ML
Things that you should (and should not) do in your Materials Informatics research.
An Open Source Machine Learning Framework for Everyone
Graph deep learning library for materials
Supplementary material accompanying Frey, N. C.; Akinwande, D.; Jariwala, D.; Shenoy, V. B. Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information…
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
SchNetPack - Deep Neural Networks for Atomistic Systems
The materials for the Fall ML in Materials course at the UTK MSE
A repository of code and data for the USCB sklearn workshop on February 7th & 8th, 2019
An open-source, low-code machine learning library in Python
Software for generating machine-learning interatomic potentials for LAMMPS
A toolkit for visualizations in materials informatics.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Turns Data and AI algorithms into production-ready web applications in no time.
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
Understanding Kolmogorov-Arnold Networks: A Tutorial Series on KAN using Toy Examples
A public repository collecting links to state of the art QA and evaluation sets for various ML and LLM applications
Higher-order equivariant neural networks for charge density prediction in materials
FAIR Chemistry's library of machine learning methods for chemistry
Crystal graph convolutional neural networks for predicting material properties.
A text-guided crystal structure generative AI
Convert any PDF into a podcast episode!
Convert any PDF into a podcast episode!
A one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.一站式开源高质量数据提取工具，支持PDF/网页/多格式电子书提取。