{"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\/pt\/2024\/01\/microsoft-ai-discover-an-ultra-efficient-battery-electrolyte\/","title":{"rendered":"IA da Microsoft descobre um eletr\u00f3lito de bateria ultra-eficiente"},"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>A Microsoft AI e o Laborat\u00f3rio Nacional do Noroeste do Pac\u00edfico (PNNL) descobriram um novo eletr\u00f3lito s\u00f3lido que pode reduzir drasticamente a utiliza\u00e7\u00e3o de l\u00edtio nas baterias at\u00e9 70%. Este material inovador, informalmente designado por N2116, oferece uma solu\u00e7\u00e3o para as preocupa\u00e7\u00f5es ambientais associadas \u00e0 extra\u00e7\u00e3o de l\u00edtio. Prev\u00ea-se que o l\u00edtio, o principal componente de numerosas tecnologias de baterias, venha a sofrer uma escassez j\u00e1 em 2025, com um aumento de dez vezes na procura esperado at\u00e9 2030. A extra\u00e7\u00e3o de l\u00edtio tem tamb\u00e9m uma vasta pegada ambiental, envolvendo uma quantidade substancial de \u00e1gua e energia. O processo completo, desde o conceito at\u00e9 ao prot\u00f3tipo de bateria funcional, demorou menos de nove meses, o que, segundo as estimativas, pode ter<\/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\" 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