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Designing organic reaction simulation engine using qualitative reasoning approach

Designing organic reaction simulation engine using qualitative reasoning approach. Y.C. Alicia Tang Tenaga Nasional University Sharifuddin M. Zain (Chemistry Department, Malaya University ) Noorsaadah A. Rahman (Chemistry Department, Malaya University ). MALAYSIA.

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Designing organic reaction simulation engine using qualitative reasoning approach

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  1. Designing organic reaction simulation engine using qualitative reasoning approach Y.C. Alicia Tang Tenaga Nasional University Sharifuddin M. Zain (Chemistry Department, Malaya University) Noorsaadah A. Rahman (Chemistry Department, Malaya University) MALAYSIA ACS'08, 21-23 November, Venice, ITALY

  2. Contents (I) • Introduction • Qualitative reasoning (QR) • Problems and motivations • Organic reactions and mechanisms • System methodology • Previous works • Functional components of QRIOM prototype ACS'08, 21-23 November, Venice, ITALY

  3. Contents (II) • Reaction examples tested • A reasoning scenario • Qualitative reasoning algorithm • Qualitative modeling algorithm • A QPT process model • Results • Conclusion and future works ACS'08, 21-23 November, Venice, ITALY

  4. Introduction (I) • This work presents a framework architecture that uses the QPT ontology as the knowledge representation scheme to model the behaviors of a number of organic reactions. • We investigated qualitative representation and qualitative simulation approaches • The goal is to develop a learning tool that teaches an organic chemistry course at the University of Malaya • will undergo mental change so that they are able to explain chemical phenomenon in a more elaborated way ACS'08, 21-23 November, Venice, ITALY

  5. Qualitative Reasoning • Qualitative Reasoning (QR) research • Attempts to model behavior of dynamic physical systems without having to include a bunch of formulas and/or quantitative data • The research spans all aspects of the theory and applications • Techniques, applications, task-level reasoning, modeling, etc. ACS'08, 21-23 November, Venice, ITALY

  6. Problems and Motivations (I) • Students faced problem in organic reaction mechanism course • They learn the subject by memorizing the steps and formulas of each reaction taught in classrooms • Poor conceptual understanding • Not knowing the principles governing the processes and the cause effect interaction among processes ACS'08, 21-23 November, Venice, ITALY

  7. Problems and Motivations (II) • Simulation of chemical reactions that relied on pre-coded facts and rules cannot explain its results • Since no tight coupling between concepts and their embodiment in software • QPT describes processes in conceptual terms and embody notions of causality • which is important to explain behavior of chemical systems. ACS'08, 21-23 November, Venice, ITALY

  8. Organic Reactions & Mechanisms • Areaction mechanismdescribesthe sequence of steps (processes) that occur during the conversion of reactants to products • Mechanism is used to explain how a reaction takes place by showing what is happening to valence electrons during the making and breaking of bonds. • Organic chemists could work out the mechanisms by using knowledge developed from their chemical intuition and experience. ACS'08, 21-23 November, Venice, ITALY

  9. System Methodology • Identifying chemical properties for organic reactions for model composition use • Classifying the possible reaction species and types • Developing the automated model construction logic • Developing the reasoning steps for predicting and simulating the chemical behaviors of selected organic reactions • Designing the means for generating explanation ACS'08, 21-23 November, Venice, ITALY

  10. Previous Works • From numerous substrates and reagents, we classify reacting species as either a nucleophile (charged/neutral) or an electrophile (charged/neutral) • upon which chemical processes are selected • Two main reusable processes identified • Namely, the “make-bond” and “break-bond”, for the entire reaction mechanisms, specifically on SN1 and SN2. ACS'08, 21-23 November, Venice, ITALY

