Dear colleagues,

The beginning of the course included an introduction to the different typ=
es of computational chemistry calculations. I explained why computational c=
hemistry is so widely used across various scientific disciplines including =
materials science, chemistry, engineering, biochemistry, physics, etc. As e=
xamples of the widespread importance, I used the Nature 2014 article on the=
top 100 cited science articles (in which DFT articles were one of the cate=
gories mentioned) and the 1998 Nobel prize to Kohn and Pople for their work=
in computational chemistry. The difference between classical atomistic (e.=
g., molecular dynamics and Monte Carlo) calculations versus quantum chemist=
ry calculations (e.g., DFT) was explained. A brief background into Density =
Functional Theory was given, with students reading and writing brief (1-2 p=
age) highlights of a few introductory articles on DFT (e.g., the DFT "=
Jacob's ladder" article by Perdew et al.). After a few weeks of in=
troductory material, including a discussion of different classes of DFT fun=
ctionals, the students were taught how to carry out basic calculation types=
(e.g., ground state optimization, transition state optimization, frequency=
calculations, constrained geometry optimizations) in Gaussian software. Th=
e students were also taught the mechanics of computing the atomistic descri=
ptors using Chargemol software. The students completed a couple of homework=
assignments related to these topics, in which they performed calculations =
on different materials.

This Spring semester, =
I had the opportunity to teach an elective course called "Calculation =
of Material and Molecular Properties" to a few undergraduate and gradu=
ate students. Most of the students coming into the course had little or no =
background in computational chemistry. The course was focused on using quan=
tum chemistry (DFT mainly) to compute optimized geometries, infrared vibrat=
ional spectra, thermodynamic properties (enthalpies, free energies, entropi=
es, zero-point energies, and heat capacities), heats of reaction, transitio=
n states, and various atomistic descriptors (bond orders, net atomic charge=
s, atomic spin moments, etc.).=C2=A0

For the cours=
e, we primarily used two software packages: Gaussian 09/16 (for the quantum=
chemistry calculations) and Chargemol (to compute the atomistic descriptor=
s, using the Gaussian-generated wfx file as input).

After this, the students s=
elected a course project. The students could choose a topic of their choice=
, although I offered some guidance on which kinds of topics might be more s=
uitable for the length of time available. The topics spanned a diverse rang=
e including hypercoordinate molecules, dinuclear transition organometallic =
compounds, solid state crystalline materials, nuclear chemistry, boron and =
carbon containing molecules that exhibit unusual bonding, force field param=
eterization, thermodynamics of explosive materials, etc. All of the class p=
rojects were focused on computing things that were new, that no one had com=
puted the answers to before. Some of the class projects used Gaussian softw=
are, but others involved other software (RASPA, VASP, Matlab, etc.) Most of=
them used Chargemol to compute the atomistic descriptors.

We had a lot of fun exploring some unusual bonding properties of m=
aterials. In one of the class periods I posed an unsolved problem concernin=
g the structure of diazomethane. The goal was to calculate the sum of bond =
orders for each atom, and to use this information to determine whether the =
central nitrogen atom is hypercoordinate or not. What made this so fun was =
that I had never calculated it before, so I learned the answer together wit=
h the students. Many of these classes were held a computer lab where each s=
tudent had individual desktop computers, and the instructor had a computer =
that could be projected onto the main screen in front of the class.=C2=A0

I believe the students learned a lot. Some of the students commented in =
the course evaluations that it was one of the most useful elective courses =
they took. Things didn't go perfectly during the course, but overall th=
e students learned some valuable new skills. We didn't go into great de=
pth of the theory, but the students learned enough to be able to carry out =
meaningful calculations. On the course homepage, I posted journal =
articles describing the theoretical methods in more depth=C2=A0 in case som=
e of the students wanted to explore the details, but I didn't require t=
he students to read these. They were required to read some introductory jou=
rnal articles.

The students turned in three brief reports of their=
project: an early one describing the project to be studied which also incl=
uded a brief overview of related literature, a middle one describing initia=
l calculation results and any difficulties they encountered (some students =
had to change project at this point if their initial idea was not working),=
and a final brief written report. The students also gave a 10 minute oral =
powerpoint presentation at the end of their project.

I believe the ability to compute me=
aningful and reliable bond orders really enriched the course and made for s=
ome fun projects. Although bond order is a widely used concept, only recent=
ly has a reliable way to compute it been developed that works across an ext=
remely wide range of material types (
T. A. Manz,=C2=A0=E2=80=9CIntroducing DDEC6 atomic populati=
on analysis: part 3. Comprehensive method to compute bond orders,=
=E2=80=9D=C2=A0*RSC Advances*, 7 (=
2017) 45552-45581 (open access) http://doi.org/10.1039/c7ra07400j .
). It's great for a student who is just learning computational chemistr=
y to be able to take a molecule or system that no one knows the bond orders=
for and be able within a few days to calculate a meaningful answer for the=
first time. It really empowers them and makes them feel that they can do r=
esearch that is meaningful and cutting edge. There are millions of interest=
ing and novel materials for which the bond orders are unknown, and students=
can really get in on the ground floor. It's quite an opportunity.

--0000000000002c0e1105705710b1--
If any of you are teaching a computational chemistry c=
ourse during the next year, you may want to incorporate some of these ideas=
into your curriculum. If any of you have recently taught a computational c=
hemistry course, feel free to mention your experiences regarding what worke=
d well and what didn't.

Sincerely,

<=
br clear=3D"all">

Thomas Manz, PhD

Assistant Professor

http://wordpress.nmsu.edu/tmanz/

New Mexico State University

Department of Chemical & Materi= als=C2=A0Engineering

All About D= iscovery!