Introduction to Computational Chemistry PDF

Introduction Dr. Satyam Ravi Email: [email protected] Ph. No. 9646937054 Overview of the course Course Name: Introduction to Computational Chemistry Course Code: CHY1005 LTP Course: Lecture, Tutorial, and Practical 4 Credits; 3 Sessions per week Course structure Module 1: In...

Introduction Dr. Satyam Ravi Email: [email protected] Ph. No. 9646937054 Overview of the course Course Name: Introduction to Computational Chemistry Course Code: CHY1005 LTP Course: Lecture, Tutorial, and Practical 4 Credits; 3 Sessions per week Course structure Module 1: Introduction: Overview of the course, history and promises of computational chemistry, tools for computational chemistry, units, errors in computed quantities. Module 2: Quantum Chemistry: Historical development, Bohr’s atomic model, de Broglie wavelength, Heisenberg uncertainty principle, Schrodinger equation, Wave function, particle in a box, Hydrogen atom, radial and angular solution to hydrogen atom, applications and limitations. Module 3: Thermodynamics: Intensive and extensive variables, state and path functions, Laws of Thermodynamics (First law and enthalpy; second law and entropy, spontaneity, and equilibrium; third law and absolute entropy) free energy, Gibbs and Maxwell's relations, Ideal and real gases. Course structure Module 4: Potential Energy Surfaces: Chemical bonds and intermolecular interactions, Types of intermolecular interactions (charge distribution of isolated molecules, electrostatic interaction, induction interaction, London or dispersion forces, hydrogen bonding, repulsive interaction, relative contribution of different terms), representing the potential energy surfaces (pair additivity, rare gas), intramolecular interactions (bond stretching, angle bending, torsional and improper terms). Module 5: Molecular Dynamics: Introduction to ensembles, force fields, integration of Newton’s laws of motion, force calculation, energy minimization, periodic boundary conditions, choice of input configuration, velocities, and time- step, applications, and calculation of simple thermodynamic variables. Guest Lectures Overview of Computational Science Computational science is the application of computational and numerical techniques to solve large and complex problems. Overview of Computational Chemistry Computational chemistry is simply the application of chemical, mathematical and computing skills to the solution of interesting chemical problems. Useful way to investigate materials that are too difficult to find or too expensive to purchase. Make predictions before running the actual experiments. Schrödinger equation is the basis. Questions commonly investigated computationally are Molecular geometry: the shapes of molecules – bond lengths, angles and dihedrals. Energies of molecules and transition states: this tells us which isomer is favored at equilibrium, and (from transition state and reactant energies) how fast a reaction should go. Questions commonly investigated computationally are Grambow, C.A., Pattanaik, L. & Green, W.H. Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry. Sci Data 7, 137 (2020). Chemical reactivity: for example, knowing where the electrons are concentrated (nucleophilic sites) and where they want to go (electrophilic sites) enables us to predict where various kinds of reagents will attack a molecule. Overview of Computational Chemistry Questions commonly investigated computationally are IR, UV and NMR spectra: these can be calculated, and if the molecule is unknown, someone trying to make it knows what to look for. Questions commonly investigated computationally are The interaction of a substrate with an enzyme: seeing how a molecule fits into the active site of an enzyme is one approach to designing better drugs. Drug discovery to fight COVID-19: Deep learning paired with drug docking and molecular dynamics simulations. Questions commonly investigated computationally are The physical properties of substances: these depend on the properties of individual molecules and on how the molecules interact in the bulk material. For example, the strength and melting point of a polymer (e.g. a plastic) depend on how well the molecules fit together and on how strong the forces between them are. Tools of Computational Chemistry Two approaches (general): (a) Quantum Mechanical Methods: numerical computation of molecular electronic structures by density functional methods, ab initio and semi-empirical techniques. (b) Classical Computational Methods: formulation of analytical expressions for the properties of molecules and their reactions. Molecular Mechanics/Molecular Dynamics/Monte – Carlo Methods. Tools of Computational Chemistry Advantages Disadvantages Best for Hybrid Methods: QM/MM Tools of Computational Chemistry Molecular mechanics – based on a ball-and-springs model of molecules. Ab initio methods – based on approximate solutions of the Schrodinger equation without appeal to fitting to experiment. Semiempirical methods – based on approximate solutions of the Schrodinger equation with appeal to fitting to experiment (i.e. using parameterization). Density functional theory (DFT) methods – based on approximate solutions of the Schrodinger equation, bypassing the wavefunction that is a central feature of ab initio and semiempirical methods. Molecular dynamics methods study molecules in motion. Length and time scales Conducting a computational project  These questions should be answered ⚫ What do you want to know? ⚫ How accurate does the prediction need to be? ⚫ How much time can be devoted to the problem? ⚫ What approximations are being made?  