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.
TorchSim
Torch-native, batchable, atomistic simulations.
Materials ProjectWidely used database, website, API and OSS ecosystem built for computing properties of 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
- Matbench Discovery
pymatviz
MultiSelect
MatterViz
Diagrams- CHGNet
jobflow
Tensorboard ReducerMatCalc
Normalizing Flows- Dielectrics
- MatPES
- Materials Project
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.
