Uros Stojiljkovic's starred repositories
auto-QChem
Quantum mechanical descriptor generation
aqme
Automated Quantum Mechanical Environments (AQME) offers transparent and reproducible workflows available for Jupyter Notebooks and command lines, including: 1) RDKit- and CREST-based conformer generation, 2) QM input file creation, 3) post-processing of QM output files, 4) generation of xTB, DFT and RDKit descriptors. https://aqme.readthedocs.io
auto-qchem-notebook-examples
example usage of auto-qchem with jupyter notebooks
handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
auto-qchem
Auto-QChem is an automated workflow for the generation and storage of DFT calculations for organic molecules.
Lucid_Somnambulist
Active learning applied to Pd-catalyzed C-N couplings.
Multiobjective_Optimization
10.26434/chemrxiv-2022-qqxd1
ullmann_project
Optimized geometries and notebooks used in the Ullmann project
Commercial_Search
Multistep notebook workflow for processing and filtering very large lists of molecules and scraping for commercial availability.
ai4chem_course
EPFL CH-457 "AI for chemistry"
pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.