Scientist. Full Professor at Politecnico di Torino (IT) @politonews, @ERC_research grantee, head of @LabPavan - gmpavanlab.com
Joined December 2010
- Tweets 310
- Following 422
- Followers 565
- Likes 728
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Pinned Tweet
10 May 2019
Extermely happy to have been appointed Full Professor at the Politecnico di Torino!! @PoliTOnews #PoliTo
I am super-excited to start a new research group in Italy!
News on multiple open positions at various levels in my group will follow very soon!!!
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Giovanni M. Pavan retweeted
1 Aug 2025
Our collaborative work "The #Martini3 #Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior" is now published in #ACSCentralScience! 🎉
📄 Read: pubs.acs.org/doi/10.1021/acs…
💾 GitHub: github.com/Martini-Force-Fie…
#MolecularDynamics #Biophysics #Simulations #Lipids
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Giovanni M. Pavan retweeted
1 Aug 2025
A true community effort ! The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior | ACS Central Science pubs.acs.org/doi/10.1021/acs…
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Giovanni M. Pavan retweeted
1 Aug 2025
Great to see this finally out!! Congrats to all coauthors!🍾
1 Aug 2025
A true community effort ! The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior | ACS Central Science pubs.acs.org/doi/10.1021/acs…
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Giovanni M. Pavan retweeted
19 Jun 2025
Metals owe their properties to how local defects emerge & amplify in collective dislocations under stress.🛠️
👉We show how tracking local atomic fluctuations & their space&⏱️correlations allows tracking metals' behavior through the elastic&plastic phases🚀
pubs.aip.org/aip/jcp/article…
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Giovanni M. Pavan retweeted
18 Jun 2025
How much complexity is needed in self-assembling molecular systems to observe non-trivial emergent behaviors typical of more complex, higher-scale systems?🤯
Not much!😲
See @NatureComms our work on the collective resilience of supramolecular polymers!🚀
nature.com/articles/s41467-0…
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Giovanni M. Pavan retweeted
19 Feb 2025
#LEAP is out @PNASNexus!🚀
Building on abstract concepts of local fluctuations & their correlations in space & ⏱️, #LEAP provides info on the physics of complex dynamical systems from the atomic- to the macro-scale in agnostic & purely data-driven way!🤩
academic.oup.com/pnasnexus/a…
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Giovanni M. Pavan retweeted
16 Dec 2024
When studying complex systems, common belief is that high-dimensional analyses are desirable to prevent losing important information... but to what extent this is really needed/beneficial remains often unclear.😵💫
Now @arxiv we challenge this assumption:
arxiv.org/abs/2412.09412v2 🚀
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Giovanni M. Pavan retweeted
20 Nov 2024
Big congrats to @MatteoCioniMC on his PhD defense! 🎉✨
After years of hard work @PoliTOnews, Matteo's brilliant research in molecular dynamics & complex systems has pushed boundaries. 🌌💻
We're proud to celebrate this huge milestone with him! 🚀👨🎓
#PhDDefense #Science
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Giovanni M. Pavan retweeted
20 Nov 2024
The choice of descriptors is key for extracting information from data.🧐
Look at @SimoneM118 work!
arxiv.org/abs/2411.12570
While advanced descriptors may rely on higher signal-to-noise, we show how even the simplest descriptor may become super efficient upon local denoising! 🚀
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Giovanni M. Pavan retweeted
8 Aug 2024
Finally out in @PNASNews “Onion 🧅 Clustering” 🚀: an efficient & essentially parameter-free unsupervised clustering method that can classify statistically relevant fluctuations & microscopic dynamical domains in noisy timeseries data of any kind!!🤩🥳
pnas.org/doi/10.1073/pnas.24…
14 Feb 2024
Tracking statistically-relevant fluctuations in noisy time-series data is key in many fields, from #MachineLearning, to the study of signals & complex systems!
Use Onion #Clustering🧅: easy, unsupervised, physically-interpretable, statistically-robust!🚀🤯
arxiv.org/abs/2402.07786
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Giovanni M. Pavan retweeted
24 Apr 2024
Recent simulations showed non-trivial dynamics in metals. But these often remain hard to prove experimentally.
Here we combine for the 1st time experimentally-resolved Au-NP structures with MD simulations & #ML, proving their real-time atomic dynamics!
onlinelibrary.wiley.com/doi/…
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Giovanni M. Pavan retweeted
14 Feb 2024
😎: Onion Clustering does not attempt to fit all data in a time-series into clusters! Depending on the resolution, dt, it classifies all the information that can be classified in statistically robust way & store the "undetermined" information in the ENV0 cluster! 🚀🤯🔥
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Giovanni M. Pavan retweeted
14 Feb 2024
Tracking statistically-relevant fluctuations in noisy time-series data is key in many fields, from #MachineLearning, to the study of signals & complex systems!
Use Onion #Clustering🧅: easy, unsupervised, physically-interpretable, statistically-robust!🚀🤯
arxiv.org/abs/2402.07786
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Giovanni M. Pavan retweeted
23 Jan 2024
Stereocontrolled Self-Assembly of a Helicate-Bridged CuI12L4 Cage That Emits Circularly Polarized Light | JACS @ChemCambridge @Cambridge_Uni @PoliTOnews
@supsi_ch @LabPavan #Stereocontrolled #Helicate #Cu #Cage #Light #Emission pubs.acs.org/doi/10.1021/jac…
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Giovanni M. Pavan retweeted
13 Dec 2023
Today at the cover of @ChemicalScience our collaboration with @LabPavan 🇮🇹🇪🇸 Moving supramolecular contacts around cyclic peptides 💍 allows control self-assembly in 2D 📄 Wonderful to work with @asicardellini @InsuaNacho @SandraDA95 @nanominions 🔖🎉👏
pubs.rsc.org/en/content/arti…
ALT https://pubs.rsc.org/en/journals/journalissues/sc#!recentarticles&adv
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Giovanni M. Pavan retweeted
Thank you again for publishing this great work with @MLSTjournal. We hope to have the opportunity to work with you again soon!
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Giovanni M. Pavan retweeted
Great new work by @martanit @asicardellini @MatteoCioniMC @pamara54 and Gabor Csanyi @LabPavan @PoliTOnews @supsi_ch @Cambridge_Eng - '#Machinelearning of microscopic structure-dynamics relationships in complex molecular systems' - iopscience.iop.org/article/1… #compchem #materials #AI
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Giovanni M. Pavan retweeted
6 Dec 2023
Fresh @ J. Chem. Eng. Data the new work of @mattiaperr! 🥳🤩
We tested SwarmCG to automatically optimize rigid 3-site water models onto multiple microscopic (exp. g(r)) macroscopic thermodynamic properties, hitting the physical limits of such models!🧐🤯
pubs.acs.org/doi/10.1021/acs…
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