Automatic Differentiation for Enhanced Potential Energy Surface Navigation: Improved Minimum and Transition State Search in Molecular Dynamics

Reaction models are essential for understanding chemical reactions, but modeling them is a time-demanding process. Automated reaction space exploration techniques, such as ChemTraYzer-TAD, can simplify this process. However, finding transition states (TS) remains a hurdle. TS geometries are crucial for calculating reaction rate constants. Quantum mechanical methods are computationally expensive for TS geometry searches, while reactive molecular mechanics, like ReaxFF, offer faster calculations. Accurate TS searches require second derivatives of energy. State-of-the-art ReaxFF implementations can provide these derivatives only through finite differentiation (FD), which introduces noise. Automatic differentiation (AD) can provide more accurate second derivatives. Hence, this work integrates AD into the classical molecular dynamics code LAMMPS for calculations of second derivatives, presenting ADfied LAMMPS. By interfacing ADfied LAMMPS with the Gaussian computational chemistry suite, LMP-Gau is developed, enabling efficient geometry optimization, frequency calculations, and reaction path following for any force field. LMP-Gau demonstrates improved energy minimization for stable molecules in comparison to standard LAMMPS methods. It is also used successfully to find transition states in 1,3-dioxolane oxidation, demonstrating improved convergence with AD compared to FD.