NIST · U.S. · NIST AI
Namely, thermodynamic properties of these systems which play a significant role in applications ranging from medicine
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Key facts
- NIST provided a $500,000 Advanced Manufacturing Technology Consortia (AMTech) Program Planning Award to initiate a collaborate effort with the American Chemical Society Green Chemistry Institute (ACS
- R., "Regional, temporal, and species patterns of mercury in Alaskan seabird eggs: Mercury sources and cycling or food web effects?," Environmental Pollution, 166, 226-232 (2012)
- K., "Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods," Journal of Chemical Physics, 146, (2017)
- K., " Multivariable extrapolation of grand canonical free energy landscapes," Journal of Chemical Physics, 147, (2017)
Summary
Secure.gov websites use HTTPS A lock or https:// means you’ve safely connected to the.gov website. By using machine learning approaches, they seek to elucidate underlying universal characteristics of fluids and fluid mixtures that enable property prediction. Understanding the thermodynamic properties of fluids and fluid mixtures is of central importance in many fields of science and engineering ranging from medicine to consumer products. This approach can also be extended to the study of confined fluids, a major area of importance in chemical separations and industrial manufacturing, and one which is critically needed to meet the “Energy Intensity of Chemical Processing” Grand Challenge laid out the National Research Council.