A Multi-Objective Approach to Force Field Optimization: Structures and Spin State Energetics of d(6) Fe(II) Complexes

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Quantum Chemistry

Background:

• Accurate molecular force fields (FFs) require parameter fitting to experimental or ab initio data.
• Traditional single-objective optimization methods for FF parameters suffer from ambiguity and non-unique solutions.
• Parameterization remains a significant challenge for developing empirical FF methods.

Purpose of the Study:

• To introduce multi-objective evolutionary algorithms (MOEAs) as a solution for automated and unambiguous FF parameterization.
• To reparameterize the ligand field molecular mechanics (LFMM) FF for iron(II)-amine complexes using MOEAs.
• To improve the accuracy of FF in reproducing complex geometries and spin state energy differences.

Main Methods:

• Application of multi-objective evolutionary algorithms (MOEAs) for FF parameter optimization.
• Reparameterization of the ligand field molecular mechanics (LFMM) FF.
• Validation against experimental and ab initio data for iron(II)-amine complexes.

Main Results:

• MOEAs efficiently reparameterized the LFMM FF, matching and then significantly improving upon the original parameter set.
• Achieved substantial reductions in root-mean-square deviation (RMSD) for Fe-N and N-N distances (0.06 Å to 0.03 Å).
• Reduced RMSD for spin state energy differences from 1.5 kcal mol(-1) to 0.2 kcal mol(-1).

Conclusions:

• MOEAs provide a powerful, efficient, and automated method for FF reparameterization and discovery.
• The new parameter sets reveal and help address limitations in existing FF parameters and experimental data interpretation.
• This work advances the development of accurate empirical force fields for modeling complex chemical systems.