Matteo Cioni
@MatteoCioniMC
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PhD student at Politecnico di Torino (Department of Applied Science and Technology, @LabPavan)
Torino, Piemonte
Joined March 2020
Metals owe their properties to how local defects emerge & amplify in collective dislocations under stress.🛠️ 👉We show how tracking local atomic fluctuations & their space&⏱️correlations allows tracking metals' behavior through the elastic&plastic phases🚀 https://t.co/XgdUosXWU0
pubs.aip.org
Metals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading f
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#LEAP is out @PNASNexus!🚀 Building on abstract concepts of local fluctuations & their correlations in space & ⏱️, #LEAP provides info on the physics of complex dynamical systems from the atomic- to the macro-scale in agnostic & purely data-driven way!🤩 https://t.co/Rs7HtD5IRv
academic.oup.com
Abstract. The behaviors of many complex systems, from nanostructured materials to animal colonies, are governed by local events/rearrangements that, while
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Big congrats to @MatteoCioniMC on his PhD defense! 🎉✨ After years of hard work @PoliTOnews, Matteo's brilliant research in molecular dynamics & complex systems has pushed boundaries. 🌌💻 We're proud to celebrate this huge milestone with him! 🚀👨🎓 #PhDDefense #Science
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Finally out in @PNASNews “Onion 🧅 Clustering” 🚀: an efficient & essentially parameter-free unsupervised clustering method that can classify statistically relevant fluctuations & microscopic dynamical domains in noisy timeseries data of any kind!!🤩🥳 https://t.co/fWH10JFATr
Tracking statistically-relevant fluctuations in noisy time-series data is key in many fields, from #MachineLearning, to the study of signals & complex systems! Use Onion #Clustering🧅: easy, unsupervised, physically-interpretable, statistically-robust!🚀🤯 https://t.co/2x5dBCkkUQ
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Nice paper shows that the ageing glassy dynamics in the adhesive elastic contact between granular particles influences the mechanics of the whole granular material. Plenty of evidence for the logarithmic relaxation in the ageing mathematically predicted by
pubs.acs.org
We develop a simple yet comprehensive nonlinear model to describe relaxation phenomena in amorphous glass-formers near the glass transition temperature. The model is based on the two-state, two-(ti...
Aging and stress relaxation in dry granular materials may be governed by thermal molecular properties at the scale of grain contacts Letter: https://t.co/Tp4EkuP65l Focus: https://t.co/6bxnpa0iE7
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Introducing fairchem - our revamped codebase consolidating our AI modeling efforts in chemistry and materials science. fairchem makes it easy to interface with our data, models, demos, and applications - including an easy to use ASE calculator: https://t.co/VGKb5OcTvV
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Thrilled to unveil our latest work! ✨✨Bridging experimental and computational approaches, we've achieved unprecedented insights! By integrating experimentally-reconstructed Au-NP structures with #MD simulations and #ML, we now unveil the real-time atomic dynamics of Au-NPs!🚀🚀
Recent simulations showed non-trivial dynamics in metals. But these often remain hard to prove experimentally. Here we combine for the 1st time experimentally-resolved Au-NP structures with MD simulations & #ML, proving their real-time atomic dynamics! https://t.co/FpN2OcCNMi
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Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies | Journal of the American Chemical Society @cmrisko
@riskolab @universityofky @burstenj #BigData #Philosophical #Constraints #Chemical #Ontologies
pubs.acs.org
Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called “big data” are collected and used at unprecedented scales with the idea that algorithms can be designed to aid...
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🧅🧅
Tracking statistically-relevant fluctuations in noisy time-series data is key in many fields, from #MachineLearning, to the study of signals & complex systems! Use Onion #Clustering🧅: easy, unsupervised, physically-interpretable, statistically-robust!🚀🤯 https://t.co/2x5dBCkkUQ
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Statement regarding my sacking from Max Planck Society https://t.co/UdD9DLpznD
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8 years without my Bro 💛 A stolen life. Yours, mine, ours. #veritàpergiulioregeni #giustiziapergiulioregeni 💛💛💛
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Best of luck, my friend! 👽👽
Big news! 🍻🥳 This week I officially started a new journey as a postdoctoral researcher @TUeindhoven in the Molecular Machine Learning Group led by Prof. @fra_grisoni ! Can't wait to see what the future holds!
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Training machine learning potentials for reactive systems: A Colab tutorial on basic models #machinelearning #compchem
onlinelibrary.wiley.com
A self-guided Colab tutorial about machine learning potential for reactive systems are presented in this work. The tutorial begins with the introduction of feedforward neural network and kernel-bas...
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Fresh @ J. Chem. Eng. Data the new work of @mattiaperr! 🥳🤩 We tested SwarmCG to automatically optimize rigid 3-site water models onto multiple microscopic (exp. g(r))+macroscopic thermodynamic properties, hitting the physical limits of such models!🧐🤯
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New @MLSTjournal: Combining local structural (SOAP) & dynamical (LENS) descriptors 1) improves the 2 separating relevant dynamical fluctuations from noise🚀 2) unveils microscopic structure-dynamic relationships for a variety of complex molecular systems🤯 https://t.co/G7NmeXGjfg
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New @MLSTjournal: Combining local structural (SOAP) & dynamical (LENS) descriptors 1) improves the 2 separating relevant dynamical fluctuations from noise🚀 2) unveils microscopic structure-dynamic relationships for a variety of complex molecular systems🤯 https://t.co/G7NmeXGjfg
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Philip Ball reviews In a Flight of Starlings: the Wonder of Complex Systems by Giorgio Parisi
physicsworld.com
Philip Ball reviews In a Flight of Starlings: the Wonder of Complex Systems by Giorgio Parisi
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It’s that special time of year when our PhD students showcase their research to the @PoliTOnews community. A big congrats to @martanit and @MatteoCioniMC on their presentations! 👏
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An enlightening seminar on molecular motors by @EPenocchio! Thank you for visiting and engaging in valuable discussions with us!
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I taught principal components analysis, PCA, in my #MachineLearning course yesterday. Some students were curious about the component loadings! Last night I coded this interactive PCA demonstration with @matplotlib. Change the data and watch the variance explained and loadings
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