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Caroline Bishop Nov 19, 2024 00:32 NVIDIA introduces ALCHEMI to revolutionize AI-driven material discovery, aiming to accelerate R&D with machine learning interatomic potentials and high-throughput simulations. In a significant leap forward for material science, NVIDIA has unveiled its AI Lab for Chemistry and Materials Innovation, known as ALCHEMI, to expedite the discovery of new materials through artificial intelligence. This initiative is set to transform the traditional material discovery process, which often takes decades, into a streamlined operation achievable in mere months, according to NVIDIA. AI-Accelerated Workflow The AI-driven workflow for material discovery is structured into four key stages: hypothesis generation, solution space definition, property prediction, and experimental validation. Each stage is designed to leverage AI to maximize efficiency and precision in discovering novel materials. During hypothesis generation, large language models (LLMs) trained on chemical literature assist scientists in synthesizing insights and formulating hypotheses. The solution space definition stage employs generative AI to explore new chemical structures, while property prediction uses machine learning interatomic potentials (MLIPs) and density functional theory (DFT) simulations to validate properties. Finally, the experimental validation phase utilizes AI to recommend candidates for lab testing, optimizing the balance between known chemistry and unexplored potential. Revolutionary Tools and Techniques NVIDIA’s ALCHEMI provides APIs and microservices to support developers in deploying generative AI models and AI surrogate models. These tools are crucial for efficiently mapping material properties and conducting simulations, which are vital for high-throughput screening and innovation. ALCHEMI introduces machine learning interatomic potentials (MLIPs) that provide a cost-effective and accurate method for predicting material properties. This technique has diverse applications across chemistry, material science, and biology, enabling large-scale simulations that were previously impractical due to high computational costs. Impact on Research and Development The NVIDIA Batched Geometry Relaxation NIM (NVIDIA Inference Microservice) significantly accelerates geometry relaxation processes, showcasing a…

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Filed under: News - @ November 19, 2024 4:23 am