A team of researchers from U.S. and Indian institutions was awarded the 2023 ACM Gordon Bell Prize for a materials-simulation framework that combines the accuracy provided by Quantum Many-Body (QMB) methods with the efficiency of Density-Functional Theory (DFT).
The team proposed three interconnected modules for their new method.
One was a methodological advance in inverse DFT (invDFT) linking QMB methods to DFT.
The second module was a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy, which they dubbed MLXC.
The third was an adaptive higher-order spectral finite-element based DFT implementation that integrates MLXC with efficient solver strategies and supercomputing innovations in linear algebra, mixed-precision algorithms, and asynchronous compute-communication.
The award was presented at the International Conference for High-Performance Computing, Networking, Storage and Analysis (SC23).
From Association for Computing Machinery
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Abstracts Copyright © 2023 SmithBucklin, Washington, D.C., USA
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