UCSD Chemistry 185/285 Class

Methods in Computational Chemistry

Introduction:

At the end of his Nobel address in 1966, Robert S. Mulliken stated: "In conclusion, I would like to emphasize my belief that the era of computing chemists, when hundreds if not thousands of chemists will go to the computing machine instead of the laboratory, for increasingly many facets of chemical information, is already at hand. There is only one obstacle, namely, that someone must pay for the computing time."

We now know that the computing power that Mulliken had available at the University of Chicago in the mid-1960's can be had for about the equivalent of one month's salary of an assistant professor! Twenty years later, Stephen Wilson, in his book 'Chemistry by Computer', paraphrased the first of Mullikens's two sentences as follows:

Today, the situation has been reached where, in many cases, the computational chemist can substitute the computing machine for the test tube. Not that the computational approach to the study of chemistry should be regarded as a rival to the traditional experimental techniques. Often the two approaches are complementary, one approach providing data which are not available from the other, and vice versa.

These two quotes provide a backdrop to our interest in computational chemistry in the curriculum. If computational chemistry is as important as stated by these two authors, it is certainly something that we will want to integrate into the curriculum.

Computational Chemistry:

* Rapidly growing field of science in which computer hardware and software are used to simulate a chemical process or to compute a chemical property.

* Makes use of the affordable technology to better understand the chemistry of a particular problem.

Theory <--> Experiment

* Exploring the unknown

One of the strengths of the computer is that it allows us to address important chemical concepts that are otherwise left untouched. Computational theoretical chemistry has become an equal partner with experimental chemistry in elucidating properties of molecules.

Example:

For some molecules such as the transient species that occur in combustion, or interstellar space, or as proposed reaction intermediates, frequently the only available data is obtained from theoretical calculations. An example of this type is ;the N3H3 moelcule, which, though isoelectronic with ozone (O3), cyclopropane, and cyclopropene, is totally unknown. Can it exist? Theory can tell us and provide the fingerprints to detect it.

* Complications of Experimental Procedures

In other cases, experimental data might exist, but the experiments might be very difficult to interpret without the benefit of coordinate thoeretical predictions. An example might be the cytosine molecule or other bases in DNA. Even something as simple as the IR spectrum is essentially uninterpretable, as its spectra is likely to be contaminated by energetically equivalent tautormers that occur easily and are important in the theory of point mutations. Consequently, by predicting its spectra and that of the tautomers with a reliable quantum mechanical method, we can interpret the spectra and use it to fingerprint the occurrence of point mutations.

* Economic Benefit

A great potential economic benefit occurs in areas like drug design. For drugs to be approved by the FDA, they must pass extensive anaimal tests to assess any potentially dangerous side effects. Yet, subjecting a drug to such tests is expensive. Hence, if a theoretical tool exists to screen out about ~1000 possibilities the ~20 or so with the best prospects for success, the cost for producing the drug can be reduced by a factor of ~50.

These are just a few examples where we use theoretical chemistry to our advantage. There are multiple examples where this computation chemistry/experimental chemistry partnership shows its usefullness. We should keep these and other examples in our mind as we go through this course.

Focus: What we can understand by making use of a model. Consequently, we must focus on the strengths and weaknesses of different theoretical models that might be applied to a given problem. We must present both the mathematical model and the use of the computer to implement that model.

Goal: Our goal is to provide one more tool, along with the various synthetic and analytical tools the chemist has, to gain additional insight into a particular chemical event or to predict new chemical events. These chemical events can range from synthesis of new molecules, to reaction of molecules, to interaction of electromagnetic radiation with matter, to dynamic motion of molecules.

Computational Areas to Study:

  • Molecular Mechanics
  • Quantum Chemistry
  • Molecular dynamics
  • Electrostatics
  • Reaction Dynamics

    Stages:

    A. Problem Identification

    B. Selection of Model

    Goal: Model should be useful as an additional tool along with various synthetic and analytical tools, to gain insight into a particular chemical event, or, to predict new chemical events.

    'Molecular Modeling'

    Components of Theoretical Chemistry: