In the case of the new battery technology, Microsoft began with a data set of 32.6 million materials. Using Azure Quantum Elements HPC resources to run simulations and AI models, it reduced the field of contenders to 500,000, then 500, then 150, then 18.
In total, the battery project has been underway for about a year, from its inception through the fabrication of around 40 test batteries. (Along with powering the clock, they’ve also been used to light up LEDs in the colors of Microsoft’s corporate logomark: red, yellow, blue, and green.)
This innovative approach could revolutionize how we develop and enhance battery technologies. In their collaborative initiative, the Microsoft Quantum team harnessed the power of AI to "identify around 500,000 stable materials in the space of a few days," according to a statement by Microsoft.
Artificial intelligence (AI) and large-scale cloud computing is speeding up the search for new battery materials. An AI-enhanced collaboration between Microsoft and the Pacific Northwest National Laboratory (PNNL) has already produced one promising new material, which the two are sharing publicly today.
In their collaborative initiative, the Microsoft Quantum team harnessed the power of AI to "identify around 500,000 stable materials in the space of a few days," according to a statement by Microsoft. Creating next-generation batteries poses a crucial challenge, traditionally constrained by labor-intensive processes.
It has been corrected to say that the material can reduce the amount of lithium by as much as 70 percent. We regret the error. Microsoft and the Pacific Northwest National Laboratory used AI and high-performance computing to discover a promising new battery material faster than ever before.
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