Pinned Repositories
4D-STEM
Manifold, Clustering, Interactive visualization
chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
coding-interview-university
A complete computer science study plan to become a software engineer.
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
complexPyTorch
A high-level toolbox for using complex valued neural networks in PyTorch
crystal4D
datasharing
The Leek group guide to data sharing
deep_complex_networks
Implementation related to the Deep Complex Networks
dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
high_dim_descriptor
A feature engineering approach for modeling ternary perovskite target properties by Fourier transforming direct features into the periodic reciprocal crystal lattice
anveshnathaniou's Repositories
anveshnathaniou/4D-STEM
Manifold, Clustering, Interactive visualization
anveshnathaniou/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
anveshnathaniou/coding-interview-university
A complete computer science study plan to become a software engineer.
anveshnathaniou/Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
anveshnathaniou/complexPyTorch
A high-level toolbox for using complex valued neural networks in PyTorch
anveshnathaniou/crystal4D
anveshnathaniou/datasharing
The Leek group guide to data sharing
anveshnathaniou/deep_complex_networks
Implementation related to the Deep Complex Networks
anveshnathaniou/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
anveshnathaniou/high_dim_descriptor
A feature engineering approach for modeling ternary perovskite target properties by Fourier transforming direct features into the periodic reciprocal crystal lattice
anveshnathaniou/LAMMPSatOU
anveshnathaniou/IDGL
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
anveshnathaniou/llama
Inference code for LLaMA models
anveshnathaniou/machine-learning-book
Code Repository for Machine Learning with PyTorch and Scikit-Learn
anveshnathaniou/mapidoc
Public repo for Materials API documentation
anveshnathaniou/matgl
Graph deep learning library for materials
anveshnathaniou/matminer_examples
A repo of examples for the matminer (https://github.com/hackingmaterials/matminer) code
anveshnathaniou/ML-ElectronMicroscopy-2023
anveshnathaniou/PGCGM
Source code for generating materials with 20 space groups using PGCGM
anveshnathaniou/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
anveshnathaniou/py4DSTEM
anveshnathaniou/py4DSTEM_tutorials
Sample code for py4DSTEM
anveshnathaniou/pymatgen
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
anveshnathaniou/SLAPS-GNN
PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"
anveshnathaniou/SUBLIME
[WWW'22] Towards Unsupervised Deep Graph Structure Learning
anveshnathaniou/workshop
The Materials Project Workshop Curriculum