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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Autonomous experimentation and machine learning of material properties Location Material
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization Location
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Methods for the Prediction and Correlation of Thermophysical Properties
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and
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polyethylene, linear low-density polyethylene and polypropylene. To overcome this challenge, we have previously shown that utilizing machine learning, real-time NIR measurements can be correlated to
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software packages) is encouraged to improve reproducibility and to increase usage by the community. [1] https://doi.org/10.1039/C9SM01877H [2] https://doi.org/10.1063/1.5123683 [3] https://doi.org
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increasing. The large number of data sources in combination with a wide variety of industrially important chemicals and associated properties, as well as the absence of commonly accepted machine-readable
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novel machine learning approaches capable of interpreting this information and integrating data from other “-omic” platforms such as genomics, transcriptomics, and proteomics. Leveraging artificial
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mechanism and the charge transport in thin dielectric films deposited on graphene using a non-contact microwave technique ( https://dx.doi.org/10.1021/acs.jpcb.9b11622 ) and monolayer graphene ( https
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Superalloys. J. Phase Equilib. Diffus. 43 , 931–952 (2022). https://doi.org/10.1007/s11669-022-01011-1 Keywords Calphad; Diffusion mechanisms; Multicomponent diffusion; Eligibility citizenship Open to