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Janosh Riebesell - CV

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.
In April 2024, I joined Radical AI to vertically integrate MLIPs and robotic labs.
I contribute to open source projects and help maintain matbench-discovery, torch-sim, pymatgen, pymatviz, atomate2. See the full list.

  Publications Sort by

  1. Atomate2: modular workflows for materials science

    A. Ganose, ..., J. Riebesell, ..., A. Jain 10.1039/D5DD00019J  — Digital Discovery — 2025-7
  2. 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
  3. 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
  4. A Foundational Potential Energy Surface Dataset for Materials

    A. Kaplan, ..., J. Riebesell, ..., S. Ong 10.48550/arXiv.2503.04070 — 2025-3
  5. 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
  6. 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
  7. 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
  8. 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
  9. Jobflow: Computational Workflows Made Simple

    A. Rosen, ..., J. Riebesell, ..., A. Ganose 10.21105/joss.05995  — Journal of Open Source Software — 2024-1
  10. A foundation model for atomistic materials chemistry

    I. Batatia, ..., J. Riebesell, ..., G. Csányi arxiv.org/abs/2401.00096v1  (preprint) — 2023-12
  11. 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
  12. 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 Logo pymatgen 1668 ⭐ 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 Logo Matbench Discovery 165 ⭐ 463 commits Python, TypeScript, Svelte

    Benchmark for machine learning energy models simulating a real-world materials discovery campaign.

  • pymatviz Logo pymatviz 235 ⭐ 423 commits Python, Svelte, CSS

    A toolkit for visualizations in materials informatics to complement pymatgen.

  • atomate2 Logo atomate2 223 ⭐ 379 commits Python, Jupyter Notebook, Shell

    atomate2 is a library of computational materials science workflows used by the Materials Project and beyond.

  • MultiSelect Logo MultiSelect 322 ⭐ 288 commits TypeScript, Svelte, CSS

    Keyboard-friendly, accessible and customizable multi-select web component.

  • Scientific Diagrams Logo Scientific Diagrams 325 ⭐ 251 commits Typst, TeX, Svelte

    [Typst](https://typst.app) and [LaTeX](https://www.latex-project.org) diagrams of concepts in physics/chemistry/ML.

  • MatterViz Logo MatterViz 158 ⭐ 241 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 Logo Aviary 57 ⭐ 237 commits Python

    Compositional, structural and coarse-grained structural ML energy model implementations (Roost, Wren, CGCNN, Wrenformer) with a consistent API.

  • CHGNet Logo CHGNet 313 ⭐ 196 commits Python, C, Cython

    Pretrained universal neural network potential for charge-informed atomistic modeling published on the Sep 2023 cover of NMI.

  • jobflow Logo 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 Logo Tensorboard Reducer 74 ⭐ 85 commits Python, TeX

    Reduce multiple PyTorch TensorBoard runs to new events/CSV/JSON. Good for model ensembles.

  • MatCalc Logo MatCalc 95 ⭐ 77 commits Python, Jupyter Notebook

    A Python library for calculating materials properties from ML force field potential energy surfaces.

  • Normalizing Flows Logo 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 Logo MLIP PES softening 21 ⭐ 61 commits Python, CSS, Svelte

    Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning

  • TorchSim Logo TorchSim 244 ⭐ 30 commits Python

    Torch-native, batchable, atomistic simulation.

  • Dielectrics Logo 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 Logo MACE Foundation Models 22 commits message, documentation_url, status

    Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.

  • MatPES Logo MatPES 38 ⭐ 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 Logo Materials Project message, documentation_url, status

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

  Education

  Languages

  •  English
  •  German
  •  French
  •  Spanish

  Nationality

  •  Canadian
  •  German

  Programming Languages and Tools

(emphasis ≈ proficiency)

  Memberships

  Hobbies