Mapping the genome of intermetallic clathrates
ORAL · Invited
Abstract
In this work, we have developed a high-throughput approach that combines electronic structure theory and machine learning to systematically compute the formation energy of several thousand compositions and identify new stable ternary and quaternary compounds.
Several proposed compositions have been tested by synthesis providing feedback to the calculations and leading to the discovery of new intermetallic host/guest compounds with either clathrate-like or competing structures. Single-phase synthesis and first-principles calculations enabled the joint theoretical and experimental characterization of their transport properties, revealing promising thermoelectric performance, other-disorder phase transitions, and extremely low thermal conductivity.
*This research was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Science and Engineering (Grant No. DE-SC0022288).
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Publication: [1] P. Yox, F. Cerasoli, A. Sarkar, V. Kyveryga, G. Viswanathan, D. Donadio, and K. Kovnir, New Trick for an Old Dog: From Prediction to Properties of "Hidden Clathrates" Ba2Zn5As6 and Ba2Zn5Sb6, J. Am. Chem. Soc. 145, 4638 (2023).
[2] P. Yox, F. T. Cerasoli, A. Sarkar, G. Amobi, G. Viswanathan, J. Voyles, O. l. Lebedev, D. Donadio, and K. Kovnir, Organizing Chaos: Boosting Thermoelectric Properties by Ordering the Clathrate Framework of Ba8Cu16As30, Chem. Mater. (2024).
[3] A. Sarkar, F. T. Cerasoli, G. Viswanathan, D. Donadio, and K. Kovnir, ABa6Cu31Te22 (A = K, Rb, Cs) Featuring Polyanionic Copper–Telluride Frameworks with Ultralow Thermal Conductivity, ACS Appl. Mater. Interfaces 16, 39613 (2024).
[4] F. T. Cerasoli and D. Donadio, Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning, J. Chem. Phys. 161, 044101 (2024).
Presenters
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Davide Donadio
- University of California, Davis
- University of California Davis