Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials.

Area of Science:

• Computational Chemistry
• Spectroscopy
• Biophysics

Background:

• Simulating optical spectra and excited-state dynamics requires integrating molecular dynamics with excited-state calculations.
• Classical force fields are often used for efficient sampling but introduce inaccuracies when combined with excited-state methods.
• Accurate spectral density estimation is crucial for understanding system-bath coupling.

Purpose of the Study:

• To develop and validate a multiscale simulation strategy for accurate excited-state calculations.
• To investigate the spectral density and photophysics of 3-methyl-indole in different environments.
• To overcome limitations of classical force fields in multiscale simulations.

Main Methods:

• Combining Electrostatically Embedded Machine Learning potentials (EMLE) simulations with the QM/MMPol polarizable embedding model.
• Utilizing molecular dynamics for efficient sampling in conjunction with advanced quantum mechanical methods.
• Calculating excited states and spectral density for 3-methyl-indole in gas phase, water, and protein environments.

Main Results:

• The multiscale protocol accurately reproduces results from ab initio QM/MM calculations.
• The strategy effectively computes excited states and spectral density for 3-methyl-indole.
• Demonstrated accuracy across gas, solution, and protein environments.

Conclusions:

• The developed multiscale strategy provides a computationally efficient and accurate approach for simulating optical spectra and excited-state dynamics.
• This method enables reliable investigations into the photophysics of tryptophan and other biological systems.
• It bridges the gap between biological motion timescales and photophysical processes.