Corin Wagen

I’ve been pretty critical of peer review in the past, arguing that it doesn’t accomplish much, contributes to status quo bias, etc.

I first encountered organic chemistry on Wikipedia, my freshman year of high school.

I started this blog one year ago today, with a post on site-selective glycosylation.

Not Boring recently published a panegyric about Varda, a startup that’s trying to create “space factories making drugs in orbit.”

(with apologies to Maimonides and Nozick) Screening on only one substrate before assessing the substrate scope.

Much ink has been spilled on whether scientific progress is slowing down or not (e.g.).

Recently, I wrote about how scientists could stand to learn a lot from the tech industry.

Previously, I wrote about various potential future roles for journals.

“They performed his signs among them, and miracles in the land of Ham.”

A few days ago, I wrote about kinetic isotope effects (KIEs), probably my favorite way to study the mechanism of organic reactions.

I’m writing my dissertation right now, and as a result I’m going back through a lot of old slides and references to fill in details that I left out for publication.

I frequently wonder what the error bars on my life choices are.

This image inspired by the rightly famous original.

In many applications, including cheminformatics, it’s common to have datasets that have too many dimensions to analyze conveniently.

(This is more of a housekeeping post than an actual post with content; apologies.)

This Easter week, I’ve been thinking about why new ventures are so important.

While scientific companies frequently publish their research in academic journals, it seems broadly true that publication is not incentivized for companies the same way it is for academic groups.

If you are a scientist, odds are you should be reading the literature more.

It’s a truth well-established that interdisciplinary research is good, and we all should be doing more of it (e.g. this NSF page).

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.

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 science, if you know what you are doing, you should not be doing it.

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.

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 SN2.

Last January, I aimed to read 50 books in 2022.

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.

13C NMR is, generally speaking, a huge waste of time.

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 comp…

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.

Michael Nielsen and Kanjun Qiu recently published a massive essay entitled “A Vision of Metascience: An Engine of Improvement for the Social Processes of Science.”

Spoilers below for Ursula Le Guin’s short story “The Ones Who Walk Away From Omelas.”

The failure of conventional calculations to handle entropy is well-documented.

Talent, by Tyler Cowen and Daniel Gross, is a book about talent selection—in other words, a book about hiring.

In our recently published work on screening for generality, we selected our panel of model substrates in part using cheminformatic techniques.

In the course of preparing a literature meeting on post-Hartree–Fock computational methods last year, I found myself wishing that there was a quick and simple way to illustrate the relative error of different approximations on some familiar model reaction…

Who is Peter Thiel?

This is the second in what will hopefully become a series of blog posts (previously) focusing on the fascinating work of Dan Singleton (professor at Texas A&M).

Old image of MIT, c/o Wikimedia Commons Over the past few weeks, I’ve been transfixed, and saddened, by Eric Gilliam’s three-part series about the history of MIT (my alma mater).

Organic chemists often think in terms of potential energy surfaces, especially when plotting the results of a computational study.

Now that our work on screening for generality has finally been published in Nature, I wanted to first share a few personal reflections and then highlight the big conclusions that I gleaned from this project.

One common misconception in mechanistic organic chemistry is that reactions are accelerated by speeding up the rate-determining step.

The growing accessibility of computational chemistry has, unfortunately, led to a preponderance of papers with bad computations.