Matteo Becchi

I work with Molecular Dynamics simulations, usually with coarse-granied models, in the field od supramolecular self-assembly. I also work in developing tools of data analysis devoted to the analysis of the simulation trajectories.

• Data analysis tools development.
• Molecular Dynamics simulations for supramolecular self-assembly.

ERC sectors

• Becchi, Matteo; Pavan, Giovanni M. (2025)
  Maximum information extraction via clustering and minimization of Shannon entropy. In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY. ISSN 2632-2153
  Contributo su Rivista
• Martino, Simone; Doria, Domiziano; Lionello, Chiara; Becchi, Matteo; Pavan, Giovanni M (2025)
  A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information. In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY, vol. 6. ISSN 2632-2153
  Contributo su Rivista
• Doria, Domiziano; Martino, Simone; Becchi, Matteo; Pavan, Giovanni M. (2025)
  Data-driven assessment of optimal spatiotemporal resolutions for information extraction in noisy time series data. In: JOURNAL OF CHEMICAL PHYSICS ONLINE, vol. 162. ISSN 1089-7690
  Contributo su Rivista
• Lionello, Chiara; Becchi, Matteo; Martino, Simone; Pavan, Giovanni M. (2025)
  Relevant, Hidden, and Frustrated Information in High-Dimensional Analyses of Complex Dynamical Systems with Internal Noise. In: JOURNAL OF CHEMICAL THEORY AND COMPUTATION, vol. 21, pp. 6683-6697. ISSN 1549-9618
  Contributo su Rivista
• Becchi, Matteo; Fantolino, Federico; Pavan, Giovanni M. (2024)
  Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems. In: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 121. ISSN 0027-8424
  Contributo su Rivista