COMPUTATIONAL CHEMISTRY

CARLOS J. CABELLO LÓPEZ

NICOLE M. CRUZ REYES

STEPHANNIE ROSARIO GARRIDO

ADRIANA N. SANTIAGO RUIZ

PROF. DALVIN MÉNDEZ

UNIVERSITY OF PUERTO RICO IN CAYEY

DEPARTMENT OF BIOLOGY

RISE PROGRAM

CONTENT• Introduction:

• Definitions• Concepts• Programs Definition

• Problem & Hypothesis• Methodology & Results• Discussion:

• What do our results mean?• Comparisons• New Application

• Conclusion• Cited Literature

INTRODUCTION

COMPUTATIONAL CHEMISTRY

• Computational Chemistry is a branch of chemistry that uses the product of theoretical chemistry that is translated into computational programs to calculate molecular properties and its changes and also to perform simulation to macromolecular systems. (Rusdi 2007)

BASIC CONCEPTS• Chemical compounds Electronegativity

• Molecular compounds• Ionic compounds

• Polarity• Redox Potential• LUMO/HOMO

M+ NM-

SCHRÖDINGER’S EQUATION

• H=Hamiltonian operator, total energy of the electron within the atom

• E=Actual energy of the electron

• Ψ=wave function, describes the wavelike nature of the electron (Tro 2014).

By modifying H, various molecular properties can be studied.

Hψ = Eψ

PROGRAMS• Gabedit (ORCA)• Excel• R Studio

OBJECTIVES • Be able to obtain a knowledge about computational chemistry,

its relevance and application’s in science

• Learn to use the Gabedit program

• Calculate the redox potential of certain molecules

• Assigned • Created• Modified

• Analyze the final data using R Studio

PROBLEM

If the molecules are correctly drawn and the proper chemical group was added to each model, will the experimental values of the

redox potentials match the postulate values?

HYPOTHESIS

By analyzing the obtained data of the molecular models with the Gabedit (ORCA) program, and using the R Studio to create a graph of the results, the Redox potentials

will match their postulate values.

+ DISCUSSION

METHODOLOGY &

RESULTS

PROCEDURESCalculation of dipole

moments with Gabedit

• Results –Molecules calculated

Design of new molecules and prediction of

redox potentials

• Results –Molecules designed

Comparison of predictions with actual

results

• Results

Using R Studio

PROCEDURE #11. Draw the molecule in Gabedit (this is mainly visual)2. Close the tab where the molecule is drawn 3. Click on “ORCA” 4. Write an input file

a. Copy the results the program gives b. Paste the results in any text-editor program (TextEdit (Mac), Notepad

(Windows), there are many options to choose from)i. Use the correct format to write before and after the results pasted

c. Save the document with “.inp” 5. Write the “sh” file

a. With an sh file already written change the title of the files being used b. Make sure the names are written correctly since those files will be used

by the Super Computer to produce the final file6. Use the files to reproduce the results using a Super Computer

RESULTS #1

0 1 2 3 40

0.5

1

1.5

2

2.5

Dipole Moment vs # Cl Atoms

expHFDFTnormalized guess

# of Cl

Dipo

le M

omen

t (De

bye)

ASSIGNED MOLECULES

FILES

Input file

“Sh” files

PROCEDURE #21. Choose a molecule (or create a completely new one)2. Add an Electron Donating Group or an Electron

Withdrawing Group   3. Make a prediction of the redox potential4. Compare the prediction with the results obtained

RESULTS #2• Explain problem

RESULTS #2

PROCEDURE #31. Use the theoric results from the paper titled “Building and

testing correlations for the estimation of one-electron reduction potentials of a diverse set of organic molecules.” (Méndez-Hernández et al. 2014)

2. Make a table with the theoric and the experimental results obtained from procedure #1.

3. Compare and analyze the values on the table

RESULTS #3

Table 4: Results of Dipole Moments (used for Graph 2)

Name Molecule LUMO E

(eV) red. Potential

(V) Adriana N. Santiago-Ruiz 1 -2.0457 -1.9 Adriana N. Santiago-Ruiz 2 -2.5354 -1.6

