One platform,
full workflow.

Atomiverse combines the entire molecular workflow into one platform. Load structures from files (XYZ, CIF, SDF/MOL, multi-frame trajectories) or generate them from SMILES strings. Visualize and measure in 3D. Build new structures interactively. Run MLIP-powered calculations. Export results. All from the same interface — or automate everything from Python.

Three ways to work

Whether you prefer a visual interface, a Python script, or running on your own cluster — Atomiverse meets you where you are.

Computation capabilities

Atomiverse leverages machine-learned interatomic potentials (MLIPs) to make molecular simulations fast and accessible. Available through both the web app and the Python SDK:

• Single-point energies — evaluate energies at any geometry.
• Geometry optimization — minimize structures or search for transition states.
• Vibrational analysis — compute vibrational frequencies, visualize modes, and inspect IR spectra.
• Thermochemistry — ideal-gas entropy, enthalpy, and Gibbs free energy.
• Potential energy scans — 1D scans along any geometric parameter.
• Conformer generation — rapid ensemble generation with geometry filtering and deduplication.
• Reaction workflows — atom mapping, TS geodesic interpolation, barrier analysis, and reaction network generation.

Additional features

• Format support — XYZ, CIF, SDF/MOL, SMILES, multi-frame trajectories, COSMO data.
• Interactive measurements — bond lengths, angles, dihedrals, with customizable display options.
• Non-covalent interactions — automatic detection and visualization of hydrogen bonds and close contacts.
• Flexible export — PNG screenshots, animated GIFs, XYZ files, and SMILES strings.
• REST API — full programmatic access to parsing, conversion, building, and job submission endpoints.
• ASE integration — the Python SDK works directly with ASE Atoms objects, fitting into existing computational chemistry pipelines.

How to cite

If you use Atomiverse in your work, please consider citing the relevant projects below.

1. Atomiverse

   Atomiverse — Interactive Molecular Visualization, Building & Computation.
   https://www.atomiverse.com
2. xyzrender — molecular rendering

   Goodfellow, A. S. (2026). xyzrender: Publication-quality molecular graphics. [Computer software].
   https://github.com/aligfellow/xyzrender
3. PET-MAD — machine-learned interatomic potential

   Malosso, C., Bigi, F., Pegolo, P., Abbott, J. W., Loche, P., Rossi, M., Ceriotti, M., & Mazitov, A. (2026). High-quality, high-information datasets for universal atomistic machine learning. arXiv:2603.02089.

   BibTeX

   @article{malosso2026petmad,
     title   = {High-quality, high-information datasets for
                universal atomistic machine learning},
     author  = {Malosso, Cesare and Bigi, Filippo and Pegolo, Paolo
                and Abbott, Joseph W. and Loche, Philip
                and Rossi, Mariana and Ceriotti, Michele
                and Mazitov, Arslan},
     journal = {arXiv preprint arXiv:2603.02089},
     year    = {2026}
   }

4. mlip.cpp — MLIP inference runtime

   Spackman, P. R. mlip.cpp: Standalone C++ implementation of Machine Learning Interatomic Potentials using ggml.
   https://github.com/peterspackman/mlip.cpp
5. racerTS — conformer ensemble generation

   Schmid, S. P., Seng, H., Kläy, T., & Jorner, K. (2025). Rapid generation of transition-state conformer ensembles via constrained distance geometry. ChemRxiv.
   doi:10.26434/chemrxiv-2025-d50pd

   BibTeX

   @misc{schmid_rapid_2025,
     title     = {Rapid generation of transition-state conformer
                  ensembles via constrained distance geometry},
     url       = {https://chemrxiv.org/engage/chemrxiv/article-details/69173ebea10c9f5ca165ef65},
     doi       = {10.26434/chemrxiv-2025-d50pd},
     language  = {en},
     publisher = {ChemRxiv},
     month     = nov,
     author    = {Schmid, Stefan P. and Seng, Henrik
                  and Kl\"ay, Thibault and Jorner, Kjell},
     year      = {2025}
   }

6. CREST — conformer geometry deduplication

   Pracht, P., Bohle, F., & Grimme, S. (2020). Automated exploration of the low-energy chemical space with fast quantum chemical methods. Phys. Chem. Chem. Phys., 22(14), 7169–7192.
   doi:10.1039/C9CP06869D

   BibTeX

   @article{pracht2020crest,
     title     = {Automated exploration of the low-energy chemical
                  space with fast quantum chemical methods},
     author    = {Pracht, Philipp and Bohle, Fabian
                  and Grimme, Stefan},
     journal   = {Physical Chemistry Chemical Physics},
     volume    = {22},
     number    = {14},
     pages     = {7169--7192},
     year      = {2020},
     publisher = {Royal Society of Chemistry},
     doi       = {10.1039/C9CP06869D}
   }