39 lines
4.2 KiB
JSON
39 lines
4.2 KiB
JSON
{
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"HubID": "4981",
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"Date": "11/25/2024",
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"HubTags": [
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"artificial intelligence",
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"External Platform Posts",
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"Future Map Forward Guidance",
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"Future Map"
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],
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"Contacts": [
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"c4965SueBrown",
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"c5822DougSchnitzspahn",
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"c6254Colin_TRUE",
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"c6355JulieBrown",
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"c6392AnnieAgle",
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"c6400KentEbersole",
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"c6403BellaWebb",
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"c6448MegCarney",
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"c6461ScottHolley",
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"c6476NIckSargent",
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"c6477JeffMoag",
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"c6482Ana_Kristiansson",
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"c6504Kaydi_Pyette",
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"c6509CampbellMyers",
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"c6525Dan_Steinhart",
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"c6526StephenMcBride",
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"c6535MoeHachem"
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],
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"Companies": "",
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"File": "",
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"Image": "",
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"Summary": "<p>Here’s some Monday motivation for anyone remotely involved in product R&D and sustainable materials:</p><p>🌟 Imagine an explosion of new lab-created materials for durable goods and bio-grown ingredients for consumables and body care. Thanks to tools like Google GNoME and Meta OMat24 (both open-source), powered by vast datasets and AI, hundreds of thousands of synthetic materials and ingredients have already moved to experimental synthesis. Thousands could be ready for use in real-world products.</p><p>🔬 Democratizing R&D. Picture an all-in-one lab box for material discovery, accessible to companies of any size. Alan Aspuru-Guzik's Matter Lab is leading the way with a modular system that combines advanced software, synthesis, and testing robots. Enter a material description, and the system instantly simulates and tests molecules. This puts the power of cutting-edge material science into anyone's hands.</p><p>🌍 A template for transforming industries. Companies like Halcyon are taking AI-powered innovation even further. They’re building platforms to harness vast seas of data, helping energy transition practitioners accelerate the move toward decarbonization. Could their platform could be a blueprint that can transform industries far beyond energy?</p><p>🤖 Leveraging AI for Breakthroughs: I’ve started tracking thousands of innovations weekly—an overwhelming task without AI. By pairing AI with my process, I am surfacing breakthroughs that matter most for my work.</p><p>We’re entering an age where material science, R&D, and product development are becoming accessible to everyone—regardless of company size or expertise. This shift democratizes innovation and levels the playing field.<br /></p><p>Here's more:</p><p>NVIDIA's ALCHEMI platform integrates AI with high-performance computing to expedite materials discovery. It offers tools for generating synthetic datasets to train AI models and introduces accelerated materials discovery modules, such as machine learning interatomic potentials (MLIPs), to enhance the efficiency of materials research. </p><p>AlphaMat: Developed as a material informatics hub, AlphaMat connects data, features, models, and applications. It is designed to handle datasets of varying scales and can design structural and component descriptors effective for various AI models. AlphaMat has been instrumental in discovering thousands of new materials across photonics, batteries, catalysts, and capacitors. </p><p>NOMAD AI Toolkit: The Novel-Materials-Discovery (NOMAD) Artificial-Intelligence Toolkit is a web-based infrastructure for interactive AI-based analysis of materials science data. It operates on FAIR (Findable, Accessible, Interoperable, and Reusable) data stored in the NOMAD Archive, facilitating data mining, machine learning, and other AI techniques to analyze materials data. </p><p>Microsoft Azure Quantum Elements: Microsoft's Azure Quantum Elements platform combines AI with traditional high-performance computing and quantum tools for materials science, chemistry, and pharmaceutical research. It utilizes physics-based AI models and advanced algorithms to process complex research data, aiming to accelerate the discovery of new materials. </p><p><span></span>IBM's Materials Discovery: IBM Research is leveraging AI, robotics, hybrid cloud, and quantum computing to accelerate the discovery of new materials. Their goal is to unlock new properties and materials to address global challenges in a significantly reduced timeframe. <span></span></p>",
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"Notes": ""
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}
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