One of the largest and most popular open source materials analysis codes that defines classes for structures, molecules, slabs, etc. and interfaces seamlessly with various other materials codes. It also powers the Materials Project.
atomate2 is a library of computational materials science workflows used by the Materials Project and beyond. It supports multiple DFT codes and downstream analysis tools. Recently, we added machine learning potential-powered structure relaxation workflows.
Benchmark for machine learning energy models simulating a real-world materials discovery campaign.
Keyboard-friendly, accessible and customizable multi-select web component.
A toolkit for visualizations in materials informatics to complement pymatgen.
Compositional, structural and coarse-grained structural ML energy model implementations (Roost, Wren, CGCNN, Wrenformer) with a consistent API.
Collection TikZ figures for concepts in physics/chemistry/ML.
A library of Svelte components for building interactive web apps with performant chemistry visualizations like periodic tables, Bohr atoms, nuclei, heatmaps, scatter plots.
Pretrained universal neural network potential for charge-informed atomistic modeling published on the Sep 2023 cover of NMI.
jobflow is a library for writing computational workflows. It provides the plumbing underlying atomate2 and was adopted by several other workflow libraries.
Reduce multiple PyTorch TensorBoard runs to new events/CSV/JSON. Good for model ensembles.
Curated list of resources for learning and using normalizing flows, a powerful tool in ML for modeling probability distributions.
A Python library for calculating materials properties from ML force field potential energy surfaces.
Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
Pushing the Pareto front of band gap and permittivity with ML-guided dielectrics discovery incl. experimental synthesis.
This is a compilation of notes and solutions to problem sheets for some of the physics lectures I took, most of them in Heidelberg. Hopefully, they can be useful to others. If you find errors, please open an issue.