Janosh
Computational materials scientist. I'm interested in machine- learning-powered atomistic simulation and building software around that.
Recent Work
Matbench DiscoveryBenchmark for machine learning energy models simulating a real-world materials discovery campaign.
Materials ProjectThe Materials Project is a database of computed properties for inorganic materials.
atomate2
atomate2 is a library of computational materials science workflows used by the Materials Project and beyond.
MatPESA foundational DFT potential energy dataset for materials covering 89 elements and emphasizing data diversity and quality (at PBE and r2SCAN level).
MLIP PES softening
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
DielectricsPushing the Pareto front of band gap and permittivity with ML-guided dielectrics discovery incl. experimental synthesis.
MACE Foundation Models
Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
CHGNetPretrained universal neural network potential for charge-informed atomistic modeling published on the Sep 2023 cover of NMI.
Open Source
pymatgen
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.
- Matbench Discovery
Benchmark for machine learning energy models simulating a real-world materials discovery campaign.
pymatvizA toolkit for visualizations in materials informatics to complement pymatgen.
atomate2 is a library of computational materials science workflows used by the Materials Project and beyond.
MultiSelectKeyboard-friendly, accessible and customizable multi-select web component.
Scientific Diagrams[Typst](https://typst.app) and [LaTeX](https://www.latex-project.org) diagrams of concepts in physics/chemistry/ML.
MatterVizA library of Svelte components for building interactive web apps with performant chemistry visualizations like periodic tables, Bohr atoms, nuclei, heatmaps, scatter plots.
AviaryCompositional, structural and coarse-grained structural ML energy model implementations (Roost, Wren, CGCNN, Wrenformer) with a consistent API.
- CHGNet
Pretrained universal neural network potential for charge-informed atomistic modeling published on the Sep 2023 cover of NMI.
jobflowjobflow is a library for writing computational workflows. It provides the plumbing underlying atomate2 and was adopted by several other workflow libraries.
Tensorboard ReducerReduce multiple PyTorch TensorBoard runs to new events/CSV/JSON. Good for model ensembles.
MatCalc
A Python library for calculating materials properties from ML force field potential energy surfaces.
Normalizing FlowsCurated list of resources for learning and using normalizing flows, a powerful tool in ML for modeling probability distributions.
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
TorchSim
Torch-native, batchable, atomistic simulation.
- Dielectrics
Pushing the Pareto front of band gap and permittivity with ML-guided dielectrics discovery incl. experimental synthesis.
Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
- MatPES
A foundational DFT potential energy dataset for materials covering 89 elements and emphasizing data diversity and quality (at PBE and r2SCAN level).
- Materials Project
The Materials Project is a database of computed properties for inorganic materials.
Notes on Physics
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.
