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@MISC{Wasmer:1020056,
      author       = {Wasmer, Johannes and Rüssmann, Philipp},
      othercontributors = {Chen, Po-Yen and Burdulea, Ilinca and A, Lixia},
      title        = {{S}i{S}c {L}ab 2022, {P}roject 6. {A} machine learning
                      playground in quantum mechanical simulation.},
      reportid     = {FZJ-2023-05857},
      year         = {2022},
      note         = {$https://iffmd.fz-juelich.de/RhXA4J_JTwebjYKG35TbuQ$},
      abstract     = {Density functional theory (DFT) is one of the most widely
                      used simulation techniques. About a third of world
                      supercomputing time is spent each year on such calculations.
                      DFT approximates the solution to the Schrödinger equation,
                      to elucidate the electronic structure of materials and
                      molecules. While it makes quantum many-body problems in a
                      lot of systems of interest tractable, it still is
                      computationally demanding. It typically scales in $O(N^3)$
                      with the number of electrons in the system, limiting its
                      application to systems with a few thousand atoms at most.
                      Over the last 15 years, the development of surrogate models
                      based on machine learning (ML) has steadily gained momentum
                      in the field of atomistic simulation. In ab initio molecular
                      dynamics for instance, machine-learned interatomic
                      potentials at a fraction of the cost and comparable accuracy
                      of mechanistic methods have already become mainstream. Now
                      these surrogate models also start to increasingly be
                      developed to predict the underlying electronic structure
                      properties of atomic systems directly.In this project, the
                      students will be given the chance to play around with a wide
                      array of state-of-the-art models in this field, from
                      traditional kernel methods to deep graph convolution
                      networks. They will be provided with a computational
                      infrastructure and training datasets from DFT calculations.
                      The challenges ladder they will climb has the rungs a)
                      understanding the electronic structure data, b)
                      understanding the reasoning behind the various surrogate ML
                      modeling approaches, c) discovering common features of the
                      atomic systems to come up with clever optimizations of the
                      model architectures, and d) achieving reasonable prediction
                      accuracy for the targeted electronic structure properties.
                      The project goals will be adjusted, in a reasonable range
                      given the limited time frame, according to the speed of
                      progress and the particular interests of the
                      students.Expected prerequisites: Applied Quantum Mechanics,
                      basic Python skills. Desired, but optional: Physics track,
                      some hands-on ML experience.Advisors : Johannes Wasmer,
                      Philipp Rüßmann , Stefan Blügel},
      month         = {Nov},
      date          = {2022-11-01},
      organization  = {RWTH Aachen University, Aachen
                       (Germany), 1 Nov 2022 - 1 Mar 2023},
      subtyp        = {Blog},
      cin          = {IAS-1 / PGI-1},
      cid          = {I:(DE-Juel1)IAS-1-20090406 / I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521) / HDS LEE - Helmholtz
                      School for Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-5211 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/1020056},
}