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Simulating iron in oxygen-containing environments: An improved Fe-O interaction for density-functional tight-binding.

Ville Korpelin1, Janne Nevalaita2, Marko M Melander1

  • 1Nanoscience Center, Department of Chemistry, University of Jyväskylä, Jyväskylän yliopisto 40014, Finland.

The Journal of Chemical Physics
|June 11, 2025
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Summary
This summary is machine-generated.

This study improves the accuracy of density-functional tight-binding (DFTB) simulations for iron-oxygen interactions. The enhanced parameterization enables more reliable large-scale modeling of iron chemistry in aqueous environments.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Surface Science

Background:

  • Iron's interaction with oxygen-containing species is crucial for corrosion, catalysis, and biological processes.
  • First-principles density-functional theory (DFT) accurately models these interactions but is computationally expensive.
  • Second-principles density-functional tight-binding (DFTB) offers a faster alternative but requires accurate parameterization.

Purpose of the Study:

  • To address limitations in current DFTB parameterizations concerning Fe-O pairwise repulsion.
  • To develop and validate an improved Fe-O repulsion model for DFTB.
  • To demonstrate the enhanced DFTB parameterization's utility in simulating iron dynamics in aqueous environments.

Main Methods:

  • Developed a new Fe-O repulsion parameterization for DFTB by fitting to relevant structures.
  • Benchmarked the improved DFTB parameterization against DFT calculations for Fe-water and Fe-oxygenated species interactions.
  • Simulated the dynamics of atomic Fe and FeN4-modified graphene in aqueous solutions using the enhanced DFTB method.

Main Results:

  • The improved Fe-O repulsion parameterization significantly enhances DFTB accuracy for iron-oxygen interactions.
  • Validated simulations show good agreement with DFT benchmarks for various Fe-containing systems.
  • The new parameterization successfully captures the dynamics of Fe and Fe-graphene systems in water.

Conclusions:

  • The developed DFTB parameterization provides a computationally efficient and accurate method for studying iron chemistry in wet environments.
  • This advancement facilitates large-scale simulations of catalytically relevant processes involving iron.
  • The improved model opens new avenues for research in corrosion, electrocatalysis, and bioinorganic chemistry.