Mulukutla Krishna Praneet supervised by Dr. Marimuthu Krishnan received his Master of Science – Dual Degree in Computational Natural Sciences (CND). Here’s a summary of his research work on Data-driven Approaches for Molecular Trajectory Generation and Kinetic Inference

Molecular dynamics (MD) simulations provide a powerful framework for studying the time evolution of molecular systems, enabling the characterization of both thermodynamic and kinetic properties. However, many biologically and chemically relevant processes, including protein conformational transitions and ligand-binding events, occur on timescales that remain inaccessible to conventional atomistic simulations. Monte Carlo and enhanced sampling methods can efficiently explore configurational space and accurately reproduce equilibrium distributions, but they do so at the expense of physical time, thereby limiting access to kinetic information. This thesis investigates whether physically meaningful molecular trajectories and dynamical information can be recovered without performing extensive molecular dynamics simulations. Three complementary approaches are developed and evaluated, each relying on data that is substantially less expensive to generate than fully converged MD trajectories.  

The first approach introduces a velocity-based trajectory reconstruction scheme that seeks to recover the temporal ordering of shuffled molecular dynamics frames. The method combines Maxwell–Boltzmann velocity likelihoods with a velocity continuity criterion, and a greedy optimization algorithm is employed to identify the most probable sequence of configurations. The approach performs well for simple, low-dimensional systems such as n-pentane, although its effectiveness decreases for more complex molecular systems.

The second and principal contribution of this thesis is DIME (Dynamics Inferred from Monte Carlo Ensembles), a framework that reconstructs molecular kinetics from static, Boltzmann-weighted ensembles. Metastable states are identified using density-based clustering, and a sparse transition network is constructed based on geometric and empirical adjacency relationships. A rate matrix satisfying detailed balance is then generated by construction, while absolute timescales are calibrated using a short MD reference trajectory. DIME is validated using n-pentane and alanine dipeptide, where it successfully reproduces MD-derived rate matrices, state occupancy autocorrelation functions, and implied timescales with quantitative accuracy following calibration. 

The third component of this work explores the use of deep generative models for trajectory generation in continuous configurational space. Normalizing flows trained on molecular configurations are employed to interpolate within latent space and generate low-barrier transition pathways. In a second approach, continuous normalizing flows are combined with Hamiltonian Neural Networks in latent space to produce dynamical trajectories. Both methods are demonstrated on the Muller–Brown potential ¨ energy surface.

Overall, the results presented in this thesis demonstrate that meaningful kinetic information can be extracted from computational approaches that require substantially less effort than fully converged molecular dynamics simulations. Among the methods developed, DIME provides the most comprehensive and rigorously validated framework for recovering molecular kinetics from equilibrium ensembles.

September 2026