Researchers at Argonne National Laboratory are preparing to use one of the world’s first exascale systems, Aurora, to accelerate the design and discovery of new materials for various applications.
What is Aurora and why is it important?
Aurora is an exascale supercomputer that is being built by Argonne National Laboratory in collaboration with Intel and Hewlett Packard Enterprise. Exascale systems are capable of performing a quintillion (10^18) calculations per second, which is about 50 times faster than the current fastest supercomputers. Aurora is expected to be one of the first exascale systems in the world when it is deployed for science in 2023.
Aurora will provide researchers with unprecedented computing power and artificial intelligence (AI) capabilities to tackle some of the most challenging scientific problems, such as understanding the origin of the universe, developing clean energy sources, and finding new treatments for diseases.
One of the key areas that Aurora will enable breakthroughs in is materials science, which is the study of how the structure and properties of materials affect their behavior and performance. Materials science is essential for developing new technologies that can improve the quality of life, such as batteries, catalysts, solar cells, and medicines.
How will Aurora help materials discovery?
To design and discover new materials with desired properties, researchers need to understand how atoms and electrons interact with each other in different environments. However, this is a very complex and computationally intensive task that requires accurate and reliable methods to simulate and predict the behavior of materials at different scales.
One of the methods that researchers use to study materials is called Quantum Monte Carlo (QMC), which is a statistical technique that uses random sampling to calculate the quantum mechanical properties of materials, such as their energy, magnetism, and conductivity. QMC is considered one of the most accurate methods for predicting materials properties, but it also requires a lot of computational resources and time.
To overcome this challenge, researchers at Argonne and other national laboratories have developed an open-source code called QMCPACK, which implements QMC on high-performance computing systems. QMCPACK has been used to study a wide variety of materials, such as metals, oxides, organic molecules, and nanomaterials.
However, QMCPACK also needs to be adapted and optimized for the new architecture and scale of exascale systems like Aurora. This is why researchers are participating in the Aurora Early Science Program (ESP), which is a program that provides access to pre-production time on Aurora to prepare codes and applications for the exascale era.
What are the goals and benefits of the ESP project?
The ESP project led by Anouar Benali, a computational scientist at Argonne, aims to prepare QMCPACK for Aurora and use it to model the behavior of materials at an unprecedented level of detail. The project complements a broader effort supported by the Department of Energy’s Exascale Computing Project (ECP), which is dedicated to preparing codes and applications for the nation’s larger exascale ecosystem.
The project has several goals and benefits, such as:
- Improving QMCPACK’s speed and accuracy in predicting the properties of larger and more complex materials
- Enabling QMCPACK to leverage Aurora’s AI capabilities to enhance the efficiency and reliability of QMC simulations
- Developing new algorithms and workflows to integrate QMCPACK with other codes and data sources
- Providing a user-friendly interface and documentation for QMCPACK
- Applying QMCPACK to study important materials problems, such as improving battery performance, designing new catalysts, and discovering novel quantum materials
By achieving these goals, the project will not only advance the state-of-the-art in materials science and chemistry, but also provide a valuable tool for the scientific community to use Aurora for materials discovery.
“The power of exascale supercomputers combined with advances in artificial intelligence will provide a huge boost to the process of materials design and discovery,” Benali said. “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.”
