{"id":995577,"date":"2024-01-05T08:07:40","date_gmt":"2024-01-05T16:07:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=995577"},"modified":"2025-08-08T10:48:04","modified_gmt":"2025-08-08T17:48:04","slug":"afmr-scientific-discovery-and-innovation","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/afmr-scientific-discovery-and-innovation\/","title":{"rendered":"AFMR: Scientific Discovery and Innovation"},"content":{"rendered":"
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Scientific Discovery and Innovation<\/h1>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n
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Academic research plays such an important role in advancing science, technology, culture, and society. This grant program helps ensure this community has access to the latest and leading AI models.<\/em><\/strong><\/p>\nBrad Smith, Vice Chair and President<\/cite><\/blockquote>\n\n\n\n

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AFMR Goal: Accelerate scientific discovery in natural sciences<\/h2>\n\n\n\n

via proactive knowledge discovery, hypothesis generation, and multiscale multimodal data generation<\/p>\n<\/div>\n\n\n\n

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These projects focus on using foundation models to enhance knowledge discovery and hypothesis generation across many different areas. They particularly leverage the ability of general models to make sense of the exponentially growing volume of scientific literature in astronomy, materials science, and neuroscience. These efforts include exploring domain-specific prompt engineering and specializing foundation models through fine-tuning using techniques such as Low-Rank Adaption (LoRA). A series of proposals are dedicated to biomedical and life sciences research and innovation, including specialized models for drug discovery, genomics, protein engineering, and rare diseases. These proposals underscore the potential of foundation models to accelerate scientific discovery and innovation across many fields and disciplines.<\/p>\n\n\n\n

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University of Texas at Arlington<\/strong>: Miao Yin (PI)<\/p>\n\n\n\n

Ion chromatography (IC) is a powerful analytical chemistry technique for selective, sensitive quantification of aqueous ions spanning applications from environmental monitoring to biopharma pipelines. However, intrinsic slow analysis times severely throttle sample throughput. This project intends to develop an artificial intelligence-based platform accelerating IC by leveraging immense datasets from vast historical runs coupled with large foundation models tailored to effectively encode complex interactive influences of system parameters spanning columns, eluents, and detectors on separation performance into predictive modeling engines on Microsoft Azure. Additionally, a special tuning algorithm with analytical chemistry specialists’ feedback will be developed to ensure the correct prediction of the large foundation IC model. Broader anticipated impacts are poised to revolutionize ion chromatography practices with AI across academic, manufacturing, and innovation areas while providing students at MSI with interdisciplinary research opportunities incorporating computer science and analytical chemistry.<\/p>\n\n\n\n\n\n

Georgia Institute of Technology<\/strong>: Yunan Luo (PI)<\/p>\n\n\n\n

This proposal aims to leverage foundation models, including large language models trained on natural language and protein sequences, to advance protein function prediction and optimization. Two key areas of focus are 1) protein function prediction – predicting the biological roles of natural proteins and 2) protein function optimization – predicting which sequence mutations are beneficial for enhancing the function of natural proteins.<\/p>\n\n\n\n

Related papers:<\/strong><\/p>\n\n\n\n