Janosh Riebesell

  Selected Publications Sort by date title author first ↓

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

   B. Deng, ..., J. Riebesell, ..., G. Ceder — http://arxiv.org/abs/2405.07105 — 2024-5

2. Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

   M. Evans, ..., J. Riebesell, ..., R. Armiento — 10.48550/arXiv.2402.00572 — 2024-2

3. 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 — 2024-1

4. 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 — 2024-1

5. Jobflow: Computational Workflows Made Simple

   A. Rosen, ..., J. Riebesell, ..., A. Ganose — 10.21105/joss.05995 — 2024-1

6. A foundation model for atomistic materials chemistry

   J. Riebesell, I. Batatia, ..., G. Csányi — arxiv.org/abs/2401.00096v1 — 2023-12

7. 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 — 2023-9

8. Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

   J. Riebesell, R. Goodall, ..., K. Persson — 10.48550/arXiv.2308.14920 — 2023-8

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 commits stars title ↓

• pymatgen 1405 ⭐ 1003 commits Python, Cython, Jupyter Notebook

  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.

• atomate2 128 ⭐ 367 commits Python

  atomate2 is a library of computational materials science workflows used by the Materials Project and beyond. It supports multiple DFT codes and downstream analysis tools. Recently, we added machine learning potential-powered structure relaxation workflows.

• Matbench Discovery 71 ⭐ 323 commits Python, Svelte, CSS

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

• MultiSelect 271 ⭐ 273 commits TypeScript, Svelte, CSS

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

• pymatviz 130 ⭐ 254 commits Python, Svelte, CSS

  A toolkit for visualizations in materials informatics to complement pymatgen.

• Aviary 43 ⭐ 234 commits Python

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

• TikZ 185 ⭐ 192 commits TeX, Svelte, Python

  Collection TikZ figures for concepts in physics/chemistry/ML.

• CHGNet 199 ⭐ 181 commits Python, C, Cython

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

• Elementari 126 ⭐ 174 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.

• jobflow 87 ⭐ 117 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 66 ⭐ 79 commits Python, TeX

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

• MatCalc 42 ⭐ 75 commits Python

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

• Normalizing Flows 1316 ⭐ 73 commits Python

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

• MACE-MP 383 ⭐ 22 commits Python, Shell

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

• Dielectrics 7 ⭐ 17 commits HTML, Python

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

  Education

• PhD Student University of Cambridge

  Thesis title: Can machine learning accelerate high-throughput searches for novel functional materials?

• MPhil in Scientific Computing University of Cambridge

  Thesis title: Probabilistic Data-Driven Discovery of Thermoelectric Materials

• MSc in Physics ITP Heidelberg

  Thesis title: Functional Renormalization Group Analytically Continued to Finite Temperatures

• BSc in Physics Hamburg University

  Thesis title: van der Waals Corrections for Density Functional Theory - DFT+D2 applied to Graphene-hBN-Heterostructures

  Awards

• Studienstiftung des deutschen Volkes

  Full scholarship for outstanding students (German Academic Scholarship Foundation) 2015 - 2023

  Volunteer Work

• Studenten bilden Schüler Board member and head of IT

  Non-profit student initiative that connects University student volunteers with children from underprivileged families for free tutoring.

• Afara Foundation Board member and head of IT

  A non-profit student association that runs orphanages in Namibia and funds children's education and medical treatment in several African countries.