2023-Sustainable Industrial Processing Summit
SIPS2023 Volume 9. Navrotsky Intl. Symp. / Geochemistry

Editors:F. Kongoli, M. Asta, C. Drouet, S.Hayun, R. Riedel, W. Sun
Publisher:Flogen Star OUTREACH
Publication Year:2023
Pages:142 pages
ISBN:978-1-989820-88-9 (CD)
ISSN:2291-1227 (Metals and Materials Processing in a Clean Environment Series)
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    HIGH TEMPERATURE THERMOCHEMISTRY FROM EXPERIMENT, AB INITIO, AND MACHINE LEARNING

    Sergey Ushakov1; Qijun Hong1; Alexandra Navrotsky2;
    1ARIZONA STATE UNIVERSITY, Tempe, United States; 2ARIZONA STATE UNIVERSITY, Phoenix, United States;
    Type of Paper: Regular
    Id Paper: 14
    Topic: 67

    Abstract:

    The measurements, computations, and predictions of high temperature thermodynamic properties are of interest to geoscience, material science, and engineering. The experimental techniques to provide structural and thermodynamic data above 1500 °C were developed in Navrotsky’s group for over 10 years. This resulted in the first demonstration of crystal structure refinements on laser-heated aerodynamically levitated samples using synchrotron X-ray and neutron diffraction and drop calorimetry measurements with splittable nozzle aerodynamic levitator [1]. High temperature diffraction provides experimental data on thermal expansion, atomic displacement parameters, and volume change in phase transformations. Drop calorimetry on levitated samples provides enthalpy of fusion. These data can also be obtained from ab initio molecular dynamic computations. The experimentally benchmarked computations can provide reliable data on high temperature heat capacities [2].
    Melting or decomposition temperature is a widely used thermodynamic property. Experimental measurements and ab initio computations require time, resources, and expertise. The machine learning model has been developed and trained on ~10,000 experimental and ab initio values of melting points for congruently melting compounds. It has been applied to predict melting or decomposition temperatures of ~5,000 known mineral species which revealed new correlations with the time of Late Heavy Bombardment event and structural complexity index [3]. The model is publicly accessible via the web interface on Hong’s group website for the prediction of melting temperatures within seconds [4].

    Keywords:

    geochemistry; inorganic solids; Thermodynamics; crystalline; functional materials; machine learning

    References:

    [1] S. V. Ushakov, P. S. Maram, D. Kapush, A. J. Pavlik, III, M. Fyhrie, L. C. Gallington, C. J. Benmore, R. Weber, J. C. Neuefeind, J. W. McMurray, A. Navrotsky, Adv. Appl. Ceram. 117, s82-s89 (2018) https://doi.org/10.1080/17436753.2018.1516267.
    [2] Q.-J. Hong, A. van de Walle, S. V. Ushakov, A. Navrotsky, Calphad 79, 102500 (2022) https:/doi.org/10.1016/j.calphad.2022.102500.
    [3] Q.-J. Hong, S. V. Ushakov, A. van de Walle, A. Navrotsky, PNAS 119, e2209630119 (2022) https://doi.org/10.1073/pnas.2209630119.
    [4] https://faculty.engineering.asu.edu/hong/melting-temperature-predictor/

    Cite this article as:

    Ushakov S, Hong Q, Navrotsky A. (2023). HIGH TEMPERATURE THERMOCHEMISTRY FROM EXPERIMENT, AB INITIO, AND MACHINE LEARNING. In F. Kongoli, M. Asta, C. Drouet, S.Hayun, R. Riedel, W. Sun (Eds.), Sustainable Industrial Processing Summit Volume 9 Navrotsky Intl. Symp. / Geochemistry (pp. 81-82). Montreal, Canada: FLOGEN Star Outreach