Argonne National Laboratory's Aurora exascale supercomputer is poised to play a pivotal role in cutting-edge materials science advancements. The fusion of exascale computing and artificial intelligence offers transformative potential for materials research, with significant implications for industries including battery technology, pharmaceuticals, and electronics.
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Argonne National Laboratory is currently in the midst of developing Aurora, a groundbreaking exascale computing system within the United States. This initiative encompasses fifteen research teams participating in the Aurora Early Science Program under the Argonne Leadership Computing Facility, a user facility of the DOE Office of Science. These researchers will be the first to leverage the supercomputer for scientific exploration.
The power of exascale supercomputers combined with advances in aritificial intelligence will provide a huge boost to the process of materials design and discovery.
Anouar Benali, Computational Scientist at Argonne and Project Leader
The introduction of exascale supercomputing represents a significant stride in materials design and exploration, with Aurora able to predict material behavior faster than any conventional technology.
Anouar Benali, a computational scientist, is spearheading the project to align a chemistry and materials science code - QMCPACK - with the capabilities of Aurora. This involves fine-tuning the QMCPACK code to meet the exacting demands of materials research. Benali's team is tackling the computational challenges alongside industry leaders Intel and Hewlett Packard Enterprise.
At its core, the open-source code QMCPACK employs the Quantum Monte Carlo (QMC) method to unravel material properties, aided by the Schrödinger equation.
The QMCPACK code excels in solving the Schrödinger equation, which is necessary in order to understand the behavior of atoms and electrons within materials. The interactions between atoms and electrons fundamentally shape the material's structure and its ensuing properties. Analyzing these behaviors becomes difficult in more complex systems, however, which is why QMCPACK's abilities are so groundbreaking.
“With each new generation of supercomputer, we are able to improve QMCPACK’s speed and accuracy in predicting the properties of larger and more complex materials,” Benali states. “Exascale systems will allow us to model the behavior of materials at a level of accuracy that could even go beyond what experimentalists can measure.”
This collaboration has far-reaching consequences within the materials science and technology landscape. At the outset, Benali aimed to utilize QMCPACK with Aurora to identify high-performance materials for microchip transistors. However, it has become clear that QMCPACK could further impact sectors such as electric vehicles, pharmaceuticals, and renewable energy.
The Future
The intersection of exascale computing, artificial intelligence, and the QMCPACK code marks a potential turning point in materials research. This collaboration offers opportunities ranging from precise predictions of material properties to advancements in technologies like advanced batteries and catalytic processes.
The team is also hopeful that this technology could be leveraged for the discovery of new materials.
“With the boost we’re getting from exascale machines and our software, we’re now at a point where we can work together with AI and machine learning specialists to reverse engineer material design instead of trying everything at the simulation level,” said Benali.
“If we know which properties we need for a particular application, we can use AI to scan for promising materials and tell us which ones to investigate further. This approach has the potential to revolutionize computer-aided materials discovery.”
Source:
Argonne National Laboratory (no date) Argonne’s Aurora supercomputer set to supercharge materials discovery. Available at: https://www.anl.gov/article/argonnes-aurora-supercomputer-set-to-supercharge-materials-discovery (Accessed: 17 October 2023).