Genetic algorithms in molecular design of novel fabrics Sylvia Wower


Published on

Genetic algorithms in molecular design of novel fabrics Sylvia Wower / Market Research / Philadelphia / DVIRC / Manufacturing / Philadelphia MSA

Published in: Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Genetic algorithms in molecular design of novel fabrics Sylvia Wower

  1. 1. NTC Project: C05-PH01 Fitness Landscape Genetic Algorithms in Molecular Design of Novel Fibers Les Sztandera, leader (PhilaU); Hugh Cartwright (Oxford), Chih-Chung Chu (Cornell) The formulation of materials which satisfy strict property constraints is an increasingly important problem in polymer chemistry. We are using two techniques from the field of artificial intelligence to help design such polymers. As a model we used a neural network whose role is to predict the properties of a given polymer from its composition or struc- ture, thereby solving what is known as the forward prob- lem. We also used a genetic algorithm, which solves the inverse problem by acting as a search procedure to find the optimum formulation. Combined together in a collabora- tive manner, these two techniques form a paired algorithm in which the neural network is used in the calculation of the genetic algorithms’ fitness function. The tool created this way is known as a Hybrid Intelligent System. We chose object-oriented design to develop the software tool, rather than a functional approach, because it produces more main- tainable and easily understood system architecture and code. : Tg = 299.9 K [z-axis value = fitness function] Initially, our work focused on the second of the two tools required for the Hybrid System, the Genetic Algorithm (GA). The present formulation of the GA comprises two sub-systems: a GA engine with five fitness functions and a graphical user interface (GUI) front end which provides the user with an easy route into the functionality of the GA en- gine. The design methodologies we used to produce the GA are analogous to the evolutionary in- cremental software engineering model. Starting from a basic genetic algorithm, we added input and output functionalities to yield a checked test-bed model, which we will use as the basis of the polymer modeling system. We designed a suitable GUI sub-system so the user can enter and modify GA parameters; this incorporates checks to validate parameters as they are entered. The fitness function is a quantitative measure of solution quality and is specific for a given target glass transition temperature (Tg) [see graph]. The fitness landscape shows the quality of the formulation solutions across the search space. Initial versions of the algorithm included only a single fitness function. Recently, we added a number of functionalities and four additional fitness functions which we incorporated into the architectures of both the GA en- gine and the GUI. Case Studies In our Case Study # 1, we used the neural network to for- mulate the mole fractions of three constituent monomers which will form a terpolymer with a target Tg. The system under investigation contains as its monomers, n-octadecyl acrylate, ethyl acrylate and acrylonitrile. The data for this system came from Jordan.1,2 Our Case Study # 2 aimed to solve a problem that is of considerably higher dimensional- ity, since the copolymer system now contained nine co- monomers. The forward problem was of the same form as in Case Study # 1 and again was solved by a neural network. However, the inverse problem demands another method of solution since employing the custom algorithm used in Case Study # 1 was only practicable due to the small-scale nature of the problem. In Case Study # 2, the search space becomes more complex, so that a simplistic algorithm would be ineffective.3 Instead we chose the genetic algo- rithm to solve the inverse problem. Continuing Research We are now extending our work to include data from even larger databases, and to investigate the prediction of properties of random sets of polymers formed from an al- phabet of divalent