[month] [year]

Mayank Modi

Mayank Modi supervised by Dr. Prabhakar Bhimalapuram  received his Master of Science – Dual Degree  in Computational Natural Science (CNS). Here’s a summary of his research work on Promoting auto-ionization in water: A coarse grained approach:

 Water, a ubiquitous molecule in life and chemical processes, undergoes autoionization, where it splits into a proton (H+) and a hydroxide ion (OH-). This process plays a crucial role in determining pH and conductivity. However, the mechanism of autoionization remains a mystery due to the vast difference in stability between water and dissociated ions. This difference creates a multiscale kinetic challenge, making it difficult to simulate the entire process using traditional methods. This thesis investigates the water auto-ionization mechanism using well-tempered metadynamics, a powerful tool for exploring free energy landscapes. We employ atomic neural network potentials (NNPs) to overcome the computational limitations of traditional density functional theory (DFT) calculations, allowing us to explore the ionization pathway at a nanosecond timescale. Central to our methodology is the identification of an effective collective variable to drive the ionization in a way that doesn’t break the dynamics of water or disturb the free energy surface. Our approach of using a group of water molecules in vicinity and biasing the molecules to align to a favourable configuration for ionization allows us to initiate ionization on our choice of water molecules. In our thesis we demonstrate the evolution of our collective variable arising from the observed mechanics of auto-ionization along with the various observed patterns in which the ions exist in the system. We have employed a few algorithms to identify the existence of ions. The combination of our collective variables and the analysis algorithms, gives us a more definite molecular understanding of the dynamics defined in previous related work. We demonstrate that our observed dynamics are in excellent agreement with the physical qualities of water and also well represent the dynamics of ionization talked about in previous articles. With the collected data we also attempt to conduct important classification of various parameters that might describe the initiation conditions of ionization with a good accuracy. In the bigger picture, this understanding may allow us to get closer towards using machine learning neural network potentials to reliably study the dynamics of other water implicit systems and hence gather more information about auto-ionized water and its effects on such systems. In essence this thesis demonstrates the use of neural network potential n2p2 as a reliable method of simulating ions in water without disturbing the dynamics of water or the ions. The use of neural network potential instead of DFT calculations allows us to study significantly longer timescales within the same processing constraints as before. This can be a significant step towards studying systems that are affected by the auto-ionization of water eg. water implicit protein folding.

 October 2024