Recently, I’ve been working to assign the relative configuration of some tricky diastereomers, which has led me to do a bit of a deep dive into the world of computational NMR prediction. Having spent the last week or so researching the current state-of-the-art in simulating experimental ¹ H NMR spectra, I’m excited to share some of my findings.

Python is an easy language to write, but it’s also very slow.

Eric Gilliam, whose work on the history of MIT I highlighted before, has a nice piece looking at Irving Langmuir’s time at the General Electric Research Laboratory and how applied science can lead to advances in basic research.

When thinking about science, I find it helpful to divide computations into two categories: models and oracles. In this dichotomy, models are calculations which act like classic ball-and-stick molecular models. They illustrate that something is geometrically possible—that the atoms can literally be arranged in the proper orientation—but not much more. No alternative hypotheses have been ruled out, and no unexpected insights have emerged.

—Richard Hamming What’s the difference between science and engineering? Five years ago, I would have said something along the lines of “engineers study known unknowns, scientists study unknown unknowns” (with apologies to Donald Rumsfeld), or made a distinction between expanding the frontiers of knowledge (science) and settling already-explored territory (engineering). These thoughts seem broadly consistent with what others think.

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.

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.

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.