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

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

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

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

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

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

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

8. ACS'08, 21-23 November, Venice, ITALY Organic Reactions & Mechanisms A reaction mechanism describes the 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.

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

10. ACS'08, 21-23 November, Venice, ITALY 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 S N 1 and S N 2.

11. QRIOM: Functional Components

12. ACS'08, 21-23 November, Venice, ITALY Reaction examples Reasoning cases Mechanisms = 2 Reaction formulas= 3 Specific cases of simulation = 28 SN1 Tertiary alcohol + Hydrogen halide (CH3)3COHHX CH3CH3CH3COH+ HF CH3CH3CH3COH+ HCl CH3CH3CH3COH+ HBr CH3CH3CH3COH+ HI Alkyl halide (tertiary)+ Water molecules (CH3)3CX2H2O (in excess) (CH3)3CF+2H2O (CH3)3CCl+2H2O (CH3)3CBr+2H2O (CH3)3CI+2H2O

13. ACS'08, 21-23 November, Venice, ITALY SN2 Alkyl Halide (primary) + Incoming nucleophile CH3CH2XHydroxyl 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-

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

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

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

17. Qualitative Simulation Top Level Design See next slide

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

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

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

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

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

23. The ability to generate causal explanation has been one of the promises of the QR approach Causal graph generated during reasoning

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

26. 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 Results discussion (I)

27. ACS'08, 21-23 November, Venice, ITALY 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 Results discussion (II)

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

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

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

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

32. Questions? COIT building, UNITEN Faculty members