My personal blog, focusing on issues of chemistry and metascience, unified by trying to answer the question "how can we make science better"?

Corin Wagen

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

Optimizing Python

I've been playing around with generating non-equilibrium conformations by molecular dynamics recently, and I've been thinking about how to best parse the outputs of a dynamics simulation.

Choosing Maximally Dissimilar Molecules

In many applications, including cheminformatics, it’s common to have datasets that have too many dimensions to analyze conveniently. For instance, chemical fingerprints are typically 2048-length binary vectors, meaning that “chemical space” as encoded by fingerprints is 2048-dimensional.

Dimensionality Reduction in Cheminformatics

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 institutions (like

Elegy for the MIT of Yesteryear

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 Rate-Determining Span

I’ve been pretty critical of peer review in the past, arguing that it doesn’t accomplish much, contributes to status quo bias, etc. But a few recent experiences remind me of the value that peer review provides: in today’s scientific culture, peer review is essentially the only time that scientists get honest and unbiased feedback on their work. How can this be true?

Peer Review, Imperfect Feedback

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.

Computational NMR Prediction: A Microreview

An (in)famous code challenge in computer graphics is to write a complete ray tracer small enough to fit onto a business card. I've really enjoyed reading through some of the submissions over the years (e.g. 1, 2, 3, 4), and I've wondered what a chemistry-specific equivalent might be.  As a first step in this space—and as a learning exercise for myself as I try to learn C++—I decided to try and write a tiny Lennard–Jones simulation.

Business Card Lennard–Jones Simulation

I took a pistol course in undergrad, and while I was a poor marksman I enjoyed the experience. In particular, I was surprised by how meditative the act of shooting was. As our instructor explained, much of good shooting comes down to not doing anything when you pull the trigger.

Book Review: Working Backwards

Apologies for the long hiatus: we've had some health issues in the family, and startup life has been particularly overwhelming. With any luck, I'll be able to return to a more regular posting frequency soon.

What’s the right relationship between theory, computation, and experiment? Much has been written on this.

The SolidWorks Model of Simulation

The Pauling model for enzymatic catalysis states that enzymes are “antibodies for the transition state”—in other words, they preferentially bind to the transition state of a given reaction, rather than the reactants or products. This binding interaction stabilizes the TS, thus lowering its energy and accelerating the reaction.

Transition States Are Polarizable

Previously, I wrote about various potential future roles for journals. Several of the scenarios I discussed involved journals taking a much bigger role as editors and custodians of science, using their power to shape the way that science is conducted and exerting control over the scientific process.

Editorial Overreach and Scientific Authority

Spoilers below for Ursula Le Guin’s short story “The Ones Who Walk Away From Omelas.” If you haven’t read it, it’s short—go and do so now!


 TW: child abuse, suicide.

In her short story “The Ones Who Walk Away From Omelas,” Ursula Le Guin describes an idyllic town (Omelas) built entirely on the misery of a single, innocent child.

Omelas, Hirschman, Altom: On Healing Academia

A few days ago, I wrote about kinetic isotope effects (KIEs), probably my favorite way to study the mechanism of organic reactions. To summarize at a high level: if the bonding around a given atom changes over the course of a reaction, then different isotopes of that atom will react at different rates.

What Happened to IRMS?

TW: stereotypes about molecular dynamics.

In his fantastic essay “The Two Cultures,” C. P. Snow observed that there was (in 1950s England) a growing divide between the academic cultures of science and the humanities: He reflects on the origins of this phenomenon, which he contends is new to the 20th century, and argues that it ought to be opposed: Snow’s essay is wonderful: his portrait of a vanishing cultural intellectual unity

The Two Cultures in Atomistic Simulation

I frequently wonder what the error bars on my life choices are. What are the chances I ended up a chemist? A scientist of any type? Having two children in graduate school? If I had the ability, I would want to restart the World Simulator from the time I started high school, run a bunch of replicates, and see what happened to me in different simulations.

Tech As Control Group For Science

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.

Misleading H/D KIE Experiments

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.

Entropy in the Water Dimer

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.

Screening for Generality: Reflections

—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.

Science and Engineering

“Like arrows in the hand of a warrior are the children of one's youth.” –Psalm 127:4 What if our most fundamental assumptions about parenting were wrong? That’s the question that Michaeleen Doucleff’s 2021 book

 Hunt, Gather, Parent

tries to tackle.

Book Review: Hunt, Gather, Parent

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.

2022 Paper(s) of the Year

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. I’ll briefly summarize the piece’s key arguments here, connect it to some ideas from

 Zero to One

, and finish by drawing some conclusions for academia.

Definite Games, Indefinite Optimism

As an undergraduate student in the sciences at MIT, contempt for management consulting was commonplace. Consulting was the path for people who had ambition devoid of any real interests, the “sellout road” where you made endless Powerpoints instead of providing any tangible improvement to the world. In contrast, going to graduate school was a choice that showed commitment and integrity.

Consulting as Private-Sector Graduate School

This Easter week, I’ve been thinking about why new ventures are so important. Whether in private industry, where startups are the most consistent source of innovative ideas, or in academia, where new assistant professors are hired each year, newcomers are often the most consistent source of innovation. Why is this?

Why New Ventures Are So Important

Who was Richard Hamming, and why should you read his book? If you’ve taken computer science courses or messed around enough with

 scipy

, you might recognize his name in a few different places—Hamming error-correction codes, the Hamming window function, the Hamming distance, the Hamming bound, etc. I had heard of some of these concepts, but didn’t know anything concrete about him before I started reading this book.

