Computer science in chemistry 0600-S1-O-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.
Lecture topics:
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
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.
Laboratory topics:
Exercise no. 1. Statistical analysis of experimental data, the mean, standard deviation, dispersion measures - practical, classic problem-solving.
Exercise no. 2. Statistical analysis of experimental data, dependence of the mean and measures of statistical dispersion on the number of samples - practical, classic problem-solving.
Exercise no. 3. Regression analysis, application of the linear regression to calculate the first-order reaction rate constant - practical, classic problem-solving.
Exercise no. 4. Calculation of the pH of the two acids mixture - practical, classic problem-solving.
Exercise no. 5. Multiple linear regression - practical, classic problem-solving.
Exercise no. 6. Linear regression –linearizing transformation - practical, classic problem-solving.
Exercise no. 7. Numerical integration, the rectangular, trapezoidal and Simpson’s rule method - practical, classic problem-solving.
Exercise no. 8. Numerical solving of differential equations Euler, Runge – Kutta, Milne methods - practical, classic problem-solving.
Exercise no. 9. Simplex optimization - practical, classic problem-solving.

Total student workload

Contact hours with teacher: - participation in lectures - 15hrs - consultations- 45 hrs Self-study hours: - preparation for lectures – 30 hrs - writing essays/ papers/ projects- 10 hrs - reading literature- 10 hrs - preparation for test/ examination- 40 hrs Altogether: 150 hrs (6 ECTS)

Learning outcomes - knowledge

W1: Knows and understands selected numerical methods used for chemical calculations K_W04 W2: Knows and understands the methods used to the analysis and interpretation of statistical data K_W05

Learning outcomes - skills

U1: Applies selected statistical methods to the analysis of experimental data K_U05 U2: Uses selected numerical methods to solve problems in the field of exact sciences K_U04

Learning outcomes - social competencies

K1: Is aware of the level of his knowledge and skills, as well as the need for continuous training and personal development K_K05 K2: Is aware of the need to conscientiously and accurately perform each task K_K03

Teaching methods

Lecture: Expository teaching methods: informative (conventional) lecture) Laboratory: Exploratory teaching methods: Practical, classic problem-solving

Observation/demonstration teaching methods

- display

Expository teaching methods

- informative (conventional) lecture
- discussion

Exploratory teaching methods

- practical
- laboratory
- classic problem-solving

Type of course

compulsory course

Prerequisites

MS Excel basic knowledge, mathematics at the secondary school level.

Course coordinators

Piotr Szczepański

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, GaussSeidel method, GaussJordan elimination method, NewtonRaphson method for nonlinear algebraic equations
9. Interpolation, Lagrange interpolation formula, differences and divided differences, Newton’s interpolation formula, numerical differentiation
10. 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
11. 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. T. E. Shoup, Applied numerical methods for the microcomputer, Prentice-Hall, Inc., Englewood Cliffs, New Jersey 1984.
3. M. Otto, Chemometrics. Statistics and computer application In analytical chemistry, WILEY-VCH, 2016.
4. R. G. Brereton, Chemometrics. Data analysis for the laboratory and chemical plant, John Wiley & Sons Ltd, 2003.
5. Guide to the Expression of Uncertainty in Measurement, ISO, Switzerland 1995.
6. D.W. Rogers, Computational chemistry using the PC, John Wiley & Sons. Inc., 2003.
7. P. Gemperline, Practical guide to chemometrics, CRC Press, 2006
8. P. C. Jurs, Computer software applications in chemistry, John Wiley & Sons. Inc., 1996.
9. 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:

Description of 0600-S1-O-IC in USOSweb