Information technology in chemistry 0600-S1-ChK-IwCh
Lecture:
Acquisition of theoretical knowledge concerning the analysis and interpretation of experimental results, experimental design,simulation of chemical processes and perform simple numerical calculations.
Lecture topics:
1. University Study Support System (USOS) - properties and applications.
2. Introductory lecture (rules for passing the course, scope of material, laboratory tasks, literature, example of an exam test).
3. Introduction to statistical analysis of experimental data, significant digits (figures), statistical analysis of random error, the uncertainty of measurement, type B evaluation of uncertainty.
4. Propagation of uncertainty, type A evaluation of uncertainty, combined standard uncertainty.
6. Linear regression – least square method, weighted linear regression, analysis of residuals, linearizing transformations.
7. Nonlinear regression – polynomial equation fitting, multiple linear regression analysis, regression coefficients, selecting variables – stepwise procedures.
8. Numerical integration, integral and 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, GaussSeidel method, GaussJordan elimination method, NewtonRaphson 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
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.
Exercises with the use of appropriate computer programs (MS Excel, IBM SPSS) are concerned with the following issues:
Exercise no. 1. Statistical analysis of experimental data, the mean, standard deviation, dispersion measures - practical, classic problem-solving.
Exercise no. 2. Regression analysis, application of the linear regression to calculate the calibration curve - practical, classic problem-solving.
Exercise no. 3. Calculation of the pH of the two acids mixture - practical, classic problem-solving.
Exercise no. 4. Multiple linear regression - practical, classic problem-solving.
Exercise no. 5. Linear regression –linearizing transformation - practical, classic problem-solving.
Exercise no. 6. Numerical integration using the rectangular, trapezoidal, and Simpson’s rule methods - practical, classic problem-solving.
Exercise no. 7. Numerical solving of differential equations Euler, Runge – Kutta, Milne methods.
Exercise no. 8. Simplex optimization.
Total student workload
Learning outcomes - knowledge
Learning outcomes - skills
Learning outcomes - social competencies
Teaching methods
Observation/demonstration teaching methods
Expository teaching methods
- informative (conventional) lecture
Exploratory teaching methods
- laboratory
Type of course
Prerequisites
Course coordinators
Assessment criteria
Assessment methods:
written examination - K_W04, K_W05, K_U04, K_U05
laboratory - K_W04, K_W05, K_U04, K_U05
Assessment criteria:
Lecture: written examination, test: fail- 0÷49 %, satisfactory- 50÷60%, satisfactory plus- 61÷65%, good - 66÷75%, good plus- 76÷80%, very good- 81÷100%.
Laboratory: two tests fail- 0÷49 %, satisfactory- 50÷60%, satisfactory plus- 61÷65%, good - 66÷75%, good plus- 76÷80%, very good- 81÷100%.
Exam problems:
1. Introduction to statistical analysis of experimental data, significant digits (figures), statistical analysis of random error, uncertainty of measurement, type B evaluation of uncertainty, propagation of uncertainty, type A evaluation of uncertainty, combined standard uncertainty
2. Linear regression – least square method, weighted linear regression, analysis of residuals, linearizing transformations
3. Non-linear regression – polynomial equation fitting
4. Multiple linear regression analysis, regression coefficients, selecting variables – stepwise procedures
5. Numerical integration, integral and its geometric interpretation, rectangle method, trapezoidal method, Simpson’s rule method, Gauss–Legendre method.
6. Fundamentals of numerical solving of differential equations, Euler method, Runge–Kutta method, Milne method (predictor-corrector)
7. Methods for solving algebraic equations, bisection method, secant method (regula falsi), tangent method (Newton-Raphson)
8. Methods for solving systems of linear equations, Matrix calculus – fundamentals, Cramer method, GaussSeidel method, GaussJordan elimination method, NewtonRaphson method for nonlinear algebraic equations
9. Optimization methods, method of changing a single parameter, random walk method, grid search method (factorial design), rules for creating a regression model, experimental design, the simplex method, variable-size simplex, expansion, contraction, optimization criteria
10. Monte Carlo methods - integration and simulation, pseudorandom number generators, Monte Carlo integration, Monte Carlo simulation
Practical placement
not applicable
Bibliography
1. P. Szczepański, Chemistry IT, Toruń 2012.
2. M. Otto, Chemometrics. Statistics and computer application In analytical chemistry, WILEY-VCH, 2016.
3. Guide to the Expression of Uncertainty in Measurement, ISO, Switzerland 1995.
4. D.W. Rogers, Computational chemistry using the PC, John Wiley & Sons. Inc., 2003.
5. P. Gemperline, Practical guide to chemometrics, CRC Press, 2006.
6. J. N. Miller, J. C. Miller, Statistics and chemometrics for analytical chemistry, Pearson Education Limited, 2018.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: