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21summercodingcamp

2021 Summer // Kim & Paton Group Coding Camp Materials

aqme

Automated Quantum Mechanical Environments (AQME): The code is an ensemble of automated QM workflows, including: 1) RDKit- and CREST-based conformer generator and ready-to-submit QM input files starting from individual files or databases, 2) post-processing of QM output files to fix extra imaginary frequencies, unfinished jobs and error terminations

autode

automated reaction profile generation

cclib

Parsers and algorithms for computational chemistry logfiles

cnpred

Cetane number prediction model

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computational_maths

A short collection of Jupyter notebooks explaining some basic computational math

dbtoml

DBcg: Conformer generator (.com files for Gaussian) starting from smiles or sdf databases. Also, the code reoptimizes output files with imaginary frequencies and failed optimization jobs.

hierarchical-qsar-modeling

introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"

machine_learning_refined

Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

master-machine-learning-algorithms

This is a repository for notes and codes of reading the book --- Jason Brownlee Master Machine Learning Algorithms

mml-companion

This is a companion to the ‘Mathematical Foundations’ section of the book, Mathematics for Machine Learning by Marc Deisenroth, Aldo Faisal and Cheng Ong, written in python for Jupyter Notebook.

morfeus

A Python package for calculating molecular features

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

python-data-analysis-third-edition

Python Data Analysis, Third Edition, Published by Packt

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

rxn-insight

Analyzing chemical databases and predicting reaction conditions with cheminformatics

scicompforchemists

Scientific Computing for Chemists text for teaching basic computing skills to chemistry students using Python, Jupyter notebooks, and the SciPy stack. This text makes use of a variety of packages including NumPy, SciPy, matplotlib, pandas, seaborn, NMRglue, SymPy, scikit-image, and scikit-learn.

sklearn_tutorial

Materials for my scikit-learn tutorial

synthetic-key-steps

Code repository for synthetic key step generation and analysis

vmls-companions

These are companion notebooks written in Julia and Python for: "Introduction to Applied Linear Algebra" by Boyd and Vandenberghe.