithm.
mofid
Metal node to deconstruct
Open-Babel
Scriberrrr's Repositories
Scriberrrr/cgcnn
Crystal graph convolutional neural networks for predicting material properties.
Scriberrrr/imatgen
image-based generative model for inverse design of solid state materials
Scriberrrr/MOF-CGCNN
We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two major modifications were made to the original CGCNN algorithm.The new pooling method mainly depends on the SBUs to describe the local chemical environment around the metal sites. Considering certain adsorbates dominated by cage window sites, we incorporated certain intrinsic structural features, e.g., PLD, LPD, φ, and AV, to the CGCNN algorithm.
Scriberrrr/mofid
Metal node to deconstruct
Scriberrrr/Open-Babel