Giovanni M. Pavan Research Group

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ERC Consolidator Grant

DYNAPOL – Modeling approaches toward bioinspired dynamic materials

Abstract 

The DYNAPOL project will develop multiscale molecular models and use advanced computational simulation and machine learning techniques to discover the fundamental chemical-physical principles to learn how to design new classes of artificial materials with bio-inspired dynamic properties, or similar to those of living materials.



Description of the research project 

DYNAPOL will explore new routes to design new types of artificial materials for various technological applications. It will use innovative chemical-physical concepts, different from those on which technological materials are typically based, and exploit self-assembly properties. The idea is to take inspiration from nature and how it builds complex materials possessing fascinating properties, such as the ability to actively respond to different types of external stimuli: environmental (temperature, salt concentrations, pressure), biological (specific interactions with proteins or tissues), chemical and physical. Examples of similar natural supramolecular materials are microtubules or protein filaments, which can reconfigure in response to specific inputs.
In order to design bioinspired artificial polymeric materials it is necessary to understand in detail the molecular principles that control their dynamic behavior, and to learn the relationships existing between the chemical structure of the constitutive self-assembling building blocks and the dynamic properties of the assemblies that these form across various spatio-temporal scales. To this end, the DYNAPOL project will use multiscale molecular models, advanced molecular simulation techniques and machine learning. The models obtained will be validated through continuous comparison with experimental data from various international collaborations. This is a highly multidisciplinary research with a pioneering character, which will avail of the close collaboration between chemists, physicists, engineers and computer scientists.

Impact  

DYNAPOL is a fundamental research project that aims at exploring approaches and trace new routes toward innovative technological materials. The results of this project will impact various research fields and technological areas of high current interest, such as biomedical, pharmaceutical, energy, chemical. At the same time it will develop new knowledge allowing to explore applications not yet foreseen in the field of innovative materials and complex molecular systems.

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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Avatar Alexander Menke @alexmenke62 ·
11 Aug

Pleased to announce my first collaborative authorship!

Well-Tempered Metadynamics Simulations Predict the Structural and Dynamic Properties of a Chiral 24-Atom Macrocycle in Solution https://pubs.acs.org/doi/10.1021/acsomega.2c03536#.YvRKK1PsPRo.twitter

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Avatar Chiara Lionello @lionellochiara ·
12 Aug

It's so difficult to say goodbye to these amazing people! Thanks @SaricLab for this great opportunity! Hope to see you again soon!❤️🍷

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Avatar Andela Saric @sariclab ·
12 Aug

Group hike in the vineyards of Vienna to say “see you again” to @LionelloChiara, a wonderful visiting Phd student from @LabPavan at @PoliTOnews. Thanks for sharing your science&fun with us! https://twitter.com/lionellochiara/status/1558134441024839680

Chiara Lionello @LionelloChiara

It's so difficult to say goodbye to these amazing people! Thanks @SaricLab for this great opportunity! Hope to see you again soon!❤️🍷

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Avatar Hybrid Materials Interfaces @hmi_group ·
12 Aug

Just learnt a lot about the behaviour of metal surfaces and lipid layers through data-driven analyses and representations - great talk by @MaxDellePiane from @LabPavan group!

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Avatar Pablo Piaggi @piaggipablo ·
8 Aug

Our paper on ice nucleation using first principles MD simulations driven by an ML model just appeared online in PNAS. 300,000 atoms with ab initio accuracy! Thanks @snsf_ch @doescience @CcscCsi @DeepModeling @plumed_org for making this possible. https://www.pnas.org/doi/full/10.1073/pnas.2207294119

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