molecular fragments. This problem has previously been studied using linear correlation; early re- sults suggest that the neural network hybrid method will be a more accurate predictor of polymer properties. Other Contributors: Graduate Students: Jonathan Mohr, Sylwia Wower, Xi Chen (PhilaU); Undergraduate Stu- dents: Andrew Regis (PhilaU), Rohan Gunatillake (Ox- ford); Contributing Faculty: Fernando Tovia (PhilaU). Industry Interactions: 2 [Tribology Consulting Int., ETHI- CON Products Co] Project Web Address: For Further Information: Using artificial intelligence techniques, we are designing polymer formulations with specified properties, such as stretch, strength, bulk, comfort and dyeability. 1. E. F. Jordan et al., J. App. Poly. Sci., 16 :3017 (1972). 2. E. F. Jordan et al., J. App. Poly. Sci., 17:1545 (1973). 3. M. Mitchell, J. H. Holland and S. Forrest, in Advances in Neu- ral Information Processing Systems 6, Eds. J. D. Cowan, G. Tesauro, and J. Alspector, Morgan Kaufman, San Francisco (1994). 4. H. M. Cartwright, Applications of Artificial Intelligence in Chemistry, OUP, Oxford (1994) 5. Hugh M Cartwright and Les M. Sztandera (Eds.), Soft Com- puting in Chemistry, Springer-Verlag, Heidelberg (2002). 6. Hugh Cartwright (Ed.), Intelligent Data Analysis in Science, Oxford Chemistry Masters Series, Oxford Univ. Press (2000). 7. Hugh M. Cartwright, Investigation of Structure-Biodegrada- bility Relationships in Polychlorinated Biphenyls using Self- organizing Maps Neural Computing and Applications 11:30 (2002) 8. Ketan Patel and Hugh M. Cartwright, Clustering of Large Data Sets in the Life Sciences in: Soft Computing in Chemis- try, Hugh M. Cartwright & Les M. Sztandera (Eds.), Springer-Verlag. Heidelberg (2002). 9. H. M. Cartwright, L. M. Sztandera and C. C. Chu, Genetic Al- gorithms in Molecular Design of Novel Fibers, International Journal of Intelligent Systems [submitted] (2005). 10. Rohan Gunatillake, Part II Chemistry thesis: Oxford Univ., Hybrid Intelligent Systems in Polymer Design (2005) National Textile Center Research Briefs – Chemistry Competency: June 2006
  2. 2. NTC Project: C05-PH01 Les M. Sztandera, a Professor of Com- puter Information Systems at PhilaU served as a Distinguished Fulbright FLAD Chair in Information Systems Les earned a Diploma from Cambridge (England) in 1989, an M.S. from Univ. of Missouri in 1990 and a Ph.D. in com- puter and engineering science from the Univ. of Toledo in 1993. Les’ research interests include fuzzy logic, pattern recognition, computer vision, genetic algorithms, neural networks, hybrid in- telligent systems, and modeling and management of uncertainty. I98-P01, S01-PH10, C04-PH02s*, C05-PH1* (215)-951-5356 Hugh M. Cartwright, a Lecturer and Laboratory Officer in Chemistry at Ox- ford Univ. (UK), joined the faculty in 1984. He earned a B.Sc. in 1969 and a Ph.D. in 1972 in chemical sciences at the Univ. of East Anglia (Norwich Eng.). Hugh is the author of Applica- tions of Artificial Intelligence in Chem- istry. His research interests center on the use of artificial intelligence in sci- entific problems, such as the dispersal of airborne pollution, optimization of organic synthesis, industrial process control and development, drug design, bacterial growth, bio-informatics and the assessment of medical data. C04-PH02s, C05-PH01 44 (0) 1865 275 483 Chih Chung Chu, a Professor in Bio- medical Engineering at Cornell, joined the faculty in 1978 after 3 years at Univ. of Alabama - Birmingham. C.C. earned a Ph.D. in polymer chemistry from Flor- ida St in 1976 and a B.S. in chemistry Tamkang Univ. (Taiwan) in 1968 and served from 1986-90 as Visiting Re- search Associate Professor of Surgery at the Hahemann Univ. School of Medi- cine (Philadelphia). His interests in- clude basic research in polymer/fiber morphology and degradation mecha- nisms and applied research in bio- medical polymers and fibers for human body repair. M01-CR01*, M03-CR04*, C04-PH02s, C05-PH01 (607)-255-1938 National Textile Center Research Briefs – Chemistry Competency: June 2006