How AI can Speed Up Battery Material Discovery

I am pleased to announce the publication of a groundbreaking, peer-reviewed research paper (https://lnkd.in/eY6MJ6Ac in the prestigious Journal of the American Chemical Society). The paper is entitled, “Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation,” which was shared previously on arXiv at https://lnkd.in/gRSPfcEZ. The paper provides details on the digital tools that enabled the rapid discovery of a new battery electrolyte through a collaboration between Microsoft and the Pacific Northwest National Laboratory (PNNL). In January of this year, Microsoft announced (https://lnkd.in/ejHztbZv) that the Azure Quantum team had used AI and high-performance computing (HPC) to screen over 32 million compounds and, in collaboration with PNNL, identified one promising candidate for use as a new battery electrolyte. The top candidate, which was identified with the Azure Quantum Elements platform and experimentally validated by PNNL, contains approximately 70% less lithium than existing lithium-ion batteries. This newly discovered battery electrolyte could lead to the development of novel energy-storage solutions that are more sustainable than existing batteries.  Notably, the entire discovery process involving candidate generation, AI screening, HPC simulations, and laboratory testing took only nine months. This rapid pace of scientific research and the discovery of a promising battery electrolyte were made possible by the tools available in Azure Quantum Elements, demonstrating the power of Microsoft’s AI models and HPC simulations to accelerate time to results.  I congratulate the entire Azure Quantum team and PNNL for the collaborative efforts in this endeavor, which not only showcased the ability of Azure Quantum Elements to accelerate scientific discovery, but also produced a tangible result that may have applications in the energy industry as well as in the field of sustainability.  I welcome other organizations to join us on this exciting journey so that groundbreaking discoveries can be made in additional fields at an unprecedented pace.   #solidelectrolytes #AIformaterials #energystorage #cleanenergy #Microsoft #PNNL #AI4Science #AzureQuantum #Azure

Innovating Beyond Lithium: AI's Role in Pioneering Next-Gen Batteries As the world grows increasingly reliant on lithium for everything from mobile phones to electric vehicles, the challenges associated with its supply and environmental impact are becoming more apparent. Addressing this, Microsoft and Pacific Northwest National Laboratory (PNNL) are leading a project that could change the future of battery technology. 𝐖𝐡𝐲 𝐋𝐞𝐬𝐬 𝐋𝐢𝐭𝐡𝐢𝐮𝐦? Lithium, while efficient and powerful, poses significant geopolitical, economic, and environmental challenges. Its extraction is energy-intensive, and reserves are concentrated in just a few countries, which could lead to supply disruptions. Moreover, the growing demand is pushing researchers to seek sustainable and less problematic alternatives. 𝐄𝐧𝐭𝐞𝐫 𝐀𝐈 𝐚𝐧𝐝 𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 Leveraging artificial intelligence (AI) and cloud-based high-performance computing (HPC), the teams at Microsoft and PNNL have joined forces to innovate battery technology. AI's capability to process and analyze vast datasets has enabled the identification of potential alternatives to lithium with speed and accuracy. The collaboration has led to a discovery - a battery that substitutes about half of the lithium atoms with sodium. This not only reduces reliance on lithium but also leverages sodium’s abundance and cost-effectiveness. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐭𝐡𝐚𝐭 𝐥𝐞𝐝 𝐭𝐨 𝐭𝐡𝐢𝐬 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: >> Massive Material Screening: Starting with a list of 32 million potential materials, AI helped narrow down the list to 18 promising candidates. >> Refined Criteria: Further refinement using more stringent screening criteria, recommended by material scientists at PNNL, pinpointed a viable candidate for testing. >> Experimental Success: The substitution approach was validated experimentally, marking a significant step towards a more sustainable battery technology. This initiative not only exemplifies the power of AI in accelerating material discovery but also highlights a sustainable pathway for battery manufacturing that could lessen environmental impact and reduce geopolitical tensions. 🤔 How do you see AI transforming other industries with similar resource challenges? What implications does this development have for the future of energy storage and electric vehicles? #innovation #technology #future #management #startups

AI is propelling sustainability by helping us discover new innovations faster. In collaboration with Pacific Northwest National Laboratory, Microsoft researchers used AI models to identify a new battery material that can help build high-capacity batteries. These AI models surfaced the most viable materials out of a pool of 32 million potential options. Without AI, this analysis would’ve taken years. With AI, it was the work of one long weekend. This collaboration with PNNL is a great example of how investing in AI for sustainability can unlock and accelerate the development of new game-changing capabilities. This is one of our five points in our new paper, Accelerating Sustainability with AI: Innovations for a Better Future. 🔽 https://lnkd.in/gjqgEFNw