Informatics in chemistry (+USOS) 0600-S1-EN-IC

Lecture:
Acquisition of theoretical knowledge concerning the analysis and interpretation of experimental results, experimental design,simulation of chemical processes and perform simple numerical calculations.
Laboratory
Acquisition of practical skills to apply of computers in the analysis and interpretation of experimental results, experimental design, simulation of chemical processes and perform simple numerical calculations.
In the case of laboratory, it is necessary to have a basic knowledge about MS Excel.

List of topics:
Lecture:
1. Introductory lecture
2. USOS training
3. Introduction to statistical analysis of experimental data, significant digits (figures), statistical analysis of random error, uncertainty of measurement, type B evaluation of uncertainty,
4. Propagation of uncertainty, type A evaluation of uncertainty, combined standard uncertainty
5. Linear regression – least square method, weighted linear regression, analysis of residuals, linearizing transformations
6. Non-linear regression – polynomial equation fitting
7. Multiple linear regression analysis, regression coefficients, selecting variables – stepwise procedures
8. Numerical integration, integral and its geometric interpretation, rectangle method, trapezoidal method, Simpson’s rule method, Gauss–Legendre method.
9. Fundamentals of numerical solving of differential equations, Euler method, Runge–Kutta method, Milne method (predictor-corrector)
10. . Methods for solving algebraic equations ,bisection method, secant method (regula falsi), tangent method (Newton-Raphson)
11. Methods for solving systems of linear equations, Matrix calculus – fundamentals, Cramer method, GaussSeidel method, GaussJordan elimination method, NewtonRaphson method for nonlinear algebraic equations
12. Interpolation, Lagrange interpolation formula, differences and divided differences, Newton’s interpolation formula, numerical differentiation
13. Optimization methods, method of changing a single parameter, random walk method, grid search method (factorial design), rules for creating a regression model, experimental design,
14. Simplex method, variable-size simplex, expansion, contraction, optimization criteria
15. Monte Carlo methods - integration and simulation, pseudorandom number generators, Monte Carlo integration, Monte Carlo simulation.

Laboratory:
Exercise no. 1. Statistical analysis of experimental data, the mean, standard deviation, dispersion measures.
Exercise no. 2. Statistical analysis of experimental data, dependence of the mean and measures of statistical dispersion on the number of samples.
Exercise no. 3. Regression analysis, application of the linear regression to calculate the first-order reaction rate constant
Exercise no. 4. Calculation of the pH of the two acids mixture
Exercise no. 5. Multiple linear regression
Exercise no. 6. Linear regression –linearizing transformation
Exercise no. 7. Numerical integration, the rectangular, trapezoidal and Simpson’s rule method
Exercise no. 8. Numerical solving of differential equations Euler, Runge – Kutta, Milne methods
Exercise no. 9. Simplex optimization