Name: Malik Waqar Arshad
Type: User
Company: University of Houston
Bio: PhD Chemical Engineering from UST-KRICT South Korea. Research interest fields are
Heterogeneous catalysis, DFT Calculations, and data science.
Twitter: waqararshadciit
Location: Houston, TX, USA
Malik Waqar Arshad's Projects
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
ase interface for Quantum Espresso
atomate is a powerful software for computational materials science and contains pre-built workflows.
atomate2 is a library of computational materials science workflows
A Python implementation of global optimization with gaussian processes.
Python API for the Surface Reactions database on Catalysis-Hub.org, used for querying and uploading data.
A machine learning environment for atomic-scale modeling in surface science and catalysis.
Catalyst Micro-kinetic Analysis Package for automated creation of micro-kinetic models used in catalyst screening
A simple, robust and flexible just-in-time job management framework in Python.
Public portion of COE 3803 "Data Analytics for Engineers" course at Georgia Tech.
A personal project to understand and implement basic data science algorithms
Deep Learning Specialization by Andrew Ng on Coursera.
A collection of various deep learning architectures, models, and tips
TensorFlow Basic Tutorial Labs
A book on modeling materials using VASP, ase and vasp
Fast batch fully connected MLP algorithm for classification using numpy memmap
The Fireworks Workflow Management Repo.
A list of cool features of Git and GitHub.
Gaussian processes framework in python
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Build interactive, publication-quality documents from Jupyter Notebooks
A general linear model for microkinetic catalytic systems.
The course materials for "Machine Learning in Chemistry 101"
High-throughput DFT of MOFs using ASE/VASP
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 Data Science Handbook: full text in Jupyter Notebooks