Janosh Riebesell - CV

On Sep 8 2025, I joined Periodic Labs as a member of technical staff.
From April 2024 to June 2025, I worked at Radical AI on MLIPs and robotic labs.
I worked for the Materials Project from Jul 2022 to Dec 2023 where I developed materials science foundation models (CHGNet, MACE-MP) and high-throughput workflows for generating more diverse, higher-quality reference datasets for future models.
I contribute to open source projects and maintain matbench-discovery, torch-sim, pymatgen, pymatviz, matterviz, atomate2. See the full list.

Publications Sort by

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
Atomate2: modular workflows for materials science
A. Ganose, ..., J. Riebesell, ..., A. Jain — 10.1039/D5DD00019J — Digital Discovery — 2025-7 — 5 citations
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 — 5 citations
A Foundational Potential Energy Surface Dataset for Materials
A. Kaplan, ..., J. Riebesell, ..., S. Ong — 10.48550/arXiv.2503.04070 — 2025-3 — 6 citations
Systematic softening in universal machine learning interatomic potentials
B. Deng, ..., J. Riebesell, ..., G. Ceder — 10.1038/s41524-024-01500-6 — npj Computational Materials — 2025-1
TorchSim: An efficient atomistic simulation engine in PyTorch
O. Cohen, J. Riebesell, ..., A. Gangan — 10.1088/3050-287X/ae1799 — IOP AI for Science — 2025 — 2 citations
Discovery of high-performance dielectric materials with machine-learning-guided search
J. Riebesell, T. Surta, ..., A. Lee — 10.1016/j.xcrp.2024.102241 — Cell Reports Physical Science — 2024-10
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 — 15 citations
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 — 12 citations
Jobflow: Computational Workflows Made Simple
A. Rosen, ..., J. Riebesell, ..., A. Ganose — 10.21105/joss.05995 — Journal of Open Source Software — 2024-1 — 18 citations
A foundation model for atomistic materials chemistry
I. Batatia, ..., J. Riebesell, ..., G. Csányi — arxiv.org/abs/2401.00096v1 (preprint) — 2023-12 — 171 citations
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 — 397 citations
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

pymatgen

1961 1050 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.
MatterViz

359 551 commits TypeScript, Svelte, Python

A library of Svelte components for building interactive web apps with performant chemistry visualizations like periodic tables, Bohr atoms, nuclei, heatmaps, scatter plots.
Matbench Discovery

251 531 commits Python, TypeScript, Svelte

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

333 483 commits Python, TypeScript, Svelte

A toolkit for visualizations in materials informatics to complement pymatgen.
Svelte Widgets

378 424 commits TypeScript, Svelte, CSS

Keyboard-friendly, accessible and customizable Svelte components incl. MultiSelect, Toc, PageSearch and more.
atomate2

347 386 commits Python, Jupyter Notebook, Shell

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

684 284 commits Typst, TeX, Svelte

Typst and LaTeX diagrams of concepts in physics/chemistry/ML.
CHGNet

404 196 commits Python, C, Cython

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

127 153 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

78 104 commits Python, TeX

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

1637 80 commits Python

Curated list of resources for learning and using normalizing flows, a powerful tool in ML for modeling probability distributions.
MatCalc

151 77 commits Python, Jupyter Notebook

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

24 61 commits Python, CSS, Svelte

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

488 39 commits Python

Torch-native, batchable, atomistic simulations.
Dielectrics

14 31 commits HTML, Python, TeX

Pushing the Pareto front of band gap and permittivity with ML-guided dielectrics discovery incl. experimental synthesis.
MACE Foundation Models

302 1 commits Python

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

59 Jupyter Notebook, Python

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

Widely used database, website, API and OSS ecosystem built for computing properties of inorganic materials.