DYNSIGHT
An open Python platform that streamlines the extraction and analysis of time-series data from simulations or experimentally resolved trajectories. Dynsight simplifies workflows, enhances accessibility, and supports the analysis of time-series and trajectory data to unravel the dynamic complexity of systems across different scales. Typical workflow suggested by Dynsight: (i) identifying and tracking objects, and resolving their trajectories (for example, in experimental systems where these are not automatically available as in molecular simulations); (ii) translating the trajectories into data that are easier to handle and analyze using suitable descriptors; and (iii) extracting meaningful information from these data.
PyPI package
https://pypi.org/project/dynsight/
Reference publication
S. Martino, M. Becchi, A. Tarzia, D. Rapetti, G. M. Pavan
“dynsight: an open Python platform for simulation and experimental trajectory data analysis”
arXiv 2025, DOI:10.48550/arXiv.2510.23493
ONION CLUSTERING
An unsupervised clustering method that can identify statistically relevant fluctuations and microscopic dynamical domains in noisy time-series data of any kind. The method proceeds layer-by-layer, classifying dynamical environments (clusters) in time-series data from the most evident to the least populated (hidden) ones, and iterating until no further dynamical clusters can be discriminated/classified in a statistically robust way. Onion Clustering analyses are fully data-driven and essentially parameter-free. Onion Clustering stands out as a general, robust, physically interpretable method useful to characterize and understand complex dynamical systems.
PyPI package
https://pypi.org/project/onion-clustering/
Reference publication
M. Becchi, F. Fantolino and G. M. Pavan
“Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems”
Proc. Natl. Acad. Sci. USA 2024, 121, e2403771121
LENS
The Local Environments and Neighbors Shuffling (LENS) descriptor allows tracking local fluctuations and unveiling the dynamic complexity of a variety of molecular systems. Analysis of LENS time series provides an insight into innately dynamic molecular ensembles and, offer interesting perspectives on the behavior of complex systems in general as, for example, phase transitions, nucleation phenomena, and dynamic phases equilibrium.
GitHub
https://github.com/GMPavanLab/LENS
Reference publication
M. Crippa, A. Cardellini, C. Caruso and G. M. Pavan
“Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling”
Proc. Natl. Acad. Sci. USA 2023, 120, e2300565120
TimeSOAP
A structurally rich description of atomic environments of the Smooth Overlap of Atomic Position (SOAP) descriptor considering the time-dependent local variations. TimeSOAP essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. It permits the detection of dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems.
GitHub
https://github.com/GMPavanLab/TimeSOAP
Reference publication
C. Caruso, A. Cardellini, M. Crippa, D. Rapetti and G. M. Pavan
“TimeSOAP: tracking high-dimensional fluctuations in complex molecular systems via time-variations of SOAP spectra”
J. Chem. Phys. 2023, 158, 214302
Swarm-CG
A software designed for automatically optimizing the bonded terms of a coarse-grained (CG) model of a molecule, in explicit or implicit solvent, with respect to a reference (e.g. all-atom (AA)) trajectory and starting from a preliminary CG model (topology and non-bonded parameters). The package is designed for usage with Gromacs and contains 3 modules for:
- Evaluating the bonded parametrization of a CG model
- Optimizing bonded terms of a CG model
- Monitoring an optimization procedure
GitHub
https://github.com/GMPavanLab/Swarm-CG
Reference publication
C. Empereur-Mot, L. Pesce, G. Doni, D. Bochicchio, R. Capelli, C. Perego and G. M. Pavan
“Swarm-CG: Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization”
ACS Omega 2020, 5, 32823–32843
C. Empereur-mot, R. Capelli, M. Perrone, C. Caruso, G. Doni and G. M. Pavan
“Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG“
J. Chem. Phys. 2022, 156, 024801