?Swarm intelligence? is a behaviour exhibited by simple ?agents? that interact with one another and with their environment by following a set of simple rules. These interactions, over time, lead to the emergence of an "intelligent" global behaviour, unknown to the individual agents. For example, a single ant may not be smart enough or powerful enough to carry a big insect to its nest, but the entire colony can. Swarm intelligence, in nature, can be seen in ant colonies, flocks of birds or herds of animals.
Applying the same technique, the researchers, led by Prof. Shankar Prasad Bhattacharyya (former Raja Ramanna Fellow of Department of Atomic Energy) from IIT Bombay, have developed an evolutionary model to find the energy configuration and electric charge distribution in a molecule of polythiophene, a polymer.
Any molecule consists of a group of atoms linked together by chemical bonds. The structure of the molecule is expressed in terms of the length and the angle of this chemical bond formed between different atoms. A molecule can have various forms and properties based on the arrangement of its constituent atoms. Such an arrangement of atoms in a particular configuration also corresponds to different energy levels of the molecule. Lower the energy level, more stable the configuration. These energies, when plotted against geometrical parameters, like bond length and angle, give rise to a potential energy surface in n-dimensional space. Each point on this surface corresponds to a unique structure having a particular level of energy and electronic structure. It is challenging to determine the global minimum potential energy point of a molecule and its electronic charge distribution simultaneously due to presence of multiple local maximum and minimum potential energy points.
The researchers have applied the technique of swarm intelligence to solve this problem. ?To search for a global minimum point on a function, we can have multiple agents, known as swarm particles, exploring the space and communicating with each other to optimize the search efficiency. Let's say we start with 10 or 20 swarm particles with random positions and random initial velocities that will move in the n-dimensional space. While traversing their path, they will remember the best point found by them so far, call it individual best point. If they come across a point which is better than the individual best point, then it is updated. In addition, the swarm remembers its global best point. If an individual best point is found to be better than global best point, then global best point is updated,? explains Rishabh Shukla from IIT, Guwahati, and the lead author of the study. ?At each time step, the velocity of each particle is updated based on 3 factors: current velocity, its distance from the individual best point, and its distance from the global best point. This way, the swarm is continuously going towards better points and finally reaches the true global minimum.?
This study is a unique attempt to apply techniques of swarm intelligence, hitherto used in computer science, to molecular structures. "The work signifies that collective intelligence displayed by classical swarms can be exploited to search through the space of the nuclear degrees of freedom (classical evolution) and bring in simultaneous evolution of the electronic charge distribution (quantum evolution). The net outcome is a mixed quantum classical method that smoothly and simultaneously locates the global minimum energy configuration of nuclei and the associated electron density distribution in a large molecule,? says Prof. Bhattacharyya.
Source: Research Matters
Image courtesy: Research Matters
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