Description
Back to topAtomistic simulations such as molecular dynamics (MD) are a cornerstone of computational material science. MD is a powerful tool that can generate fully-resolved, (classically) dynamically correct trajectories based only on a description of the energetics of the interactions between atoms. A longstanding challenge in MD is the development of approximations to the exact quantum potential energy surface that are computationally affordable and scalable, therefore enabling simulations of much larger systems over much longer times than are possible using direct solutions to Schrödinger’s equation.
Until recently, the functional form of these so-called interatomic potentials was largely based on physical considerations. In the past years, machine learning approaches thoroughly reshaped the field through the introduction of numerical methods which require less prior knowledge, lead to lower regression errors, and better transferability. While machine learning has shown great promise, developments are often still guided by ad hoc heuristics, which slows down further progress. This calls for a rigorous study of the modeling and numerical errors involved in the representation of forces and energies obtained from quantum mechanics by models of classical mechanics, through both a priori or a posteriori error estimates, of uncertainty quantification for detecting which parameters influence most the results, of the influence of the training database or how it should be augmented to minimize prediction errors.
This workshop aims to explore mathematical challenges of this kind and to discuss how fundamental insights can be translated into practical improvements in the cost/accuracy tradeoff of the next generation of data-driven interatomic potentials, enabling robust large-scale simulations at unprecedented accuracies and spatio-temporal scales.
This workshop will include lightning talks for early career researchers (including graduate students). In order to propose a lightning session talk, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. The registration form should not be used to propose a lightning session talk.
The deadline for proposing is April 2, 2024. If your proposal is accepted, you should plan to attend the event in-person.
Organizers
Back to topSpeakers
Back to topSchedule
Back to topSpeaker: Ralf Drautz (Ruhr-Universität Bochum)
Speaker: Alice Allen (Los Alamos National Laboratory)
Speaker: Teresa Head-Gordon (University of California, Berkeley (UC Berkeley))
Speaker: Ilyes Batatia (Cambridge University)
Speaker: Jan Janssen (Max-Planck-Institut für Eisenforschung GmbH)
Speaker: Gabor Csanyi (University of Cambridge)
Speaker: Yangshuai Wang (University of British Columbia)
Speaker: Ngoc-Cuong Nguyen (Massachusetts Institute of Technology (MIT))
Speaker: Jigyasa Nigam (EPFL)
Speaker: Aparna Subramanyam (Los Alamos National Laboratory (LANL))
Speaker: Michele Ceriotti (EPFL)
Speaker: Cameron Owen (Harvard University)
Speaker: Bingqing Cheng (UC Berkeley)
Speaker: Cas van der Oord (University of Cambridge)
Speaker: Julien Lam
Speaker: James Kermode (University of Warwick)
Speaker: James Goff (Sandia National Laboratory)
Speaker: Rose Ceronsky (University of Wisconsin)
Speaker: Elena Gelzinyte (Fritz Haber Institute of the Max Planck Society)
Speaker: TBA
Speaker: Cosmin Marinica (CEA)
Speaker: Roman Zubatyuk (Carnegie Mellon)
Speaker: Anton Bochkarev (Ruhr-Universität Bochum)
Registration
Back to topIMSI is committed to making all of our programs and events inclusive and accessible. Contact to request accommodations.
In order to register for this workshop, you must have an IMSI account and be logged in. Please use one of the buttons below to login or create an account.