Workshop for AI-Powered Materials Discovery at Great Plains

Molecular Dynamics Simulations of Phase Transformations in AgI with Machine-Learned Interatomic Potentials

Jun 23, 2025, 2:33 PM

28m

Room 216 MUC

Future Directions in AI for Particle Physics, Nuclear Physics, and Materials Science Parallel Session C: AI-Enhanced Materials Design, Simulation, and Discovery

Speaker

Steven Baksa (Northwestern University)

Description

Silver iodide is extensively studied for its ionic conductivity, in addition to its thermally-driven polymorphism into several phases including wurtzite, zincblende, rocksalt, and body-centered cubic [1,2]. The exact mechanisms and driving forces behind these transformations, however, are not well understood. Recently, molecular dynamics simulations (MD) informed by machine-learned interatomic potentials (MLIP) have been a promising tool to provide an atomistic picture of mechanically- and thermally-driven transformations [3]. To that end, we report progress in MD-MLIP simulations of the dominant phases in the pressure-temperature window of interest (1-5 kbar, 200-500K) to provide insight on the atomistic mechanisms for the polymorphism with implications for other drivers for such transformations (i.e. mechanochemistry).

References

[1] S. De Panfilis, A. Di Cicco, A. Filipponi, & M. Minicucci. Solid and liquid AgI at high pressure and high temperature: A X-ray absorption spectroscopy study. High Press. Res. 22, 349 (2010).

[2] O. Ohtaka, H. Takebe, A. Yoshiasa, H. Fukui, & Y. Katayama. Phase relations of AgI under high pressure and high temperature. Solid State Commun. 123, 213 (2002).

[3] H. Zong, G. Pilania, X. Ding, G. J. Ackland, & T. Lookman. Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning. npj Comput. Mater. 4, 48 (2018).