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metabolites in situ. The first goal of the project will be to characterize the volatile organic chemical fingerprints of various bacteria using PTR-MS. We will then optimize a rapid, noninvasive workflow for
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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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, physical, optical, and thermal properties of WBG semiconductors, including diamond, make these materials among the most prospective for high-frequency power electronics, quantum computing, solar-blind