usccolumbia

DeepAttentionPan

GATGNN

Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials property prediction

Awesome-Machine-learning-for-discovery-of-physical-laws

A curated list of awesome resources on using machine learning and data science for discovery of physical laws

BERTOS

BERTOS: transformer for oxidation state prediction

deeperGATGNN

Scalable graph neural networks for materials property prediction

DeepSeqPan

A sequence-based pan model for peptide-MHC I binding affinity prediction.

GMTransformer

materialsUQ

Uncertainty Quantification for Materials Property Prediction: a Benchmark Study

MLatticeABC

Machine learning model for crystal lattice constant prediction

mtransformer

Materials Transformers

usccolumbia/MLatticeABC

Machine learning model for crystal lattice constant prediction

usccolumbia/TLOpt

Inverse design of composite metal oxide optical materials

usccolumbia/MOOCSP

usccolumbia/GraphGenerationPapers

usccolumbia/materialsdatasets

usccolumbia/matsynthesis

Materials Synthesizability Prediction

usccolumbia/SG_predict

usccolumbia/zorb-numpy

ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks

usccolumbia/AMDNet

Code base for AMDNet described in https://doi.org/10.1126/sciadv.abf1754

usccolumbia/ASPH-Code

data and code to reduplicate paper: Topological representations of crystalline compounds for the machine-learning prediction of materials properties

usccolumbia/Atomistic-Adversarial-Attacks

Code for performing adversarial attacks on atomistic systems using NN potentials

usccolumbia/avatarify

Avatars for Zoom, Skype and other video-conferencing apps.

usccolumbia/batterydatabase

usccolumbia/batterygui

usccolumbia/cdvae

An SE(3)-invariant autoencoder for generating the periodic structure of materials

usccolumbia/ChemDASH

Chemically Directed Atom Swap Hopping -- Crystal structure prediction by swapping atoms in unfavourable chemical environments

usccolumbia/Constrained-Bayesian-Optimisation-for-Automatic-Chemical-Design

Code to accompany the paper "Constrained Bayesian Optimisation for Automatic Chemical Design" https://pubs.rsc.org/en/content/articlehtml/2019/sc/c9sc04026a

usccolumbia/deepKNet

PointNet-based 3D deep learning model designed for decoding the structure-property relationship for generic crystalline materials.

usccolumbia/deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

usccolumbia/GNN_atomistics

Graph Neural Networks for predicting properties in bcc ferromagnetic iron

usccolumbia/icsg3d

Official implementation of "3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning" (JCIM) 2020

usccolumbia/materialsapps

Materials apps

usccolumbia/ml-dft

A package for density functional approximation using machine learning.

usccolumbia/MLSR-LTC

Source code accompanying the "Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning" paper.

usccolumbia/mse_datasets

A repository with data for various materials properties. Data and/or references are provided. Scripts for processing the data are provided

usccolumbia/mse_ML_datasets

usccolumbia/p2ptrans

An algorithm to match crystal structures atom-to-atom

usccolumbia/phonondos_e3nn

Code Repository for "Direct prediction of phonon density of states with Euclidean neural network"

usccolumbia/PI1M

A benchmark dataset for polymer informatics.

usccolumbia/transformer-xl