What is basic of Molecular Dynamics?
Molecular Dynamics (MD) is a technique in computational chemistry and physics to model the movements particles over time. Essentially, Molecular Dynamics operates at the atomic or molecular scale by considering the individual particles and their interactions within a system. Molecular Dynamics can tell you the temporal changes of a system in different conditions such as molecular diffusion, phase transitions, structural changes etc.
Assumptions
The classical Molecular Dynamics is based on two assumptions to simplify the theoretical framework. First, a charged ball models the atoms. Second, a classic spring can link these balls together to make molecules. All movement operate on the basis of the Newton’s equation, with interactions transferring energy and momentum between the particles. This means a minimal modeling details, where no chemical changes take place. The classic approach is used for simpler models, such as calculating the solvation energy.
Simulation process
First step in a molecular dynamics setup involves defining the atomic species and positions within the simulation box and the initial thermodynamic attributes and constant conditions. This is important because it is on the basis of the thermodynamic characteristics at the initial state that the initial forces and speeds are determined. These conditions are also responsible for evaluating the initial energy of the particles, which is then distributed according to the Boltzmann distribution.
Next, a suitable potential is elected to govern interactions between atoms within the simulation box. These interactions determine the interatomic (bonded), intermolecular (nonbonded) and external interactions, forces. This step requires careful consideration of previous works to find the forcefield best suited for the desired application.
Finally, Newton’s law of motion is applied based on the forces derived earlier. Forces calculated from the previous step drive the molecules/atoms towards the next position with a certain velocity. These are then adjusted based on the predefined conditions, constant volume, pressure, temperature etc. This process continues for each time step, until the end of simulation.
Notes
The first works on molecular dynamics simulations go back more than half a century. Since then, there has been numerous revisions, or upgrades!, to further increase the accuracy while allowing larger models considering the present state of the computational capacity. Some of these upgrades include the effect of electrons through the Schrödinger equation. Approximations based on the available resources facilitate the near impossible task of finding an exact solution for said equation. Sampling can preserve the functionality of the method while benefitting from the effects of electrons. This approach tries to limit the calculations to the more feasible energy states.
Researchers utilize Molecular Dynamics to gain insights into the dynamic aspect of systems in different phases. One of the more apparent advantages of theoretical simulations is the visual presentation of the modeled situation. This provides a comprehensive understanding of the ongoing process, mainly used to clarify the details of a complex mechanism.
Molecular Dynamics vs Density Functional Theory (DFT)
Density Functional Theory (DFT) is a significant improvement on the classical Molecular Dynamics that uses a simplified quantum mechanical approach. This approach is designed to correct the main calculations by introducing the electronic intricacies of molecules and materials. Compared to Molecular Dynamics, DFT operates at the electronic scale. DFT goes deeper into the realm of electron distributions, energy levels, and molecular structures. This way, we can predict electronic properties, such as charge distributions, bonding energies, and molecular geometries.
DFT operates within the Hohenberg-Kohn theorem that states all properties of the system can be derived from knowing the electron density within the system. Following this, in DFT the atoms are modeled as their electron density function and their interactions are resolved through Kohn-Sham equations complemented by exchange-correlation potential. This creates very intense computational costs for the processor that makes large scale simulations near impossible. This downside has led to innovations such as hybrid methods like QM-MD.
While the smallest scale simulations are handled by the accurate DFT approach, larger scale phenomena are usually observed in coarse-grained method.
Molecular Dynamics vs Coarse-Grained (CG) Modeling
Coarse-Grained (CG) modeling tries to simplify complex systems by grouping multiple atoms into single entities referred to as “beads”. These are kind of pseudo-particles, meaning they are not real particles but a mathematical representation. This means less calculations (less particles), but you lose accuracy. These methods are mostly used in cooperation with other methods, mentioned above, to compensate for the lack of atomistic insight. As a result, we get to look at the larger scale to specifically observe our desired species at work. We are also keeping a look at the overall atomic interactions given the computational capabilities at our disposal.
Therefore, Coarse-Grained models provide a balance between computational efficiency and capturing larger-scale phenomena. In other words, the Coarse-Grained analysis provides an acceptable compromise to elevate the scale of the Molecular Dynamics method. These models are particularly valuable when studying processes over extended time scales or systems with a multitude of interacting components. While Coarse-Grained models sacrifice some atomic-level detail. In return, they offer a pragmatic compromise to explore phenomena beyond the scope of traditional simulations.
Notes
Here we are: in this brief introduction we touched on one of the most complex and impactful engineering tools that when used effectively, these methods can significantly boost the reasoning and scientific framework of your research! If you have a project and you want to use the power of MD/DFT/CG, let us know if we can help.
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