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Computational methods struggle with bromine crystals. Periodic DFT (PBE-D3BJ) accurately predicts stability, unlike other methods, highlighting the need for experimental validation in computational chemistry.

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Area of Science:

  • Computational chemistry
  • Solid-state chemistry
  • Crystallography

Background:

  • Accurate prediction of organic crystal stability is crucial for materials science.
  • Heavy halogens like bromine present unique challenges due to complex electronic structures and non-covalent interactions.
  • Existing computational methods often struggle with the nuances of halogen bonding and crystal packing.

Purpose of the Study:

  • To benchmark the performance of various computational methods for predicting the stability of chlorine- and bromine-containing organic crystals.
  • To identify reliable computational approaches for systems with heavy halogens.
  • To assess the accuracy of different methods against experimental data.

Main Methods:

  • Benchmarking of periodic Density Functional Theory (DFT) with PBE-D3BJ functional.
  • Evaluation of CrystalExplorer (CE17/CE21), DFTB3-D3BJ, and PM7 semi-empirical methods.
  • Comparison of calculated lattice energies against experimental stability data for 14 polymorphs across six Cambridge Structural Database (CSD) families.

Main Results:

  • Chlorine-containing systems showed consistent performance across methods.
  • Bromine-containing systems exhibited significant lattice energy variations (>10 kJ/mol) and incorrect stability rankings with several methods.
  • Low mean absolute errors did not guarantee correct thermodynamic ordering for bromine systems.
  • No tested semi-empirical method demonstrated universal reliability for bromine-containing crystals.
  • Periodic DFT (PBE-D3BJ) achieved perfect agreement with experimental data.

Conclusions:

  • Bromine-containing crystals serve as highly sensitive benchmarks for computational methods.
  • Internal validation against experimental data or reference DFT is essential before employing computational methods for large-scale studies.
  • The emergence of machine-learning potentials necessitates careful validation, especially for challenging systems like bromine crystals.