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Adventures in DFTB: Toward an Automatic Parameterization Scheme.

Glen R JennessCaitlin G BresnahanManoj K Shukla
Published in: Journal of chemical theory and computation (2020)
As we push forward on understanding the fate of chemicals in the environment, we need a method that will allow for the simulation of the inherent heterogeneity. Density functional tight binding (DFTB) is a methodology that allows for a detailed electronic description and would be ideal for this problem. While many parameters can be derived directly from DFT, empirical parameters still exist in the confinement and repulsion potentials. In this manuscript, we examine these potentials and present solutions that will minimize the degree of empiricism. Our results show that it is possible to construct confinement potentials from examining the atomic radial wavefunctions. Moreover, we found that the heterogeneous repulsion potentials can be derived from using only homogeneous repulsion curves.
Keyphrases
  • blood brain barrier
  • deep learning
  • molecular docking
  • dna binding