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@INPROCEEDINGS{DiNapoli:1041786,
      author       = {Di Napoli, Edoardo and Davidovic, Davor and Genovese,
                      Luigi},
      title        = {{L}earning {M}aterials at e{X}ascale},
      reportid     = {FZJ-2025-02426},
      year         = {2024},
      abstract     = {Solving large and sparse numerical linear systems in
                      materials science on massively parallel supercomputers is a
                      complex endeavour that requires a delicate balance between
                      accuracy and computational efficiency. Challenges include
                      managing the scale and complexity of these systems,
                      optimising scalability on parallel architectures, and
                      addressing real-world material complexities. The LimitX
                      project represents a ground-breaking step in the field of
                      computational Materials Science and aims to develop an
                      innovative recommender system. This system aims to
                      revolutionise the solution of large-scale sparse linear
                      systems by accelerating and scaling the solutions of linear
                      systems so that materials science research can be routinely
                      performed on exascale clusters. At its core, this system
                      relies on a two-pronged approach: first, the development of
                      a spectral predictor system and, second, the use of an
                      extensive database of matrices that encapsulate the essence
                      of surrogate space in the field of materials science. The
                      spectral predictor system is the heart of the recommendation
                      system. It utilises deep learning techniques to predict
                      spectral properties that are crucial for the efficient
                      solution of linear systems. The extensive matrix dataset
                      captures the diversity of spectral patterns that occur in
                      material science calculations. The application of this
                      recommender system promises to be transformative, as it
                      enables simulations with hundreds of thousands of atoms, a
                      feat previously unrealisable on current pre-exascale
                      clusters. The linear scaling of DFT (density functional
                      theory) codes such as BigDFT will enable researchers to
                      simulate and analyse complex material systems with
                      unprecedented accuracy and computational efficiency, opening
                      up new horizons for scientific exploration in this
                      field.Item Type:},
      month         = {May},
      date          = {2024-05-12},
      organization  = {ISC High Performance 2024, Hamburg
                       (Germany), 12 May 2024 - 16 May 2024},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Simulation and Data
                      Laboratory Quantum Materials (SDLQM) (SDLQM) / Inno4Scale -
                      Innovative Algorithms for Applications on European Exascale
                      Supercomputers (101118139)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)SDLQM /
                      G:(EU-Grant)101118139},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-02426},
      url          = {https://juser.fz-juelich.de/record/1041786},
}