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–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning. Digital Discovery, 3(11), pp.2211-2225. Materials Genome Initiative; Machine learning
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sophisticated potential energy functions and adequate sampling to reveal the associated, intricate molecular details. In addition to being centrally important, high-quality experimental data (free energy
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fullerenes and compounds based on fullerenes. In addition, the diffusion of atomic and molecular species is studied in materials such as protonic and ionic conductors. The complementary techniques of infrared
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bacteria typically reproduce asexually, they have developed multiple methods to share genetic information via Horizontal Gene Transfer (HGT, also called Lateral Gene Transfer). These mechanisms operate
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301.975.4946 Description Predicting the bulk behavior of a material system based on its microstructure and the behavior of its constituents is an important aspect of materials design. Our current research
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with stable isotopes such as 2 H, 13 C, 15 N, and 18 O is an important tool for structural and bioanalytical methods such as nuclear magnetic resonance (NMR), small angle neutron scattering (SANS), and
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conditions. For that purpose, Raman spectroscopy-enhanced indentation technique (RS-IT) is being developed at NIST which combined instrumented indentation with Raman spectroscopy to analyze in-situ
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Anthony Herzing andrew.herzing@nist.gov 301.975.2860 Description Electron scattering is uniquely suited to the atomic-scale characterization of individual nanostructures because electrons have small (nm
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Kumar Banerjee dilip.banerjee@nist.gov 301.975.3538 Description The use of lightweight materials in vehicles will significantly increase fuel efficiency and cut emissions, but the auto industry lacks data
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Advanced Microwave Microfluidics for Real-time Diagnostics and Chemical Analysis NIST only participates in the February and August reviews. Healthcare is a driving force in the global economy. In