Janosh Riebesell - CV

From April 2024 to June 2025, I worked at Radical AI on vertically integrating MLIPs and robotic labs.
I worked for the Materials Project from Jul 2022 to Dec 2023 where I developed ML foundation models (CHGNet, MACE-MP) and high-throughput workflows for generating more diverse, higher-quality DFT datasets for future models.
I contribute to open source projects and help maintain matbench-discovery, torch-sim, pymatgen, pymatviz, atomate2. See the full list.

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Atomate2: modular workflows for materials science
A. Ganose, ..., J. Riebesell, ..., A. Jain — 10.1039/D5DD00019J — Digital Discovery — 2025-7
Accelerated data-driven materials science with the Materials Project
M. Horton, ..., J. Riebesell, ..., K. Persson — 10.1038/s41563-025-02272-0 — Nature Materials — 2025-7
Matbench Discovery - A framework to evaluate machine learning crystal stability predictions
J. Riebesell, R. Goodall, ..., K. Persson — 10.1038/s42256-025-01055-1 — Nature Machine Intelligence — 2025-6
A Foundational Potential Energy Surface Dataset for Materials
A. Kaplan, ..., J. Riebesell, ..., S. Ong — 10.48550/arXiv.2503.04070 — 2025-3
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
M. Evans, ..., J. Riebesell, ..., R. Armiento — 10.1039/D4DD00039K — Digital Discovery — 2024-8
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
B. Deng, ..., J. Riebesell, ..., G. Ceder — arxiv.org/abs/2405.07105 (preprint) — 2024-5
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
Y. Chiang, C. Chou, J. Riebesell — arxiv.org/abs/2401.17244 (preprint) — 2024-1
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
J. Riebesell, T. Surta, ..., A. Lee — arxiv.org/abs/2401.05848v1 (preprint) — 2024-1
Jobflow: Computational Workflows Made Simple
A. Rosen, ..., J. Riebesell, ..., A. Ganose — 10.21105/joss.05995 — Journal of Open Source Software — 2024-1
A foundation model for atomistic materials chemistry
I. Batatia, ..., J. Riebesell, ..., G. Csányi — arxiv.org/abs/2401.00096v1 (preprint) — 2023-12
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
B. Deng, ..., J. Riebesell, ..., G. Ceder — 10.1038/s42256-023-00716-3 — Nature Machine Intelligence — 2023-9
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
M. Horton, ..., J. Riebesell, ..., K. Persson — 10.48550/arXiv.2302.06147 — 2023-2

Open Source Sort by

pymatgen 1678 ⭐ 1048 commits Python, Cython

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 169 ⭐ 464 commits Python, TypeScript, Svelte

Benchmark for machine learning energy models simulating a real-world materials discovery campaign.
pymatviz 241 ⭐ 424 commits Python, Svelte, CSS

A toolkit for visualizations in materials informatics to complement pymatgen.
atomate2 228 ⭐ 379 commits Python, Jupyter Notebook, Shell

atomate2 is a library of computational materials science workflows used by the Materials Project and beyond.
MultiSelect 322 ⭐ 294 commits TypeScript, Svelte, CSS

Keyboard-friendly, accessible and customizable multi-select web component.
Scientific Diagrams 330 ⭐ 251 commits Typst, TeX, Svelte

Typst and LaTeX diagrams of concepts in physics/chemistry/ML.
MatterViz 161 ⭐ 249 commits TypeScript, Svelte, CSS

A library of Svelte components for building interactive web apps with performant chemistry visualizations like periodic tables, Bohr atoms, nuclei, heatmaps, scatter plots.
Aviary 58 ⭐ 237 commits Python

Compositional, structural and coarse-grained structural ML energy model implementations (Roost, Wren, CGCNN, Wrenformer) with a consistent API.
CHGNet 316 ⭐ 196 commits Python, C, Cython

Pretrained universal neural network potential for charge-informed atomistic modeling published on the Sep 2023 cover of NMI.
jobflow 104 ⭐ 136 commits Python, TeX

jobflow is a library for writing computational workflows. It provides the plumbing underlying atomate2 and was adopted by several other workflow libraries.
Tensorboard Reducer 74 ⭐ 86 commits Python, TeX

Reduce multiple PyTorch TensorBoard runs to new events/CSV/JSON. Good for model ensembles.
MatCalc 95 ⭐ 77 commits Python, Jupyter Notebook

A Python library for calculating materials properties from ML force field potential energy surfaces.
Normalizing Flows 1541 ⭐ 74 commits Python

Curated list of resources for learning and using normalizing flows, a powerful tool in ML for modeling probability distributions.
MLIP PES softening 21 ⭐ 61 commits Python, CSS, Svelte

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
TorchSim 247 ⭐ 30 commits Python

Torch-native, batchable, atomistic simulation.
Dielectrics 10 ⭐ 27 commits HTML, Python, TeX

Pushing the Pareto front of band gap and permittivity with ML-guided dielectrics discovery incl. experimental synthesis.
MACE Foundation Models 22 commits message, documentation_url, status

Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
MatPES 39 ⭐ Jupyter Notebook, Python, CSS

A foundational DFT potential energy dataset for materials covering 89 elements and emphasizing data diversity and quality (at PBE and r2SCAN level).
Materials Project message, documentation_url, status

The Materials Project is a database of computed properties for inorganic materials.