Giovanni M. Pavan Research Group

Research

We combine molecular modeling, computer simulations & machine learning to explore new routes in physical chemistry, self-assembly & bioinspired materials.

Self-assembly & supramolecular chemistry

A few selected papers:
Nature Commun. 2017, 8, 147
ACS Nano 2017, 11, 1000-11

Stimuli responsive and reactive supramolecular systems

A few selected papers:
JPCB 2018, 122, 4169-4178
JACS 2014, 136, 5385-99

Out-of-equilibrium molecular systems

A few selected papers:
ACS Nano 2019, 13, 4, 4322-4334

Complex molecular systems – auto regulation & emerging properties

Defects in dynamic supramolecular materials

A few selected papers:
J. Phys. Chem. B 2020, 124, 3, 589-599

Our research is currently supported by

Giovanni M. Pavan LabFollow

Retweet on Twitter Giovanni M. Pavan Lab Retweeted

Avatar ETH Zurich@ETH_en·

24 Feb

A digital twin of our #planet is to #simulate the Earth #system in future. It is intended to support policy-makers in taking appropriate measures to better prepare for extreme events.
https://ethz.ch/en/news-and-events/eth-news/news/2021/02/a-highly-accurate-digital-twin-of-our-planet.html

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Retweet on Twitter Giovanni M. Pavan Lab Retweeted

Avatar Nature@nature·

22 Feb

News & Views: A Nature paper reports an accessible machine-learning tool that can accelerate the optimization of a wide range of synthetic reactions — and reveals how cognitive bias might have undermined optimization by humans. https://go.nature.com/2O2Apco

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Avatar Giovanni M. Pavan Lab @LabPavan·

17 Feb

Great work @ric_capelli!
A data-driven SOAP metrics to compare & classify lipid force fields based on the local environments surrounding the lipids along MD simulations. Versatile & not restricted to lipids! Useful to compare a variety of dynamic systems! https://chemrxiv.org/articles/preprint/A_Data-Driven_Dimensionality_Reduction_Approach_to_Compare_and_Classify_Lipid_Force_Fields/14039834

Riccardo Capelli@ric_capelli

The last @LabPavan preprint is out on @ChemRxiv! We employed a dimensionality reduction approach to compare lipid force fields, and we discovered that most of the relevant differences is not in the average, but in the local representation of the system!

https://chemrxiv.org/articles/preprint/A_Data-Driven_Dimensionality_Reduction_Approach_to_Compare_and_Classify_Lipid_Force_Fields/14039834/1

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Avatar Larissa von Krbek @vkrbek·

15 Feb

(1/2) @aprahamian & I are organizing a #SystemsChemistry symposium on March 22-23 #ChemSystemsMeet, @nanoGe_Conf. Registration is now open and free for first 30 students, curtesy of @RoySocChem (+more depending on funding)
http://nanoge.org/ChemSystemsMeet/general-information
#realtimechem
#ChemTwitter ...

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Avatar Giovanni M. Pavan Lab @LabPavan·

29 Jan

Fascinated by molecular exchange in supramolecular polymers?
Your dream is to "encode" exchange pathways in supramolecular systems?
Better to start learning how to control defects & their statistical behavior!
All this in our last @ChemRxiv preprint: https://chemrxiv.org/articles/preprint/Controlling_Exchange_Pathways_in_Dynamic_Supramolecular_Polymers_by_Controlling_Defects/13655864

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