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Gordon Bell Prize Awarded for Materials Simulations That Achieve Quantum Accuracy at Scale

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The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics.

Credit: Association of Computing Machinery

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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