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UID:submissions.supercomputing.org_SC23_sess296_gb104@linklings.com
SUMMARY:Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simu
 lations of Quasicrystals and Interacting Extended Defects in Metallic Allo
 ys
DESCRIPTION:Sambit Das, Bikash Kanungo, and Vishal Subramanian (University
  of Michigan); Gourab Panigrahi and Phani Motamarri (Indian Institute of S
 cience); David Rogers (Oak Ridge National Laboratory (ORNL)); and Paul Zim
 merman and Vikram Gavini (University of Michigan)\n\nAb initio electronic-
 structure has remained dichotomous between achievable accuracy and length-
 scale. Quantum many-body (QMB) methods realize quantum accuracy but fail t
 o scale. Density functional theory (DFT) scales favorably but remains far 
 from quantum accuracy. We present a framework that breaks this dichotomy b
 y use of three interconnected modules: \n\n   (i)  invDFT: a methodologica
 l advance in inverse DFT linking QMB methods to DFT; \n\n   (ii)  MLXC: a 
 machine-learned density functional trained with invDFT data, commensurate 
 with quantum accuracy; \n\n   (iii)  DFT-FE-MLXC: an adaptive higher-order
  spectral finite-element (FE) based DFT implementation that integrates MLX
 C with efficient solver strategies and HPC innovations in FE-specific dens
 e linear algebra, mixed-precision algorithms, and asynchronous compute-com
 munication. \n\nWe demonstrate a paradigm shift in DFT that not only provi
 des an accuracy commensurate with QMB methods in ground-state energies, bu
 t also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP
 64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier sup
 ercomputer.\n\nRegistration Category: Tech Program Reg Pass\n\nSession Cha
 ir: Taisuke Boku (University of Tsukuba, Advanced HPC‑AI Research and Deve
 lopment Support Center (HAIRDESC))\n\n
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