Potestio Lab
517 posts

Potestio Lab
@r_potestio
Research team led by Raffaello Potestio at the University of Trento, Italy. Multi-scale modelling, computational biophysics, statistical mechanics, and pizza.
Trento, Trentino-Alto Adige Bergabung Kasım 2016
722 Mengikuti623 Pengikut

Biophysics, Stat Mech and Machine Learning will meet in Trento from July 7th to 11th, 2025 in our StatPhys29 Satellite Workshop "Molecular biophysics at the transition state: from statistical mechanics to AI": indico.ectstar.eu/event/252/.
Co-organized with @ai_ngrosso.

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This research is the result of a collaborative effort among the teams at the University of Trento, INFN-TIFPA, and the Donders Institute, and we are grateful for the support of our colleagues and institutions.
#MachineLearning #DeepLearning #NeuralNetworks #StatPhysics
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🥳 We are pleased to announce the publication of our paper “Density of States in Neural Networks: An In-Depth Exploration of Learning in Parameter Space” in Trans. on Machine Learning Research.
openreview.net/forum?id=BLDtW…
@MeleMargherita_ @ai_ngrosso @UniTrento @INFN_ @DondersInst
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The MEOW approach, implemented in the EXCOGITO software suite, attributes to atoms and residues a relevance score based on their dynamical and energetic properties, and allows one to rationalise their functional behaviour.
pubs.acs.org/doi/full/10.10…
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📢🥳 PAPER ALERT! 🥳📢
We are happy to advertise the publication on JPCB of the paper “A Multiscale Analysis of the CzrA Transcription Repressor Highlights the Allosteric Changes Induced by Metal Ion Binding”, by M. Rigoli, R. Potestio and R. Menichetti.
pubs.acs.org/doi/full/10.10…

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Our PhD student Camilla Spreti @Camilla_Spreti just gave a talk at the yearly PhD Workshop of the Physics Dept. of the University of Trento @UniTrento, illustrating her research on enhanced sampling of small stat mech systems.
Excellent job! 🥳
ppw2024.physics.unitn.it/home



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📢📢📢 Paper alert! 📢📢📢
Our latest effort is out!
"Molecular Dynamics Characterization of the Free and Encapsidated RNA2 of CCMV with the oxRNA Model", by G. Mattiotti, M. Micheloni, L. Petrolli, L. Rovigatti, L. Tubiana, S. Pasquali, R. Potestio
onlinelibrary.wiley.com/doi/10.1002/ma…

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