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techniques. Developed models will be used to optimize processing and conventional alloy compositions for additive manufacturing. T. Keller, G. Lindwall, S. Ghosh, L. Ma, B.M. Lane, F. Zhang, U.R. Kattner
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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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of novel optical methods for nanoscale dimensional measurements using the NIST 193 nm Microscope: a newly upgraded, custom-built, world-class high-magnification optical imaging platform optimized
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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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. "Integration of housing and social vulnerability into an optimization model for community resilience planning", Lifelines 2021-22 Conference, University of California, Los Angeles, Jan. 31 – Feb. 11, 2022. 2
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RAP opportunity at National Institute of Standards and Technology NIST Designing Liquid Scintillators for Optimal Light Yield, Pulse Shape Discrimination, and Neutron Sensitivity
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technologies to manipulate biological macromolecules such as DNA, and the controlled degradation of tissue engineering scaffold or drug delivery materials. To optimize performance and to design new applications
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learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
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experimental data archives available at NIST/TRC and advanced multidimensional optimization techniques to generate self-consistent, predictive force-filed models applicable over wide range of conditions. key
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aptamer conformation changes. Method development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability, and