“Our collaboration with Microsoft is about making AI accessible to scientists. “The intersection of AI, cloud and high-performance computing, along with human scientists, we believe is key to accelerating the path to meaningful scientific results,” said Tony Peurrung, PNNL deputy director for Science and Technology. Using AQE, the researchers were able to do this in 18 months. Typically, it would take years to go through this process and to build a prototype battery. After that, the researchers used existing HPC techniques to identify those 18 promising candidates to focus on. First, the teams used AQE’s AI models to whittle down the pool to about 500,000 candidates. Using AQE, the researchers at PNNL looked at 32 million inorganic materials to arrive at 18 candidates for their battery project. Krysta Svore, who leads Microsoft Quantum, told me that the overall idea here was to see how far the team could push what is currently available in Azure Quantum Elements (AQE) - and especially the AI accelerator - to advance materials discovery.
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