A Simulator For Explaining Organic Reactions Through Qualitative Reasoning

Introduction (I) We implemented qualitative reasoning based on QPT ontology in a software tool (QRiOM) The main educational goals of the tool are that: –The students will undergo mental change so that they are able to explain chemical phenomenon in a more elaborated way –The students would benefit from it in terms of improving their reasoning skills and enhancing their understanding of the organic processes

Introduction (II) The main focus of the design of QRiOM is on the generation of explanation (causal and behavioral aspects of organic reactions) The results of a simulation is presented in five main forms: –Names of the final product (most stable form), and organic mechanism used to predict the output –QPT processes (for inspection) –Causal diagram for tracing parameter dependency during a make-bond/break-bond organic process –The entire reaction route (from initial substrate to the final products) –Reacting species (view pairs) used in each reaction step and their chemical parameters’ states change

This module checks user selection, and returns the “type” of the inputs as either a nucleophile or an electrophile. From here, an organic reaction may be determined This module will automate the construction of a process model using QPT modeling constructs The actual reasoning and simulation starts here. The task is handled by several sub- modules. These include QSA and MUR This module will generate explanation to justify the simulated result Functional components of QRiOM Functional components of QRiOM

Main interface of QRiOM simulator prototype

ABC More learning activities and explanation can be viewed by clicking A, B and C buttons Input selection Predicted products

Learners may inspect the automated models QPT processes QPT model inspection page

Brief explanation of QPT slots is provided Check how two parameters are related in a given bond activity

The series of organic reactions occurred are stored in special purpose data structures during reasoning in order to generate causal graph; like this one Causal diagram inspection page

This button will display the pop-up window to tell the user how to read the causal diagram

More snapshots are provided for learners to examine the chemical parameter states’ changes The entire reaction route is displayed after a simulation is completed

Basic properties of each atom involved in a reaction are recorded for user inspection More learning activities and explanation

Learners can select any reacting species to study their parameter history (from the first reaction step until the entire simulation is ended) During simulation each chemical state change is recorded

Contents of the View Structure give the pairs of reacting species used in each small reaction step

Editor page for adding/deleting chemical facts and theories

Some organic chemistry terms are also provided; the page can be accessed from the main interface

Examples used are: Reasoning cases Organic Mechanisms = 2 (S N 1 and S N 2) General Reaction formulas= 3 S N 1 Tertiary alcohol + Hydrogen halide (CH3)3COHHX CH3CH3CH3COH+ HF CH3CH3CH3COH+ HCl CH3CH3CH3COH+ HBr CH3CH3CH3COH+ HI CH3CH3CHOH+ HF CH3CH3CHOH+ HCl CH3CH3CHOH+ HBr CH3CH3CHOH+ HI Alkyl halide (tertiary)+ Water molecules (CH3)3CX2H2O (in excess) (CH3)3CF+2H2O (CH3)3CCl+2H2O (CH3)3CBr+2H2O (CH3)3CI+2H2O

S N 2 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-

Other reaction formulas tested are: 1. CH3CH3CH3COH+HO- 2. CH3CH3CH3COH+ H2O 3. CH3OH+HO- 4. CH3OH+H2O 5. CH3CH3CHOH+ HO- 6. CH3CH3CHOH+ H2O 7. CH3CH2X+H2O 8. CH3X+H2O 9. CH3CH2X+X- 10. CH3X+X- 11. CH3CH2CH2CH2X+H2O 12. CH3CH2CH2CH2X+HO- 13. CH3CH2CH2X+H2O 14. CH3CH2CH2X+HO- 15. (CH3)3CX+HO- 16. (CH3)3CX+X-

Conclusion (I) The simulation has been successfully tested with positive results –The QR approach enables prediction to be made, as well as causal explanation generation about theories of a number of organic chemistry phenomena –Cause-effect chain can be explained by using only the ontological primitives of QPT A study of learners’ feedback using the software was carried out –Overall, there was general understanding that the new means of learning through qualitative simulation and explanation had proved valuable

Conclusions (II) The software –can predict final products –can explain its reasoning Unlike other chemistry software, QRiOM is implemented without having any pre-coded solution path in the knowledge base From a learner’s point of view –conceptual understanding is nurtured –reasoning ability is improved

Alicia Tang (University of Tenaga Nasional, MALAYSIA) Tel: Sharifuddin Mohd. Zain (Dept. of Chemistry, Malaya University) Noorsaadah Abd. Rahman (Dept. of Chemistry, Malaya University) Rukaini Abdullah (Dept. of AI, Malaya University)