Combining configurational energies and forces for molecular force field optimization.

Area of Science:

• Computational Chemistry
• Materials Science
• Molecular Dynamics

Background:

• Quantum chemical simulations are crucial for atomistic model development but are computationally expensive, limiting data sampling.
• Efficiently utilizing simulation data and incorporating experimental constraints is vital for practical applications.

Purpose of the Study:

• To develop a method for increasing the effective number of samples in molecular dynamics simulations.
• To optimize molecular force fields using combined energy and force information.
• To compare the proposed method with existing techniques like force matching.

Main Methods:

• Combining configurational energies and forces from molecular dynamics simulations.
• Optimizing molecular force fields via minimizing a statistical distance similarity metric.
• Illustrating the methodology with argon and water simulations.

Main Results:

• The proposed approach effectively increases the number of samples in simulations.
• Force field optimization using this method shows promising results.
• Comparison with force matching method highlights the efficacy of the new approach.

Conclusions:

• The presented method offers an efficient way to leverage simulation data for improved force field development.
• This approach facilitates the integration of computational insights with experimental data.
• The technique holds potential for advancing atomistic modeling in various scientific domains.