Giovanni M. Pavan Lab @LabPavan
Into self-assembly, complex molecular systems & materials science w/ computer models, simulations & machine learning | Managed by @LabPavan members @PoliTOnews gmpavanlab.polito.it Torino (IT) Joined December 2019-
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Giovanni M. Pavan Lab retweeted
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/ac… 💾 GitHub: github.com/Martini-Force-… #MolecularDynamics #Biophysics #Simulations #Lipids
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Giovanni M. Pavan Lab retweeted
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/ac…
Great to see this finally out!! Congrats to all coauthors!🍾
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/ac…
Congratulations to all coauthors @mattiaperr @MatteoCioniMC @MaxDellePiane @pamara54!!🙌🥳👏 Work supported by @ERC_Research & CINECA & DISAT & @PoliTOnews!🙏🙏
This provides a general data-driven approach for studying the internal physical behavior of metallic (but also other types of) systems just building on the abstract concepts of fluctuations & spatio-temporal fluctuations' correlations. 🤯🤩
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/articl…
Congratulations to all coauthors!👏🙌🚀 🙏@ERC_Research CSCS @PoliTOnews DISAT SUPSI🙏
In non-cooperative systems, all entities populating them suffer comparably from external perturbations. In cooperative systems (with internal "interactions-avidity"), the stronger units survive at the expense of the weaker ones!🤯
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/s4146…
@MarioBarbatti Great paper! And thank you @MarioBarbatti for including our work on defects & dynamics of supramolecular polymers! nature.com/articles/s4146…
Giovanni M. Pavan Lab retweeted
Twenty case studies showing theoretical predictions in chemistry that were later confirmed experimentally. #CompChem doi.org/10.1515/pac-20…
Giovanni M. Pavan Lab retweeted
Sushi assembly with speakers! #ISMSC2025
Giovanni M. Pavan Lab retweeted
Only a few days left before we kick of the GRS and GRC meeting in Les Diablerets! We hope you have safe travels!
Giovanni M. Pavan Lab retweeted
🤫 you can get a better universal #machinelearning potential by training on fewer than 100k structures. too good to be true? head to arxiv:2503.14118, to the atomistic cookbook, or to the better science social if you want to find out more about PET-MAD. 🧑🚀 over and out 👋
This work required the support of many people! Congratulations to @c__caruso for the massive effort!👏 Congrats also to all other coauthors -@martanit @asicardellini @MatteoCioniMC @mattiaperr Massimo Delle Piane @pamara54 - for the huge work!🙌 🙏 Supported by @ERC_Research 🙏
Complete details & LEAP analysis code available at: github.com/GMPavanLab/LEAP and at: doi.org/10.5281/zenodo…
#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/arti…
#LEAP analyses are agnostic & require just that a trajectory of the building blocks constituting the complex dynamical system is available. Useful to explore complex dynamical systems whose physics is unknown a priori & to revisit well-known phenomena under a new perspective.🤯
#LEAP allows to: 1) Detect relevant fluctuations in noisy trajectories 2) Classify fluctuations in classes based on their physics 3) Identify correlations in space & time between fluctuations 4) Unveil local & collective events, their correlations, causal relationships, etc.
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