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

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.

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.

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.

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.

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.

Last week, I posted a simple Lennard–Jones simulation, written in C++, that models the behavior of liquid Ar in only 1561 characters.

An (in)famous code challenge in computer graphics is to write a complete ray tracer small enough to fit onto a business card.