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Department of Energy Engineering, KENTECH
- Naju, Korea
- https://sites.google.com/kentech.ac.kr/kimgroup/
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Kentech Tutorial of Advancing Sustainability through Computational Chemistry Methods
Practical Cheminformatics Tutorials
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Tool to read a logfile produced by LAMMPS into a simple python data structure with a get()-function providing the log data.
A curated list of awesome Read the Docs projects
Provides some useful information and the LAMMPS input files to model a polymer-metal interface.
This is the 1st part of project PPP-Prediction Properties of Polymers, and this work predicts Tg of polyimides using GNN, while more than 8,000,000 Tg data being predicted.
aaronrfinney / PyEMMAarf
Forked from markovmodel/PyEMMAπ Python API for Emma's Markov Model Algorithms π
updated constant potential plugin for LAMMPS
PSP is a python toolkit for predicting atomic-level structural models for a range of polymer geometries.
Neural Network predicts ion concentration profile under nanoconfinement
An interactive structure/property explorer for materials and molecules
RadonPy is a Python library to automate physical property calculations for polymer informatics.
π₯ Use pre-trained models in PyTorch to extract vector embeddings for any image
python simulation interface for molecular modeling
Python Bindings for the LAMMPS open source MD/DEM simulator
2D and 3D Voronoi tessellations: a python entry point for the voro++ library
A set of tools for characterizing and analying 3D images of porous materials
JelfsMaterialsGroup / pywindow
Forked from marcinmiklitz/pywindowpyWINDOW is a Python package for structural analysis of discrete molecules with voids and windows, individually, in molecular systems and molecular dynamics trajectories of these.
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
Contains Jupyter notebooks and other materials prepared for the course Numerical Methods offered at TIFR Hyderabad (https://moldis-group.github.io/teaching.html)