Today I want to engage in some shameless self-promotion and highlight how cctk , an open-source Python package that I develop and maintain with Eugene Kwan, can make conformational searching easy. Conformational searching is a really crucial task in computational chemistry, because pretty much everything else you do depends on having the correct structure in the computer.
Since my previous “based and red pilled” post seems to have struck a nerve, I figured I should address some common objections people are raising. Although this is obvious, I wanted to preface all of this by saying: this is my opinion, I'm not some expert on systems of science, and many of the criticisms come from people with much more scientific and institutional expertise than me. It's very possible that I'm just totally wrong here!
In the previous post, I discussed how data associated with two of the candidates for molecules of the year – 2022 could be retrieved and then used to inspect their three dimensional structures.
13 C NMR is, generally speaking, a huge waste of time. This isn’t meant to be an attack on carbon NMR as a scientific tool; it’s an excellent technique, and gives structural information that no other methods can. Rather, I take issue with the requirement that the identity of every published compound be verified with a 13 C NMR spectrum. Very few 13 C NMR experiments yield unanticipated results.
The list of molecules of the year is out now at C&E News (but you have to have an account to view the list, unlike previous years). ♣ These three caught my eye: Electron in a cube: Synthesis and characterization of perfluorocubane as an electron acceptor ,[cite]10.1126/science.abq0516[/cite]. I have already written about this system and will not discuss it further, except to note this one topped the poll!
One of the more thought-provoking pieces I read last year was Alex Danco’s post “Why the Canadian Tech Scene Doesn’t Work,” which dissects the structural and institutional factors that make Silicon Valley so much more effective at spawning successful companies than Toronto.
Modeling ion-pair association/dissociation is an incredibly complex problem, and one that's often beyond the scope of conventional DFT-based techniques. (This study is a nice demonstration of how hard modeling ion-pairing can be.) Nevertheless, it's still often important to gain insight into what the relative energy of solvent-separated ion pairs and contact-ion pairs might be; are solvent-separated configurations energetically accessible or not?
The topic of dicarbon, C 2 , has been discussed here for a few years now. It undoubtedly would be a gas!
Last week, I posted a simple Lennard–Jones simulation, written in C++, that models the behavior of liquid Ar in only 1561 characters. By popular request, I'm now posting a breakdown/explanation of this program, both to explain the underlying algorithm and illustrate how it can be compressed.
An (in)famous code challenge in computer graphics is to write a complete ray tracer small enough to fit onto a business card. I've really enjoyed reading through some of the submissions over the years (e.g. 1, 2, 3, 4), and I've wondered what a chemistry-specific equivalent might be.