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.

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

A scikit-learn compatible neural network library that wraps PyTorch

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Deep learning for molecules and materials book

Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

Code for "Machine Learning for Physicists" lecture series by Florian Marquardt

TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

This repository provides various demos/examples of using Snowpark for Python.

Quantify the difference between two arbitrary curves in space

Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.

Notes and tutorials on Density Functional Theory calculation using Quantum Espresso.

A collection of interactive notebooks to explain concepts of quantum mechanics and related topics

Datasets for time series forecasting

Example scripts using the CSD Python API

Julia package towards classical molecular modeling of nanoporous materials

Website

Ideal Adsorbed Solution Theory

N-Dimensional MD engine with symmetry group constraints written in C

Some tutorial-style examples for validating machine-learned interatomic potentials

This is a supporting repository for the journal article https://towardsdatascience.com/introduction-to-matrix-profiles-5568f3375d90.

predicting charges on MOF atoms via a message passing MOFs

LiFePo4(LFP) Battery State of Charge (SOC) estimation from BMS raw data

Project aimed to create a collection of cool and useful functions related to colors and graphics in python

An example on how to customize Atomify examples

Example for calculating U value in DFT+U using CASTEP with the ab inito linear response method