2023 Gordon Bell Prize Announced: Frontier Supercomputer's 'Quantum Level Accuracy” Materials Simulation Winner

The ACM Gordon Bell Prize was established in 1987 and awarded by the American Computer Society. It is known as the "Nobel Prize" in the supercomputing world. The award is given annually to recognize outstanding achievements in high-performance computing. The $10,000 prize is awarded to Gordon Bell, a pioneer in high-performance and parallel computing.

Recently, at the Global Supercomputing Conference SC23, the 2023 ACM Gordon Bell Award was awarded to an 8-member international team of American and Indian researchers who achieved large-scale quantum precision material simulation. The related project is titled "Large-scale materials modeling with quantum precision: ab initio simulations of quasicrystals and interaction-propagating defects in metal alloys."

Team members come from diverse backgrounds and come from the University of Michigan, Oak Ridge National Laboratory and the Indian Institute of Science (Bangalore)

Award-winning team member.

In 2021, a Chinese supercomputing application team consisting of 14 members won the Gordon Bell Award. The team members come from Zhijiang Laboratory, National Supercomputing Wuxi Center, Tsinghua University and Shanghai Quantum Science Research Center. The team won the award in recognition of their use of my country's new generation Sunway supercomputer to conduct "ultra-large-scale quantum random circuit real-time simulation." Previously, the team won the Gordon Bell Award for two consecutive years in 2016 and 2017

Research Overview

We know that molecular dynamics Science is the process of using computer simulations to better understand the movement of atoms and molecules within systems. Ab initio (Latin, ab initio) is a branch of molecular dynamics that has proven particularly useful for important problems in physics and chemistry, including better understanding of microscopic mechanisms, gaining new insights in materials science, and proving experiments Data etc.

Please click the following link to view the paper: https://dl.acm.org/doi/pdf/10.1145/3581784.3627037

The research, led by Vikram Gavini, a professor of mechanical engineering and materials science and engineering at the University of Michigan, used the Frontier (1.14 exaflop HPE Cray EX supercomputer) at the U.S. Department of Energy's Oak Ridge National Laboratory, using the Schrödinger equation. Simulations are performed using first principles methods. The equation describes the probabilistic properties of microscopic systems, and the findings can be used to design candidate materials for new alloys and advance other computational design efforts such as drug discovery.

Gavini's team worked at Frontier and Summit Super An integrated computing framework was used on the computer to simulate dislocations, or defects, in a magnesium system composed of nearly 75,000 atoms. Magnesium alloys are promising candidates as lightweight alloys, but vacancy dislocations within them can lead to brittleness and cracking problems. Understanding dislocations in magnesium alloys could lead to lighter, more flexible alloys for industry

##Comparison of this article with previous work.

The team is using the Perlmutter supercomputer at the National Energy Research Scientific Computing Center to study the stability of quasicrystals in ytterbium-cadmium alloys

The calculations were based on density functional theory, a quantum mechanical method for calculating the atomic and electronic structure of materials, and used machine learning to approach the level of accuracy of quantum many-body calculations. They used Frontier's 8,000 nodes with a maximum computing power of 659.7 petaflops

"As we strive to achieve greater accuracy, the number of available computing systems has dropped dramatically," Gavini express. "We use the results of quantum many-body calculations on smaller systems and use machine learning to infer universal constitutive relations for electrons, which can be used in larger density functional theory calculations. Combining these methods, we are able to use tools like Frontier The advantage of such a large machine is that it is close to quantum precision." Material simulation at scale.

This study is the latest milestone in a decade of work by the Frontier team. A previous study in 2019 used Summit to simulate more than 10,000 magnesium atoms and was also nominated for the Gordon Bell Prize.

The alloy production process involves the melting and mixing of metals. Defects formed during solidification may aid or harm material properties. The atomic structure of the material plays a crucial role in the behavior of these line defects, often called dislocations.

Aluminum is a malleable metal that can accommodate dislocation and movement thanks to its atomic structure. The atomic structure of magnesium cannot easily accommodate dislocations, making it more fragile. Gavini said: "Under the right circumstances, these defects can create unprecedented properties. Why? What generates these defects? How can we exploit these defects to achieve desirable rather than undesirable properties? In previous research, we explored the energy of individual dislocations in bulk magnesium. In this study, we investigate the The result is the most detailed image yet of this structure, with near-quantum precision. Gavini hopes to apply these methods to a wide range of studies.

"If we can perform these large-scale calculations with near-quantum precision, it means we can design better materials through computational design, explore compounds for drug discovery, and understand nanoparticles at a new level. and the details of the properties of the material system,” Gavini said. "Without exascale computing and Frontier, we wouldn't be able to do these types of calculations. Now that we know how to do it, we can apply these methods broadly to explore other problems."

According to the research team, this method can be widely used in many scientific fields and answer some challenging questions that have existed for decades, from aerospace to medicine.

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