Das, S and Kanungo, B and Subramanian, V and Panigrahi, G and Motamarri, P and Rogers, D and Zimmerman, PM and Gavini, V (2023) Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys. In: UNSPECIFIED.
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Abstract
Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1 peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer. © 2023 ACM.
| Item Type: | Conference Paper |
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| Publication: | International Conference for High Performance Computing, Networking, Storage and Analysis, SC |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to authors. |
| Keywords: | Computation theory; Density functional theory; Electronic structure; Ground state; Lattice theory; Linear algebra; Machine learning; Quantum chemistry; Quantum computers; Quantum theory; Quasicrystals; Supercomputers, Density-functional-theory; Exascale computing; Finite element; Heterogeneous architectures; Light weight alloys; Machine-learning; Many body; Mixed precision; Performance; Quantum simulations, Inverse problems |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 25 May 2024 12:14 |
| Last Modified: | 25 May 2024 12:14 |
| URI: | https://eprints.iisc.ac.in/id/eprint/84955 |
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