  11. QRIOM:Functional Components

  12. Reaction examples • Reasoning cases • Mechanisms = 2 • Reaction formulas = 3 • Specific cases of simulation = 28 • SN1 • Tertiary alcohol + Hydrogen halide • (CH3)3COH HX • CH3CH3CH3COH + HF • CH3CH3CH3COH + HCl • CH3CH3CH3COH + HBr • CH3CH3CH3COH + HI • Alkyl halide (tertiary) + Water molecules • (CH3)3CX 2H2O (in excess) • (CH3)3CF + 2H2O • (CH3)3CCl + 2H2O • (CH3)3CBr + 2H2O • (CH3)3CI + 2H2O ACS'08, 21-23 November, Venice, ITALY

  13. SN2 Alkyl Halide (primary) + Incoming nucleophile CH3CH2X Hydroxyl functional group CH3F + HO- CH3Cl + HO- CH3Br + HO- CH3I + HO- CH3CH2F + HO- CH3CH2Cl + HO- CH3CH2Br + HO- CH3CH2I + HO- CH3CH2CH2F + HO- CH3CH2CH2Cl + HO- CH3CH2CH2Br + HO- CH3CH2CH2I + HO- CH3CH2CH2CH2F + HO- CH3CH2CH2CH2Cl + HO- CH3CH2CH2CH2Br + HO- CH3CH2CH2CH2I + HO- ACS'08, 21-23 November, Venice, ITALY

  14. A QPT-basedreasoning scenario ACS'08, 21-23 November, Venice, ITALY

  15. The production of alkyl halide A = tert-Butyl alcohol, B = Hydrogen chloride C= tert-Butyl chloride, D = Water molecule ACS'08, 21-23 November, Venice, ITALY

  16. ACS'08, 21-23 November, Venice, ITALY

  17. Qualitative Simulation See next slide Top Level Design ACS'08, 21-23 November, Venice, ITALY

  18. Qualitative Modeling ACS'08, 21-23 November, Venice, ITALY

  19. A “make-bond” process in QPT terms Functional dependency implemented as qualitative proportionality modeling construct ACS'08, 21-23 November, Venice, ITALY

  20. Main interface of QRIOM ACS'08, 21-23 November, Venice, ITALY

  21. A Qualitative Model ACS'08, 21-23 November, Venice, ITALY

  22. ACS'08, 21-23 November, Venice, ITALY

  23. Causal graph generated during reasoning The ability to generate causal explanation has been one of the promises of the QR approach ACS'08, 21-23 November, Venice, ITALY

  24. ACS'08, 21-23 November, Venice, ITALY

  25. ACS'08, 21-23 November, Venice, ITALY

  26. Results discussion (I) • The approach enables prediction to be made, as well as causal explanation generation about theories of many chemical phenomena • Cause-effect chain can be explained by using only the ontological primitives of QPT ACS'08, 21-23 November, Venice, ITALY

  27. Results discussion (II) • The explanation generation is causal in nature and is run-time based (not pre-coded) • Model inspection and reasoning can help to enhance a learner’s critical thinking and reasoning ability ACS'08, 21-23 November, Venice, ITALY

  28. Conclusions (I) • The qualitative models in QRIOM communicated knowledge that is common to chemistry people (via the QPT constructs). • The new computational approach can serve as alternative learning technology in developing educational software for subjects that require application of domain knowledge at intuitive level. ACS'08, 21-23 November, Venice, ITALY

  29. Conclusions (II) • What the software can provide? • the software can predict final products • the software can explain its reasoning • no pre-coded solution path or search path • From a learner’s point of view • conceptual understanding is improved • reasoning ability is sharpened ACS'08, 21-23 November, Venice, ITALY

  30. Future works • To build a graphical interface that functions much as a protocol converter between • Reasoning shell • Graphical outputs • To include user modeling in the software • Towards an intelligent tutoring system ACS'08, 21-23 November, Venice, ITALY

  31. The End.Thank you GrazieTerima Kasih ACS'08, 21-23 November, Venice, ITALY

  32. Questions? Faculty members COIT building, UNITEN

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