Every year, our group participates in a “Paper of the Year” competition, where we each nominate five papers and then duke it out in a multi-hour debate. Looking through hundreds of papers in a few weeks is a great exercise: it helps highlight both creativity and its absence, and points towards where the field’s focus might turn next.

Nature has produced most natural molecules as chiral objects, which means the molecule can come in two enantiomeric forms, each being the mirror image of the other. When a natural product is synthesised in a laboratory, a chiral synthesis means just one form is made, and then is compared with the natural product to see if it matches.

For almost all Hartree–Fock-based computational methods, including density-functional theory, the rate-limiting step is calculating electron–electron repulsion.

While looking over papers from the past year, one theme in particular stood out to me: meta-optimization, or optimizing how we optimize things.

I have previously looked at the pigments used to colour the Book of Kells, which dates from around 800 AD and which contained arsenic sulfide as the yellow colourant. The Bayeaux tapestry is a later embroidery dating probably from around 1077 and here the colours are based entirely on mordanted natural dyes.

Note: old versions of this post lacked a discussion of S _N 2. I've added an appendix which remedies this. In “The Rate-Limiting Span,” I discussed how thinking in terms of the span from ground state to transition state, rather than in terms of elementary steps, can help prevent conceptual errors. Today, I want to illustrate why this is important in the context of a little H/D KIE puzzle.

Last January, I aimed to read 50 books in 2022. I got through 32, which is at least more than I read in 2021. There’s been a bit of discourse around whether setting numerical reading goals for oneself is worthwhile or counterproductive.

A technique that I’ve seen employed more and more in computational papers over the past few years is to calculate Boltzmann-weighted averages of some property over a conformational ensemble. This is potentially very useful because most complex molecules exist in a plethora of conformations, and so just considering the lowest energy conformer might be totally irrelevant.

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!