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Ruihan Wang's Projects

bandgapboraterpred icon bandgapboraterpred

Data Mining and Graph Network Deep Learning for Band Gap Prediction in Crystalline Borate Materials

cgcnn icon cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chembench icon chembench

MoleculeNet benchmark dataset & MolMapNet dataset

chemprop icon chemprop

Message Passing Neural Networks for Molecule Property Prediction

cross-linker_screening icon cross-linker_screening

Python source code for high-throughput screening of a MOF database (containing .cif files) to find matching MOFs for a given cross-linker.

deepore icon deepore

Deep learning for rapid characterization of porous materials

dscribe icon dscribe

DScribe is a python package for creating machine learning descriptors for atomistic systems.

eqeq icon eqeq

DEPRECATED. Charge equilibration method for crystal structures

gdynet icon gdynet

Unsupervised learning of atomic scale dynamics from molecular dynamics.

megnet icon megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

mof-cgcnn icon 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.

mpn_charges icon mpn_charges

predicting charges on MOF atoms via a message passing MOFs

pgl icon pgl

Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

raspa2 icon raspa2

NO LONGER UPDATED. Use the official repository.

schnet icon schnet

SchNet - a deep learning architecture for quantum chemistry

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