Massimo Delle Piane

Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems - Nature Communications Supramolecular polymers possess features typical of complex systems, but the mechanisms that lead to the emergence of collective properties inside them are often difficult to ascertain. Here the autho...

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 @natcomms.nature.com our work on the collective resilience of dynamical supramolecular polymers!🚀
www.nature.com/articles/s41...

This was quite a gigantic endeavor, demonstrating the power of this bivariate analysis on a range of dynamic systems, from water to metals, going up to real experimental movies (!!). Happy to have contributed! And kudos to Cristina Caruso for the amazing work! 🎉

Excited to announce the release of our preprint, "The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior", now available on @chemrxiv.bsky.social

👉 Read the full preprint here: chemrxiv.org/engage/chemr...

Optimal Spatiotemporal Resolutions In general, the comprehension of any type of complex system depends on the resolution used to look at the phenomena occurring within it. But identifying a priori, for example, the best time frequencie...

#compchem #chemsky #data-analysis #machinelearning #AI folks 📣

In #ArXiv we propose the concept of “optimal spatiotemporal resolutions”!🤯
We demonstrate how any system/data has its own to be best studied/characterised & how to learn these directly from the system’s data!🚀
arxiv.org/abs/2412.13741

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You don't need to finish and submit your paper before the end of the year. You won't gain time if you do as nobody is going to review it over the holidays. And you know what, your paper is going to be so much better when you read through it with a well-rested brain in the new year.

#AcademicSky

Relevant, hidden, and frustrated information in high-dimensional analyses of complex dynamical systems with internal noise Extracting from trajectory data meaningful information to understand complex systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent ...

When studying complex systems, a common belief is that high-dimensional analyses are desirable to prevent losing important information... but to what extent this is really needed/beneficial remains often unclear.😵‍💫
In our last Arxiv preprint we challenge this assumption:
arxiv.org/abs/2412.094... 🚀🤩

Great interdisciplinary work in collaboration with Prof. @kay-severin.bsky.social's group at EPFL! Our computational work (with Luigi Leanza and Giovanni M. Pavan) provided key insights into the self-assembly mechanisms behind these fascinating structures! 🧅