From owner-chemistry@ccl.net Fri Jul 6 15:43:00 2018 From: "Thomas Manz tmanz|,|nmsu.edu" To: CCL Subject: CCL:G: thoughts on computational chemistry course Message-Id: <-53372-180706122206-23838-7jq+J5JjBhj9qQY+IcucEw.@.server.ccl.net> X-Original-From: Thomas Manz Content-Type: multipart/alternative; boundary="0000000000002c0e1105705710b1" Date: Fri, 6 Jul 2018 10:21:58 -0600 MIME-Version: 1.0 Sent to CCL by: Thomas Manz [tmanz##nmsu.edu] --0000000000002c0e1105705710b1 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Dear colleagues, This Spring semester, I had the opportunity to teach an elective course called "Calculation of Material and Molecular Properties" to a few undergraduate and graduate students. Most of the students coming into the course had little or no background in computational chemistry. The course was focused on using quantum chemistry (DFT mainly) to compute optimized geometries, infrared vibrational spectra, thermodynamic properties (enthalpies, free energies, entropies, zero-point energies, and heat capacities), heats of reaction, transition states, and various atomistic descriptors (bond orders, net atomic charges, atomic spin moments, etc.). For the course, we primarily used two software packages: Gaussian 09/16 (for the quantum chemistry calculations) and Chargemol (to compute the atomistic descriptors, using the Gaussian-generated wfx file as input). The beginning of the course included an introduction to the different types of computational chemistry calculations. I explained why computational chemistry is so widely used across various scientific disciplines including materials science, chemistry, engineering, biochemistry, physics, etc. As examples 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 categories 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 chemistry calculations (e.g., DFT) was explained. A brief background into Density Functional Theory was given, with students reading and writing brief (1-2 page) 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 introductory material, including a discussion of different classes of DFT functionals, 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. The students were also taught the mechanics of computing the atomistic descriptors using Chargemol software. The students completed a couple of homework assignments related to these topics, in which they performed calculations on different materials. After this, the students selected 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 suitable for the length of time available. The topics spanned a diverse range including hypercoordinate molecules, dinuclear transition organometallic compounds, solid state crystalline materials, nuclear chemistry, boron and carbon containing molecules that exhibit unusual bonding, force field parameterization, thermodynamics of explosive materials, etc. All of the class projects were focused on computing things that were new, that no one had computed the answers to before. Some of the class projects used Gaussian software, 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 materials. In one of the class periods I posed an unsolved problem concerning 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 with the students. Many of these classes were held a computer lab where each student had individual desktop computers, and the instructor had a computer that could be projected onto the main screen in front of the class. The students turned in three brief reports of their project: an early one describing the project to be studied which also included a brief overview of related literature, a middle one describing initial 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 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 the students learned some valuable new skills. We didn't go into great depth 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 in case some of the students wanted to explore the details, but I didn't require the students to read these. They were required to read some introductory journal articles. I believe the ability to compute meaningful and reliable bond orders really enriched the course and made for some fun projects. Although bond order is a widely used concept, only recently has a reliable way to compute it been developed that works across an extremely wide range of material types ( T. A. Manz, =E2=80=9CIntroducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders,=E2=80=9D *RSC Advances*, 7 (20= 17) 45552-45581 (open access) http://doi.org/10.1039/c7ra07400j . ). It's great for a student who is just learning computational chemistry 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 research that is meaningful and cutting edge. There are millions of interesting 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. If any of you are teaching a computational chemistry course 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 chemistry course, feel free to mention your experiences regarding what worked well and what didn't= . Sincerely, Thomas Manz, PhD Assistant Professor tmanz a nmsu.edu http://wordpress.nmsu.edu/tmanz/ New Mexico State University Department of Chemical & Materials Engineering All About Discovery! --0000000000002c0e1105705710b1 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Dear colleagues,

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).

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.

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

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 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.

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=A0RSC 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.

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

tmanz a nmsu.edu

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

New Mexico State University

Department of Chemical & Materi= als=C2=A0Engineering

All About D= iscovery!


--0000000000002c0e1105705710b1--