The answers to these questions will determine the type of calculation, model to be used.  History and evolution of Computational Chemistry 1925: Heisenberg publishes his first paper on quantum mechanics (Z. Phys., 1925, 33, 879). 1926: Schrödinger publishes his first paper on the theory of quantum mechanics (Ann. Phys., 1926, 79, 361). 1931: Pi electron theory postulated by Hückel (Z. Phys., 1931, 70, 204). 1943: The first computer, the ENIAC (Electronic Numerical Integrator and Computer) is built for the US Army Ordnance Department. 1951: The first UNIVAC (Universal Automatic Computer) is delivered to the Census Bureau. 1953: Metropolis and co-workers describe the application of the Monte Carlo method of simulation to physical chemistry problems (J. Chem. Phys., 1953, 1087, 21).  History and evolution of Computational Chemistry 1955: Scherr reports the first ab initio calculation for a large system, N2. 1956: Alder and Wainwright –molecular dynamics (MD) simulation of hard spheres. 1957: Pople publishes details on the application of self-consistent molecular orbital methods to pi electrons (J. Phys. Chem., 1957, 61, 6). 1958: The first integrated circuit board is constructed by Jack Kilby at Texas Instruments. 1961: Hendrickson publishes the results of calculations of relative conformational stabilities of cyclohexane (J. Amer. Chem. Soc., 1961, 83, 5537). 1964: Hansch and Fujita describe a new approach to analyzing drug actions: QSAR, a quantitative structure activity relationship (Hansch, C and Fujita, T., J. Amer. Chem. Soc., 1964, 86, 1616). 1964: Rahman–MD simulation of liquid Ar  History and evolution of Computational Chemistry 1966: Cyrus Levinthal, et. al. publish paper on the use of molecular graphics and computer simulation (Levinthal, C.; Scientific American, 1966, 214: 42). 1969: Levitt and Lifson report the use of force fields to refine protein conformations derived from experimental data (Michael Levitt and Shneior Lifson; J. Mol. Biol., 1969, 46, 269- 279). Ken Thompson, Dennis Ritchie and Joseph Ossanna develop a new operating system, UNIX, for the DEC PDP-7. Dennis Ritchie and Brian Kernighan create “C” at Bell Labs. 1971: Rahman and Stillinger–MD simulation of water. 1973: The Brookhaven Protein Data Bank is announced (Acta. Cryst. B, 1973, 29: 1746); N.L. Allinger describes the modeling of hydrocarbons with a new force field, MM1 (Allinger, N.L.; Sprague, J.T.; J. Amer. Chem. Soc., 1973, 95: 3893).  History and evolution of Computational Chemistry 1975: Microsoft is founded by Bill Gates and Paul Allen. 1977: Martin Karplus, et. al. publish the first molecular dynamics study of a protein (McCammon, J.A.; Gelin, B.R.; Karplus, M.; Nature, 1977 267: 585-590). 1980: The first issue of the Journal of Computational Chemistry is published. 1981: AMBER Force-field for proteins/DNA calculations; IBM introduces its Personal Computer to the market. 1982: An algorithm for docking small molecules to receptors (later to become the DOCK program) is published by Irwin Kuntz and colleagues (Kuntz, I.D.; Blaney, J.M.; Oatley, S.J.; Langridge, R.; Ferrin, T.E.; J. Mol. Biol., 1982, 161, 269).  History and evolution of Computational Chemistry 1983: Martin Karplus, et. al. publish description of the CHARMM program (Brooks, B.R.; Bruccoleri, R.E.; Olafson, B.D.; States, D.J., Swaminathan, S. and Karplus, M.; J. Comp. Chem., 1983, 4:187-217); W. F. van Gunsteren, et. al. publish description of molecular dynamics of proteins using GROMOS program (van Gunsteren, W. F.; Berendsen, H. J. C.; Hermans, J.; Hol, W. G. J.; Postma, J. P. M.; Proc. Natl. Acad. Sci., 1983, 80: 4315); The Compact Disk (CD) is launched. 1983 – Till now: A lot of developments!! (Not mentioned here.) NOBLE PRIZES: 1998: Walter Kohn, "for his development of the density-functional theory", and John Pople, "for his development of computational methods in quantum chemistry", received the 1998 Nobel Prize in Chemistry. 2013: Martin Karplus, Michael Levitt and Arieh Warshel received the 2013 Nobel Prize in Chemistry for "the development of multiscale models for complex chemical systems". Introduction: Measurements Dr. Satyam Ravi Measurements in everyday life Physical quantity Types of physical quantities Units of measurement Seven fundamental & two supplementary units SI prefixes and some practical units for measuring length Dimensions of a physical quantity Accuracy and precision of measurement Significant figures Rules for counting significant figures Exact numbers Rules for rounding off a measurement Significant figures in calculations Questions 1. How many Significant figures in each term? a. 34.6209 b. 0.003048 c. 5010.0 d. 4032.090 2. Solve the following equations using the correct number of significant figures. a. 0.003 + 3.5198 + 0.0118 b. 6.90 / 2.8952 c. 98.1 x 0.03 d. 1.40 x 103 3. A meter has 1000 millimeters in it. How many significant figures does this number have? Errors in measurement Error analysis Error analysis  Combination of errors When X = A ± B When X = A × B or A / B When X = An Standard deviation and standard error of the mean The standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. Question 1) The marks of a class of eight students (that is, a statistical population) are the following eight values: 2, 4, 4, 4, 5, 5, 7, 9. Calculate mean, absolute error, mean absolute error, relative error, percentage error, variance, standard deviation, and standard error. Thank you