ǂAn ǂartificial intelligence approach for modeling molecular self-assembly

agent-based simulations of rigid molecules

Sara Fortuna (Author), Alessandro Troisi (Author)

Abstract

Agent-based simulations are rule-based models traditionally used for the simulations of complex systems. In this paper, an algorithm based on the concept of agent-based simulations is developed to predict the lowest energy packing of a set of identical rigid molecules. The agents are identified with rigid portions of the system under investigation, and they evolve following a set of rules designed to drive the system toward the lowest energy minimum. The algorithm is compared with a conventional Metropolis Monte Carlo algorithm, and it is applied on a large set of representative models of molecules. For all the systems studied, the agent-based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated).

Keywords

self-assembly;self-organisation;agent based simulations;Monte Carlo algorithm;rigid molecules;

Data

Language:	English
Year of publishing:	2009
Typology:	1.01 - Original Scientific Article
Organization:	UNG - University of Nova Gorica
UDC:	54
COBISS:	4530171
ISSN:	1520-6106
Views:	4080
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

URN:	URN:SI:UNG
Type (COBISS):	Not categorized
Pages:	ǂstr. ǂ9877-9885
Volume:	ǂVol. ǂ113
Issue:	ǂno. ǂ29
Chronology:	2009
DOI:	10.1021/jp9030442
ID:	9175937