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 Lab Follow

We explore new routes in physical chemistry, self-assembly & bioinspired materials with molecular modeling, computer simulations & ML. @PoliTOnews @supsi_ch

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Sosso Group @sossogroup ·

7 Nov

Paper accepted 🍾in @RSC_MolEng. Genetic Algorithms to optimise the SOAP descriptor. Spearheaded by Trent @WarwickComplex and Steven @HetSysCDT, in collaboration with Albert @WarwickEngineer, James @Cambridge_Uni and Anders @AstraZeneca. Have a peek at https://pubs.rsc.org/en/Content/ArticleLanding/2022/ME/D2ME00149G !

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Giovanni M. Pavan Lab @labpavan ·

4 Nov

The atomic structure of metal NPs is key for their properties...as much as their dynamics!
We use MD & ML to unravel such complexity.
Fresh on @ChemRxiv our work "#MachineLearning of Atomic Dynamics and Statistical Surface Identities in Gold Nanoparticles"
https://chemrxiv.org/engage/chemrxiv/article-details/63642e6aac45c7a2a9a45332

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Andrew Ferguson @ferglab ·

14 Oct

🚨 Applications open for new AI+Science Postdoctoral Fellows program at UChicago. Advance AI in sci + eng. Work in lab of choice at UofC, Argonne, Fermilab, or MBL. Dedicated mentoring team. Current UChicago postdocs eligible. Apply now! https://aiscience.uchicago.edu/

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Giovanni M. Pavan Lab @labpavan ·

14 Oct

Now @softmatter our collaboration w/ Cristian Micheletti: "Density-tunable pathway complexity in a minimalistic self-assembly model".🥳
We simulated a system where complexity clearly emerges from crowding!🧐
Nice work Matteo Becchi @ric_capelli @Clanzow!🙌
https://pubs.rsc.org/en/Content/ArticleLanding/2022/SM/D2SM00968D

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MME_group @ucl_mmegroup ·

3 Oct

We are looking to hire two PDRAs from January 2023, on our @ERC_Research-selected/@UKRI_News-funded project ht-MATTER. If you are passionate about MD simulations, enhanced sampling, nucleation and crystallization, get in touch with @matteoslvlgl! Official call out soon->pls RT!

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