Name: Mansurbek Abdullaev
Type: User
Company: University of Science and Technology, South Korea
Bio: Ph.D. scholar at UST(S. Korea). Passionate about the intersection between artificial intelligence and materials discovery based on first principles
Twitter: Mansurbek_KRICT
Location: South Korea, Daejeon
Blog: https://uzbek.gitbook.io/ai/
Mansurbek Abdullaev's Projects
This repo illustrates the scraping the special issues from ACS_Catalysis Journals and filter them out according to the user's interests. The filtered data will be sent to the user's email.
AI(ML,DL) projects for tutorial
Here, aromaticity of molecules have been classified by using Graphical Convolutional Network(GCN)
Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning
Surface segregation using Deep Reinforcement Learning
A machine learning environment for atomic-scale modeling in surface science and catalysis.
Run VSCode (codeserver) on Google Colab or Kaggle Notebooks
Mohirdev.uz
Deep Learning Specialization by Andrew Ng on Coursera.
JARVIS-Tools: an open-source software package for data-driven atomistic materials design
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Machine Learning for Catalysis
https://opencatalystproject.org/
Workflow for creating and analyzing the Open Catalyst Dataset
Datasets for Data Science and AI Practicum
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.
Python interface to the SISSO (Sure Independence Screening and Sparsifying Operator) method.
PyTorch Tutorial for Deep Learning Researchers
Modules for cross validation, evaluation and plot of SISSO
Transport Classification Model
New ASE compliant Python interface to VASP