FST Graduate Research Topic

Research Topic: Molecular Dynamics (MD) Simulation Engine in computational science
Suggested By: Harris Wang
Last Update: September 28th, 2025

Constrained Object Hierarchies (COH) provides a neuroscience-inspired theoretical framework for modeling Artificial General Intelligence (AGI). As a flexible and robust foundation, COH supports the representation of general intelligent systems across diverse domains. Formally, a COH is defined as a 9-tuple: O = (C, A, M, N, E, I, T, G, D) Where: C: Components – sub-objects organized hierarchically A: Attributes – state variables describing object properties M: Methods – executable actions associated with components N: Neural Components – adaptive models for learning and inference E: Embedding Neural Component – semantic representation of the COH I: Identity Constraints – rules ensuring object integrity T: Trigger Constraints – event-condition-action rules for dynamic behavior G: Goal Constraints – optimization objectives guiding intelligent behavior D: Constraint Daemons – real-time monitors enforcing system constraints Based on the COH framework, the modelling process and result of Molecular Dynamics (MD) Simulation Engine in computational science are as follows: Description & Significance: MD simulations calculate the trajectories of atoms and molecules by numerically solving Newton's equations of motion. They are a cornerstone of computational chemistry, materials science, and biophysics [10]. The intelligence lies in maintaining stability, adhering to ensemble constraints (e.g., NVT), and efficiently calculating forces. Analysis & Requirements: The system must manage a large number of interacting particles, compute forces accurately, integrate equations of motion stably, and maintain thermodynamic conditions. COH Design: C: Simulation_Box, Atom_1..N, Force_Calculator, Integrator. A: Atom.position, Atom.velocity, Simulation_Box.temperature. M: compute_forces(), integrate_equations_of_motion(). N: A surrogate model (e.g., GNN) for faster force approximation. E: The semantic meaning of an Atom is defined by its type and state within the ensemble. I: Total_Energy = Constant (NVE ensemble). T: IF (temperature deviates) THEN rescale_velocities(). G: Minimize energy drift; maximize simulation speed. D: Monitors energy conservation. The COH model cleanly separates the computational components (C, A, M) from the intelligent aspects. The use of N for surrogate modelling explicitly captures a modern trend in MD. The constraints I, T, G formally encode the physics (conservation), the control logic (thermostat), and the performance objectives. You may choose this topic and pursue one of the following routes: 📝 Essay Survey existing methodologies for modeling, designing, and implementing similar intelligent systems. Include: Literature synthesis COH framework analysis Critical evaluation of competing approaches 🛠️ Project Design and implement the system using the COH model. Include: System architecture and design Development and testing Demonstration of core functionality 🎓 Thesis Explore COH’s theoretical foundations in depth. Include: Proposed enhancements or extensions Implementation and evaluation Development of an advanced intelligent system grounded in COH principles