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The displacement field associated with the freezing of a melt and its role in determining crystal growth kinetics.

Gang SunAlexander HawkenPeter Harrowell
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
The atomic displacements associated with the freezing of metals and salts are calculated by treating crystal growth as an assignment problem through the use of an optimal transport algorithm. Converting these displacements into timescales based on the dynamics of the bulk liquid, we show that we can predict the activation energy for crystal growth rates, including activation energies significantly smaller than those for atomic diffusion in the liquid. The exception to this success, pure metals that freeze into face-centered cubic crystals with little to no activation energy, are discussed. The atomic displacements generated by the assignment algorithm allows us to quantify the key roles of crystal structure and liquid caging length in determining the temperature dependence of crystal growth kinetics.
Keyphrases
  • ionic liquid
  • crystal structure
  • machine learning
  • deep learning
  • health risk
  • health risk assessment
  • density functional theory
  • neural network
  • aqueous solution