Pinned Repositories
cgcnn
Crystal graph convolutional neural networks for predicting material properties.
Cleavage_Energy_Manuscript
cs229-2018-autumn
All notes and materials for the CS229: Machine Learning course by Stanford University
csubeamer
dft-book
A book on modeling materials using VASP, ase and vasp
fromthetransistor
From the Transistor to the Web Browser, a rough outline for a 12 week course
hn-yu
hn-yu.github.io
Markdown Page
matsciml
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
hn-yu's Repositories
hn-yu/cgcnn
Crystal graph convolutional neural networks for predicting material properties.
hn-yu/Cleavage_Energy_Manuscript
hn-yu/cs229-2018-autumn
All notes and materials for the CS229: Machine Learning course by Stanford University
hn-yu/csubeamer
hn-yu/dft-book
A book on modeling materials using VASP, ase and vasp
hn-yu/fromthetransistor
From the Transistor to the Web Browser, a rough outline for a 12 week course
hn-yu/hn-yu
hn-yu/hn-yu.github.io
Markdown Page
hn-yu/matsciml
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
hn-yu/mp-workshop
The Materials Project Workshop Curriculum
hn-yu/RBF_neural_network_python
an implementation of a Radial Basis Function Neural Network (RBFNN) for classification problem.
hn-yu/SAWARATSUKI.Logos
ロゴを可愛く作ろう できる限りForkはしないでください
hn-yu/unordered_list
hn-yu/wherewulff
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions