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Exploring Librational Pathways with on-the-Fly Machine-Learning Force Fields: Methylammonium Molecules in MAPbX3 (X = I, Br, Cl) Perovskites.

Menno BokdamJonathan LahnsteinerD D Sarma
Published in: The journal of physical chemistry. C, Nanomaterials and interfaces (2021)
Two seemingly similar crystal structures of the low-temperature (∼100 K) MAPbX3 (X = I, Br, Cl) perovskites, but with different relative methylammonium (MA) ordering, have appeared as representatives of this orthorhombic phase. Distinguishing them by X-ray diffraction experiments is difficult, and conventional first-principles-based molecular dynamics approaches are often too computationally intensive to be feasible. Therefore, to determine the thermodynamically stable structure, we use a recently introduced on-the-fly machine-learning force field method, which reduces the computation time from years to days. The molecules exhibit a large degree of anharmonic motion depending on temperature: that is, rattling, twisting, and tumbling. We observe the crystal's "librational pathways" while slowly heating it in isothermal-isobaric simulations. Marked differences in the thermal evolution of structural parameters allow us to determine the real structure of the system via a comparison with experimentally determined crystal structures.
Keyphrases
  • molecular dynamics
  • machine learning
  • density functional theory
  • single molecule
  • artificial intelligence
  • big data
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  • deep learning
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  • mass spectrometry