Dr. Luca Donati

MATH+ Student Research Group

• Topic Estimating pH-dependent rates by machine learning techniques
• Location Room 046 at Takustr. 9 (FU Berlin) on Friday from 10 to 12
• The seminar will start on 12 May.

• Molecular Dynamics (MD) simulations are a powerful tool to study the behavior of molecules at the atomic level. A wide range of computational methods have been developed to analyze MD simulations and to extract meaningful information such as transition rates.
• The purpose of the Student Research Group is to study and extend these methods to determine how transition rates of molecular systems are affected by the pH of the environment [1,2].
• The results of this project will be relevant in studying the μ-opioid receptor system, whose activation and the emergence of adverse effects by opioids depend on the pH of the cell membrane within which it resides [3].
• The project is highly interdisciplinary and students from physics, chemistry, and mathematics are encouraged to participate.
• Please contact me if you are interested in the project.
  
  
• References
  1. L. Donati, and M. Weber. In: J. Chem. Phys. 157.22 (2022), p. 224103, DOI.
  2. R. J. Rabben, S. Ray, and M. Weber. In: J. Chem. Phys. 153.11 (2020), p. 114109, DOI.
  3. G. Del Vecchio, et al. In: Sci. Rep. 9 (2019), p. 19344, DOI.
  
  
  
  
  
• First lecture
• Introduction
• Simulation of fentanyll and μ-opioid receptor
• Notes on Hamiltonian Dynamics



• Second lecture
• Notes of second lectures
• Introduction to Python
• Jupyter notebook with basic commands (right click, save link as)
• Jupyter notebook with harmonic oscillator 1 (right click, save link as)



• Third lecture
• Notes of third lectures
• Jupyter notebook with harmonic oscillator 2 (right click, save link as)



• Fourth lecture
• Notes about Taylor expansion
• Notes about numerical integrators



• Jupyter notebook with some useful command (right click, save link as)
• Jupyter notebook with Taylor expansion example (right click, save link as)
• Jupyter notebook with complete solution of harmonic oscillator (right click, save link as)



• Fifth lecture
• Notes of fifth lectures



• Jupyter notebook with the solution of the 1D Morse potential (right click, save link as)
• Jupyter notebook with 2D diatomic molecule, complete until force calculation (right click, save link as)



• Sixth lecture
• Jupyter notebook with 2D diatomic molecule, complete (right click, save link as)
• Jupyter notebook with Gaussian distribution 1, complete (right click, save link as)



• Seventh lecture
• Jupyter notebook with Gaussian distribution 2 (right click, save link as)
• Jupyter notebook with Gaussian distribution 3 (right click, save link as)



• Eighth lecture
• Jupyter notebook with Brownian motion (right click, save link as)
• Notes on Brownian motion



• Ninth lecture
• Jupyter notebook with Brownian motion 2 (right click, save link as)
• Jupyter notebook with Langevin dynamics (right click, save link as)



• Tenth lecture
• Jupyter notebook with Python exercises (right click, save link as)
• Jupyter notebook with Langevin dynamics and rate calculation (right click, save link as)