The failure of conventional calculations to handle entropy is well-documented. Entropy, which fundamentally depends on the number of microstates accesible to a system, is challenging to describe in terms of a single set of XYZ coordinates (i.e. a single microstate), and naïve approaches to computation simply disregard this important consideration.

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 reactions, like a "report card" for different levels of theory.

Who is Peter Thiel? Tyler Cowen calls him one of the most important public intellectuals of our era. Bloomberg called him responsible for the ideology of Silicon Valley “more than any other living Silicon Valley investor or entrepreneur.” Depending on who you ask, he’s either a shadowy plutocratic genius or a visionary forward-thinking genius: but everyone seems to at least agree that he’s a genius.

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). My goal is to provide concise and accessible summaries of his work and highlight conclusions relevant to the mechanistic or computational chemist. Today I want to discuss one of my favorite papers, a detailed study of the nitration of toluene by Singleton lab at Texas

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 ). I’ll post a few quotations and responses below, but if you’re interested you should just go read the original essays (1, 2, 3). Why MIT Was Created This quote highlights how MIT was intended to be a counter-cultural university, founded on a distinctly different model than other

Organic chemists often think in terms of potential energy surfaces, especially when plotting the results of a computational study. Unfortunately it is non-trivial to generate high-quality potential energy surfaces.

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. This project originated from conversations I had with Eugene Kwan back in February 2019, when I was still an undergraduate at MIT.

One common misconception in mechanistic organic chemistry is that reactions are accelerated by speeding up the rate-determining step. This mistaken belief can lead to an almost monomaniacal focus on determining the nature of the rate-determining step. In fact, it's more correct to think of reactions in terms of the rate-determining span: the difference between the resting state and the highest-energy transition state.

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