{"id":9043,"date":"2024-01-09T18:56:59","date_gmt":"2024-01-09T18:56:59","guid":{"rendered":"https:\/\/dailyai.com\/?p=9043"},"modified":"2024-01-10T16:24:31","modified_gmt":"2024-01-10T16:24:31","slug":"microsoft-ai-discover-an-ultra-efficient-battery-electrolyte","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/01\/microsoft-ai-discover-an-ultra-efficient-battery-electrolyte\/","title":{"rendered":"Microsoft AI opdager en ultra-effektiv batteri-elektrolyt"},"content":{"rendered":"<p><b>Microsoft AI and the Pacific Northwest National Laboratory (PNNL) unearthed a new solid electrolyte that could drastically reduce lithium usage in batteries by up to 70%. <\/b><\/p>\n<p><span style=\"font-weight: 400;\">This breakthrough material, informally named N2116, offers a solution to the environmental concerns associated with lithium mining.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Lithium, the primary component in numerous battery technologies, is projected to see shortages as <a href=\"https:\/\/www.statista.com\/topics\/2049\/lithium-ion-battery-industry\/#topicOverview\" target=\"_blank\" rel=\"noopener\">early as 2025<\/a>, with a tenfold increase in demand expected by 2030.\u00a0<\/span><span style=\"font-weight: 400;\">Lithium mining also has a vast environmental footprint, involving substantial water and energy.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The end-to-end process, from concept to working battery prototype, took less than nine months, which they estimate might have traditionally taken some two decades.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Microsoft&#8217;s supercomputers accelerated the process, sifting through 32 million potential inorganic materials and narrowing them down to 18 candidates in under a week. This follows a <\/span><a href=\"https:\/\/dailyai.com\/2023\/11\/deepminds-gnome-system-discovered-millions-of-new-materials\/\"><span style=\"font-weight: 400;\">similar breakthrough by Google DeepMind<\/span><\/a><span style=\"font-weight: 400;\">, which created an autonomous research lab that discovered some 2 million new materials.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Jason Zander, Executive Vice President of Microsoft, described AI\u2019s role, stating <\/span><a href=\"https:\/\/www.bbc.co.uk\/news\/technology-67912033\"><span style=\"font-weight: 400;\">to the BBC<\/span><\/a><span style=\"font-weight: 400;\">, &#8220;This is the way that this type of science I think is going to get done in the future.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The new solid-state electrolyte, N2116, represents a more sustainable and safer alternative to traditional liquid or gel-like lithium batteries.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Solid-state batteries promise faster charging and greater energy density with extended charge cycles. By including sodium, an element more abundant and less expensive than lithium, N2116 reduces lithium requirements while maintaining efficient energy storage and transfer.\u00a0<\/span><\/p>\n<p><iframe loading=\"lazy\" title=\"Microsoft Azure Quantum and PNNL join forces to accelerate scientific discovery\" width=\"1080\" height=\"608\" src=\"https:\/\/www.youtube.com\/embed\/oWaLTXTSR_A?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><span style=\"font-weight: 400;\">Karl Mueller from PNNL highlighted the role of AI in the discovery, stating, &#8220;[We could] modify, test and tune the chemical composition of this new material and quickly evaluate its technical viability for a working battery, showing the promise of advanced AI to accelerate the innovation cycle.&#8221;<\/span><\/p>\n<h2>Harnessing AI for material discovery<\/h2>\n<p><span style=\"font-weight: 400;\">Microsoft and Pacific Northwest National Laboratory (PNNL)\u2019s <\/span><a href=\"https:\/\/news.microsoft.com\/source\/features\/sustainability\/how-ai-and-hpc-are-speeding-up-scientific-discovery\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">research<\/span><\/a><span style=\"font-weight: 400;\"> involved combining AI with high-performance computing (HPC).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here&#8217;s a breakdown of how this process worked:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Identifying potential materials<\/b><span style=\"font-weight: 400;\">: The Microsoft Quantum team used AI to analyze an extensive inorganic materials database. From this, they initially identified around 500,000 stable materials in just a few days.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Narrowing down candidates<\/b><span style=\"font-weight: 400;\">: Utilizing Microsoft&#8217;s Azure Quantum Elements, the team further refined their search from these 500,000 materials to 18 promising candidates for battery development. This process was completed in just 80 hours, demonstrating the remarkable speed at which AI can operate.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Combining AI with HPC<\/b><span style=\"font-weight: 400;\"> The AI tools were trained to evaluate various chemical elements and their combinations. They proposed a massive pool of 32 million candidates, which were then filtered through different AI tools based on stability, reactivity, and energy conduction potential.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>HPC for verification<\/b><span style=\"font-weight: 400;\">: The next phase involved using HPC for further verification. This included using density functional theory to calculate the energy of each material and molecular dynamics simulations to analyze the movements of atoms and molecules within the materials.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Final selection of candidates<\/b><span style=\"font-weight: 400;\">: After this intense computational process, the list was narrowed down to 150 candidates. Further evaluation of practical aspects like availability and cost reduced this number to 23, out of which five were already known.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Prototype development<\/b><span style=\"font-weight: 400;\">: The final step involved PNNL scientists synthesizing the chosen material and developing it into a working prototype battery. This stage is crucial to test the material&#8217;s functionality and viability.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">AI\u2019s ability to work with vast quantities of complex data and synthesize new understandings from the ground up has proven immensely effective.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, in addition to materials, AI is also accelerating the discovery of new therapeutically significant molecules for <a href=\"https:\/\/dailyai.com\/2023\/07\/first-ai-generated-drug-given-go-ahead-for-human-trials\/\">antibiotic and drug development<\/a>.\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Microsoft AI og Pacific Northwest National Laboratory (PNNL) har fundet en ny fast elektrolyt, som drastisk kan reducere brugen af litium i batterier med op til 70%. Dette banebrydende materiale, der uformelt kaldes N2116, tilbyder en l\u00f8sning p\u00e5 de milj\u00f8m\u00e6ssige bekymringer, der er forbundet med litiumminedrift. Litium, som er den prim\u00e6re komponent i mange batteriteknologier, forventes at blive en mangelvare allerede i 2025, og der forventes en tidobling af eftersp\u00f8rgslen inden 2030. Litiumminedrift har ogs\u00e5 et stort milj\u00f8m\u00e6ssigt fodaftryk, der involverer meget vand og energi. Hele processen, fra koncept til fungerende batteriprototype, tog mindre end ni m\u00e5neder, hvilket de ansl\u00e5r kan have<\/p>","protected":false},"author":2,"featured_media":9044,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105,121],"class_list":["post-9043","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning","tag-microsoft"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Microsoft AI discover an ultra-efficient battery electrolyte | DailyAI<\/title>\n<meta name=\"description\" content=\"Microsoft AI and the Pacific Northwest National Laboratory (PNNL) unearthed a new solid electrolyte that could drastically reduce lithium usage in batteries by up to 70%.\" \/>\n<meta name=\"robots\" content=\"index, follow, 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