Timothée Devergne

PhD from @Sorbonne_Univ_, now postdoc @IITalk. Doing machine learning to study physical and chemical transformations(He/Him)

[CSML IIT Lab @PontilGroup]

1/ 🚀 Over the past two years, our team, CSML, at IIT, has made significant strides in the data-driven modeling of dynamical systems. Curious about how we use advanced operator-based techniques to tackle real-world challenges? Let’s dive in! 🧵👇

[Vladimir Kostic @vkostic30]

Short anouncment: for all those into #numerical linear algebra and/or stats and/or #machinelearning and #AI in numerical #computations, checkout the conference I am co-organizing in #NoviSad in #Serbia.

https://t.co/uEQA7HTCis

[Timothée Devergne @TDevergne]

Looking forward discussing this work on Thursday at #NeurIPS2024

[Timothée Devergne @TDevergne]

I Will be presenting our work with @vkostic30 @GroupParrinello and @MPontil using the infinitesimal generator(IG) to recover dynamical properties of a molecular system from biased simulations

[CSML IIT Lab @PontilGroup]

⚙️ From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach
✍️@TDevergne, @vkostic30, @GroupParrinello, @MPontil
📃https://t.co/TS1Ti4lsf7 | 📅 Poster #3806 Dec 12, 16:30 - 19:30.

[Timothée Devergne @TDevergne]

We applied this method to the well known cases of the Muller-Brown potential, alanine dipeptide in vacuum and chignolin folding in water

[CSML IIT Lab @PontilGroup]

At #NeurIPS2024 🇨🇦 our group will present 7 contributions! These span a diverse array of topics: from theoretical advances in stochastic processes and reinforcement learning to applications in molecular dynamics and uncertainty quantification.

[CSML IIT Lab @PontilGroup]

🚨Postdoc Opportunity in Scientific Machine Learning 🚨

Join us in designing cutting-edge learning algorithms for simulating physical systems! Focus on ML for dynamical systems. @ELLISforEurope @IITalk
Details: https://t.co/7RryN0IXBz 🌟 #postdocposition #postdoc

[Timothée Devergne @TDevergne]

I am happy to share with you our new paper from my PhD.
https://t.co/fbAEausgnI
In this work, we present an alternative to the Strecker pathway for the formation of Glycine under prebiotic conditions

[Timothée Devergne @TDevergne]

This has the advantage of treating the bias potential as a linear perturbation. To do so, we introduce a ridge regression estimator of the generator and a method to build a basis set using neural networks.

[Timothée Devergne @TDevergne]

I am happy to share our new preprint with @vkostic30 @GroupParrinello and @MPontil .
We introduce a method based on the infinitesimal generator to learn dynamics from biased data.
https://t.co/wFIacvqdvJ

[Luigi Bonati @LuigiBonati]

The work on combining machine learning CVs with transition path sampling simulations is out in @JCIM_JCTC!
Kudos to visiting PhD student Jintu Zhang!

check it out here: https://t.co/dmVCB61URr

[Parrinello Group @GroupParrinello]

🚨NEW PREPRINT🚨
@PeilinKang and @TrizioEnrico present a self-consistent method to:
-Compute the committor based on a variational principle🎢
-Extensively sample the transition state ensemble🎯
-Extract physical insights from the results🔍

📜 https://t.co/rGvsn0sRH8

@IITalk 🧵

[Rocio Semino @RS_Chemist]

Very happy about Emilio Mendez's best poster prize achievement in CECAM Quantum2 workshop! #proudPI
Emilio's poster included some of our very first MAGNIFY project results 👀 @ERC_Research

[CECAM @cecamEvents]

We are on day 3 of the Flagship workshop "Quantum2 on machine learning enhanced sampling" in #CECAMHQ & online!
Alexandre Tkatchenko, @ChengBingqing & Lukas Petersen have opened today's exciting program.
➡️ https://t.co/rleeCLOZIj

[Timothée Devergne @TDevergne]

@JWestermayr Rodolphe Vuilleumier presenting quantum Renyi entropy

[Timothée Devergne @TDevergne]

The workshop continues today, with many amazing talks and discussions planned

[Timothée Devergne @TDevergne]

@JWestermayr Now is the turn of Daria Ruth Galimberti talking about computing chemically accurate free energies from hybrid QM/QM simulations