Adriana Vera-Rios 3 -3.2039 -1.1 Adriana Vera-Rios 4 -3.2977 -0.95

Carlos Cabello-Lopez 5 -3.9205 -0.75 Carlos Cabello-Lopez 14 -3.0212 -0.8

Carlos Villagrasa-Mendez 15 -3.1292 -0.75 Carlos Villagrasa-Mendez 16 -3.2487 -0.63

Cristina Rivera-Quiles 17 -3.3917 -0.58 Cristina Rivera-Quiles 18 -3.5354 -0.47

Edmaritz Hernandez-Pagan 19 -3.7898 -0.34 Edmaritz Hernandez-Pagan 20 -4.0163 -0.18 Emmanuel Santiago-Burgos 21 0 Emmanuel Santiago-Burgos 22 -4.1968 0.02 Gabriel Pastrana-Castellanos 23 -4.2725 0.05 Gabriel Pastrana-Castellanos 24 -4.8039 0.28

Joanly Rivera-Ortiz 25 -5.0953 0.59 Joanly Rivera-Ortiz 26 -5.767 0.9 Laura Diaz-Mendez 39 -1.4753 -2.1 Laura Diaz-Mendez 40 -1.4425 -2.15

Marangely D Martinez-Justiniano 42 -1.3747 -2.2

Marangely D Martinez-Justiniano 43 -1.1817 -2.22

Natalia D Rivera-Sanchez 44 -1.8144 -2.4 Natalia D Rivera-Sanchez 69 -0.613 -2.636

Nicole Cruz-Reyes 72 -1.4182 -2.08 Nicole Cruz-Reyes 45 -0.9618 -2.66

Rafael J Cummings-Lopez 48 -1.6345 -2.1 Rafael J Cummings-Lopez 6 -1.8068 -1.98

Stephannie M Rosario-Garrido 7 -1.8144 -1.96 Stephannie M Rosario-Garrido 8 -1.8867 -1.88

Valeria Laboy-Collazo 9 -2.7621 -1.27 Valeria Laboy-Collazo 49 -0.9959 -2.62

GRAPH (EXCEL)

THE “HOLISTIC” TABLE

PROCEDURE #41. Import Excel file with the organized collected data

a. Important: choose ‘x’ and ‘y’ columns correctly2. Use the command in order to tell the program to read all the data needed 3. Create a linear model with the imported data4. Create a column with the predicted values using the explanatory variable

(le)5. Calculate the error stats

a. In order to obtain the most accurate amount of data6. Create the plotting graph with the imported data

a. It is possible to customize the graph with colors7. Calculate the Root Mean Standard Deviation (RMSD) and the Mean

Absolute Deviation (MAD)8. Label the axis with the corresponding titles based on the data being

graphed9. Export graph and save the file

RESULTS GRAPHED WITH R

COMPARISON

VS

CONCLUSION• We were able to obtain a knowledge about computational

chemistry, its relevance and application’s in science

• Learned to use the Gabedit program • Calculated the redox potential of certain molecules

• Assigned • Modified

• Redox potential matched the reported values• Hypothesis was proven

• Analyzed the final data using R Studio

• Benefits of using the programs

APPLICATIONS• Medical applications

• What is the benefit of making a drug harder to oxidize?

• Metabolism of drugs

(Vogel & Catherine)

CITED LITERATURELynch E, Speelman A, Curry B, Murillo C, Gillmore J. 2012. Expanding and

Testing a Computational Method for Predicting the Ground State Reduction Potentials of Organic Molecules on the Basis of Empirical Correlation to Experiment. ACS. 77(15):6423–6430.

 

Méndez Hernández D, Gusta D, Moorea T, Gillmorea J, Montano L, Moore A, Mujica, V. 2015. Building and testing correlations for the estimation of one-electron reduction potentials of a diverse set of organic

molecules. Physical Organic Chemistry. 28(5):320–328.

 

Reece J, Urry L, Cain M, Wasserman S, Minorsky P, Jackson R. 2014. Campbell Biology. 10th ed. Glenview, IL: Pearson Education. 28-56 p.

Rusdi, R. Basic definition of computational chemistry [Internet]. [Updated 2007 February 9]. [cited 2016 May 4]. Available from: https://

stalischem.wordpress.com/2007/02/09/basic-definition-of-computational-chemistry/

ACKNOWLEDGEMENTS• RISE Program

• Dr. Díaz• Dr. Ross• Dr. Bansal

• Special thanks to…• Dr. Méndez

QUESTIONS?