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 — right in your browser.

The web app

Everything runs right in your browser. No downloads, no accounts, no setup required.

Computation capabilities

Atomiverse leverages machine-learned interatomic potentials (MLIPs) to make molecular simulations fast and accessible. Available directly in the browser:

• 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, cube files, Molden files.
• 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.
• Share via URL — share your viewer state with colleagues via a simple link.

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}
   }