Authors: Arcila Pérez, Joaquín
Source: Institutional Repository of the University of Granada (University of Granada) (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Revisión bibliográfica sobre dinámica molecular. Modelización, algoritmos y aplicaciones This Final Degree Project has focused on the study of molecular dynamics (MD), a computational technique that enables the modeling of atomic and molecular systems based on their interactions. Throughout the project, a comprehensive analysis of this methodology has been carried out, covering everything from its theoretical foundations to its various practical applications, with particular attention to the algorithmic tools used in simulations. First, the historical and scientific context in which molecular dynamics emerged has been introduced, from the pioneering experiments of Alder and Wainwright in the 1950s, through the fundamental contributions of Rahman and Verlet, to the technological advances that enabled the application of these methods to the study of complex biological systems and materials using high-performance computing (HPC) and graphics processing units (GPUs). In summary, the milestones that allowed the transition from simple hard-sphere gas simulations to advanced simulations of proteins, solvents, or crystalline networks have been identified, consolidating molecular dynamics as a key tool in materials science, biophysics, and molecular design. Next, the project addresses the foundations of classical and statistical mechanics that form the basis of simulations. On one hand, it analyzes how Newton’s laws lead to the equations of motion governing the evolution of a particle system. These equations allow the problem to be formulated as a Hamiltonian system, whose mathematical structure imposes certain properties that must be respected by numerical algorithms. One of the most relevant is symplecticity—a property that must be preserved by integration methods to ensure physically coherent long-term simulations. In this context, the Verlet algorithm and its variants have been studied, highlighting their ability to conserve system energy over time and their numerical stability compared to non-symplectic methods such as the Euler integrator. The importance of selecting an appropriate integration step to ensure both stability and accuracy has also been discussed. On the other hand, the role of interaction potentials in molecular dynamics has been analyzed, particularly classical force fields. These models approximate intermolecular forces through terms representing bonds, angles, torsions, as well as Van derWaals and electrostatic interactions. It has been emphasized how the choice of force field directly affects the precision and applicability of the simulation, making proper parametrization essential to faithfully represent the studied systems. Additionally, the importance of the level of detail with which the system is represented has been highlighted, addressing the trade-off between accuracy and computational cost. An essential part of the work has been the discussion of the different thermodynamic ensembles (NVE, NVT, NPT) and how they are implemented in practice through the use of thermostats and barostats. The most commonly used methods have been examined, such as the Berendsen and Nosé-Hoover thermostats, explaining their advantages, limitations, and their impact on the system’s dynamics and statistics. Regarding barostats, the functioning of the Berendsen and Parrinello-Rahman methods has been explained, the latter being especially useful in simulations of materials under stress. Subsequently, the issue of simulation initialization has been addressed, highlighting the importance of performing a prior energy minimization step to avoid artificial configurations with high energies or particle overlaps. This step relies on optimization methods, such as gradient descent or conjugate gradient, which allow for the identification of stable configurations corresponding to local minima on the potential energy surface. In complex systems, where this surface presents multiple local minima, the role of metaheuristics has been emphasized as tools to escape from these minima and approach the global minimum. In this context, Monte Carlo methods such as the Metropolis algorithm have also been introduced, offering an alternative to molecular dynamics by generating random system configurations based on probabilistic criteria. A key aspect addressed in the project has been the use of reduced units in simulations. This technique allows physical quantities to be expressed in a dimensionless form by using characteristic parameters of the system as scaling factors. This not only simplifies the physical expressions used but also improves numerical stability and facilitates comparison between different simulations. Furthermore, it allows code generalization, since results can later be rescaled to real physical units if desired. Finally, a pseudo-algorithm has been designed to perform a molecular dynamics simulation using this unit system. The great potential of molecular dynamics in real-world applications is also highlighted. Its uses in materials science, computational chemistry, structural biology, and particularly in drug design have been discussed. In the latter field, simulations help understand ligandprotein binding, evaluate the stability of molecular complexes, and investigate interactions at the atomic level. These capabilities are essential to accelerate the discovery of new treatments for diseases such as cancer, HIV, and neurodegenerative disorders. Finally, the project dedicates a section to the current limitations of molecular dynamics. It analyzes how computational scalability remains a challenge, especially for large or longduration simulations. It also points out the inability of classical force fields to accurately describe quantum phenomena, which has motivated the development of hybrid approaches such as the QM/MM method. Based on these limitations, several opportunities for improvement are identified, focusing on the application of artificial intelligence techniques, the integration of multiscale methods, and the exploitation of high-performance architectures—thus reflecting the great potential of molecular dynamics. In summary, this work provides an overview of molecular dynamics, covering its theoretical foundations, computational methods, techniques for regulating thermodynamic conditions, optimization strategies, and major applications in modern science. It concludes that, despite its limitations, molecular dynamics is a fundamental tool for studying complex systems at the atomic scale, with significant room for advancement. Verlet integration ; Context (archaeology) ; Simple (philosophy) ; Graphics ; Key (lock) ; Mathematical structure ; Basis (linear algebra) Advanced Physical and Chemical Molecular Interactions ; Multidisciplinary Science and Engineering Research ; Technology in Education and Healthcare