Methods of Computational Science
ACS/CHE/PHY 289.04 Spring 1995 MWF 1-1:50 p.m. at Moulton 202
Office: Moulton Hall 313B
Office hours: 2 - 3 p.m. (MTWRF) or by appointment.
Office: Felmley Hall 331A
Office hours: 10 a.m. - 12 p.m. (TR) or by appointment.
Gregory L. Baker and Jerry P. Gollub, "Chaotic dynamics: an introduction,"
Cambridge University Press.
Harvey Gould and Jan Tobochnik, "Computer Simulation Methods, Applications
to Physical Systems, Part 1 and Part 2," Addison-Wesley.
Rapid progress in computer technology in recent years has allowed both chemists
and physicists to explore complex systems such as nonlinear chaotic dynamical
systems and many body systems including macromolecules, liquids, glasses, and
solids. Computational methods used in studies of these systems are now becoming
an integral part of chemistry and physics.
It is therefore beneficial for those in chemistry and physics to acquire
skills in applying these computational techniques to problems in their disciplines.
Applied computer scientists are also increasingly involved in application
of these techniques in both academic and industrial environments. It is then
a definite advantage for those in applied computer science to be familiar
with these computational methods.
The aim of this course is to help you (i.e., majors in Applied Computer
Science, Chemistry, and Physics) learn how to solve problems in chemistry
and physics through computer modeling and simulations. The course covers a
variety of basic algorithms for numerical solution of Newton's equations of
motion and chemical kinetics equations, molecular dynamics simulations, and
Monte Carlo simulations. These computational techniques are introduced via
specific applications to chemical and physical problems in order for you to
develop the computational skills and scientific intuition required to tackle
1. To understand how to approach problems in chemistry and physics by computer
modeling and simulations of real systems.
2. To understand basic algorithms for numerical solution of Newton's equations
of motion and chemical kinetics equations, molecular dynamics simulations of
simple liquids and solids, and Monte Carlo simulations of phase transitions.
3. To be able to assess the advantages and disadvantages of each algorithm for
problems in chemistry and physics.
4. To be able to implement these algorithms as computer programs.
5. To apply these algorithms to studies of specific chemical and physical systems.
6. To analyze the results produced by these algorithms by comparing with known
analytical solutions and limiting cases, by applying scientific intuition, and
through computer visualization.
Recommended references include the following:
1. William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling,
"Numerical Recipes - The Art of Scientific Computing," Cambridge University
2. Alejandro L. Garcia, "Numerical Methods for Physics," Prentice Hall.
3. Paul L. DeVries, "A First Course in Computational Physics," John Wiley &
4. Steven E. Koonin and Dawn C. Meredith, "Computational Physics Fortran
5. "Introduction to the ISU Physics Department Computer System", PIP Printing.
(1) A homework problem will be assigned usually as soon as a topic relevant
to the problem is discussed in class.
(2) Present your solutions neatly and clearly. Illegible and/or
disorganized solutions will not be graded.
(3) Solutions will be available in a file in the Physics Department
office and in a case outside Felmley 333 in the Chemistry Department.
(4) Late homework will lose points every day, and will not be accepted after
the solutions become available.
(1) There will be 2 hour exams.
(2) No make-up exam will be given unless you can present an official
proof for an uncontrollable circumstance such as serious illness of yourself
or in your immediate family. An arrangement for a make-up exam must be made
within one week after the exam. If you fail to do so, you will not be able
to take a make-up exam.
Final Exam At 1:00 p.m. on Wednesday, May 10.
to Computational Science Courses