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CECAM/Psi-k School on Machine Learning Interatom ... (No replies)

maxdveit
11 months ago
maxdveit 11 months ago

Dear Colleagues,

We are pleased to announce the upcoming CECAM – Psi-k School on Machine Learning Interatomic Potentials for Young and Early Career Researchers (ML-IP 2023), scheduled to take place from 6th to 10th November, 2023, at Aalto University in Espoo, Finland. An option for remote, online participation will be available to those unable to attend in person and to improve accessibility.

Machine learning interatomic potentials (ML-IPs) have now become established as a key technique in atomistic modelling. They allow the simulation of many diverse types of systems, from the molecular to the solid state, at the accuracy of highly sophisticated electronic structure methods but at a greatly reduced cost. While the general methodology of training and validating a machine learning potential has been well established, and many codes and integrated software applications exist to perform these tasks, there is still a need to educate young and early-career researchers in these tasks, as well as provide a pathway to enter the field and make valuable contributions for researchers who have promising ideas that could benefit from the application of ML-IPs.

The ML-IP school is designed to address this need, following on the success of the online-only 2021 edition of the workshop. It features a mix of keynote lectures and hands-on tutorials, delivered by outstanding researchers from a variety of career stages, designed to equip attendees with a comprehensive understanding of the fundamental techniques associated with designing, fitting, and validating ML-IPs. Whether you are new to the field or seeking to refine your expertise, this event offers an invaluable opportunity to delve into the intricacies of ML-IPs and their applications.

Confirmed speakers include:

  • Alice Allen (Los Alamos National Lab, US)
  • Nongnuch Artrith (Utrecht University, the Netherlands)
  • Jörg Behler (Ruhr-Universität Bochum, Germany)
  • Michele Ceriotti (École polytechnique fédérale de Lausanne, Switzerland) 
  • Rose Cersonsky (University of Wisconsin-Madison, US)
  • Cecilia Clementi (Freie Universität Berlin, Germany)
  • Zheyong Fan (Bohai University, China)
  • Guillaume Fraux (École polytechnique fédérale de Lausanne, Switzerland)
  • Andrea Grisafi (Ecole Normale Supérieure de Paris, France)
  • Dávid Kovács (University of Cambridge, UK)
  • Chris Pickard (University of Cambridge, UK)
  • Martin Uhrin (Université Grenoble Alpes, France)
  • Sander Vandenhaute (Ghent University, Belgium)
  • Chuck Witt (University of Cambridge, UK)
  • Linfeng Zhang (AI for Science Institute and DP Technology, China)

Topics covered include:

  • Structural representations
  • Fitting ML models
  • Dataset generation and curation
  • Software frameworks
  • Scientific applications
  • ML beyond the ground-state PES

Please see our website for further details: https://www.mlip-workshop.xyz/. A detailed program will be released there in the coming weeks.

Applications for participation will open from today, 16 August, and will begin to be reviewed from Monday 28 August. The last date to apply is Friday 22 September, but we strongly encourage those interested to apply as soon as possible to ensure full consideration. Please send your application using the following link: https://www.cecam.org/workshop-details/1213

You can find detailed guidelines for your application at https://www.mlip-workshop.xyz/participate – please make sure to follow them closely for full consideration.

Please pass this announcement on to anyone who you think would be interested!

We thank our sponsors for the school, Psi-k and CECAM for their generous funding, and look forward to welcoming you to ML-IP 2023 in November.

The organizers,

  • Max Veit
  • Felix-Cosmin Mocanu
  • Chiheb Ben Mahmoud
  • Kevin Kazuki Huguenin-Dumittan
  • Jigyasa Nigam
  • Sanggyu “Raymond” Chong
  • Carlo Maino
  • Federico Grasselli
  • Davide Tisi
  • Miguel Caro



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Ab initio (from electronic structure) calculation of complex processes in materials