Book Review: The Art of Doing Science and Engineering

(Previously: 2022)

 #1. Tony Fadell,


 
 Build
 


 #2. Giff Constable,


 
 Talking To Humans
 


 #3. Ben Horowitz,


 
 The Hard Thing About Doing Hard Things
 


 #4. Dale Carnegie,


 
 How To Win Friends And Influence People
 

Sounds Machiavellian, but actually quite wholesome: a “dad book,” as my friend

Books for 2023

Now that our lab’s site-selective glycosylation has been published, I wanted to share some reflections from the computational portion of the work. As one might expect, finding transition states for these large and flexible catalysts was a substantial challenge.

Site-Selective Glycosylation: Reflections

“A cord of three strands is not easily broken.” —Ecclesiastes 4:12  Computational chemistry, like all attempts to simulate reality, is defined by tradeoffs. Reality is far too complex to simulate perfectly, and so scientists have developed a plethora of approximations, each of which reduces both the cost (i.e. time) and the accuracy of the simulation.

Composite Methods in Quantum Chemistry

Last week, I posted a simple Lennard–Jones simulation, written in C++, that models the behavior of liquid Ar in only 1561 characters. By popular request, I'm now posting a breakdown/explanation of this program, both to explain the underlying algorithm and illustrate how it can be compressed.

Business Card Lennard–Jones Simulation, Explained

This post assumes some knowledge of molecular dynamics and forcefields/molecular mechanics. For readers unfamiliar with these topics, Abhishaike Mahajan has a great guide to these topics on his blog.

Although forcefields are commonplace in all sorts of biomolecular simulation today, there’s a growing body of evidence showing that they often give unreliable results.

Are Forcefields Able To Describe Protein Dynamics?

“And I took the little scroll from the hand of the angel and ate it. It was sweet as honey in my mouth, but when I had eaten it my stomach was made bitter.” –Revelation 10:10  As machine learning becomes more and more important to chemistry, it’s worth reflecting on Richard Sutton’s 2019 blog post about the “bitter lesson.” In this now-famous post, Sutton argues that “the biggest lesson that can be read from 70 years of AI research is that

Bitter Lessons in Chemistry

If you are a scientist, odds are you should be reading the literature more. This might not be true in every case—one can certainly imagine someone who reads the literature too much and never does any actual work—but as a heuristic, my experience has been that most people would benefit from reading more than they do, and often much more.

You Should Read The Literature More

Much molecular design today can be boiled down to “put the right functional groups in exactly the right places.” In catalysis, proper positioning of functional groups to complement developing charge or engage in other stabilizing non-covalent interactions with the transition state can lead to vast rate accelerations.

Generative Linker Design

(with apologies to Maimonides and Nozick)

Screening on only one substrate before assessing the substrate scope. This is the “ordinary means” in methods development. Screening on one substrate, but choosing a substrate that worked poorly in a previous study (e.g.). This can be thought of as serial multi-substrate screening, where each substrate is a separate project, but the body of work achieves greater generality over time.

Seven Degrees of Screening for Generality

TW: sarcasm.

Today, most research is done by academic labs funded mainly by the government. Many articles have been written on the shortcomings with academic research: Sam Rodriques recently had a nice post about how academia is ultimately an educational institution, and how this limits the quality of academic research. (It’s worth a read;

For-Profit Micro Focused Research Organizations: A Proposal

Recently, I wrote about how scientists could stand to learn a lot from the tech industry. In that spirit, today I want to share a book review of

 Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley

, Antonio García Martínez’s best-selling memoir about his time in tech and “a guide to the spirit of Silicon Valley” (NYT).

 Chaos Monkeys

is one of the most literary memoirs I’ve read.

Book Review: Chaos Monkeys

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.

Potential Energy Surfaces in Python

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.

Computational Report Cards

For many organic chemists, it’s hard to grasp the vast difference between various “fast” units of time. For instance, if a reactive intermediate has a lifetime of microseconds, does that mean it can escape the solvent shell and react with a substrate? What about a lifetime of nanoseconds, picoseconds, or femtoseconds?

Timescales

This thesis, from Christian Sailer at Ludwig Maximilian University in Munich, is one of the most exciting studies I’ve read this year. Sailer and coworkers are able to generate benzhydryl carbocations from photolysis of the corresponding phosphonium salts, and can monitor their formation and lifetime via femtosecond transient absorption spectroscopy.

Plenty of Room at the Bottom

Not Boring

recently published a panegyric about Varda, a startup that’s trying to create “space factories making drugs in orbit.” When I first read this description, alarm bells went off in my head—why would anyone try to make drugs in space? Nevertheless, there’s more to this idea than I initially thought.

Crystallization in Microgravity

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.

Books from 2022

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.

Gilliam and Girard on Scientific Innovation

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.

Book Review: Zero to One

One of the most distinctive parts of science, relative to other fields, is the practice of communicating findings through peer-reviewed journal publications. Why do scientists communicate in this way? As I see it, scientific journals provide three important services to the community:  Journals help scientists communicate; they disseminate scientific results to a broad audience, both within one’s community and to a broader scientific audience.

Do We Still Need Journals?

In 2019, ChemistryWorld published a “wish list” of reactions for organic chemistry, describing five hypothetical reactions which were particularly desirable for medicinal chemistry. A few recent papers brought this back to my mind, so I revisited the list with the aim of seeing what progress had been made.

Organic Chemistry’s Wish List, Four Years Later

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.

Evaluating Error in Boltzmann Weighting

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

Combating Computational Nihilism

I started this blog one year ago today, with a post on site-selective glycosylation. According to Google Analytics, there have been 24,035 views since then. What have the top posts been?

A Year In The Blog

Much ink has been spilled on whether scientific progress is slowing down or not (e.g.). I don’t want to wade into that debate today—instead, I want to argue that, regardless of the rate of new discoveries, acquiring scientific data is easier now than it ever has been. There are a lot of ways one could try to defend this point;

Data Are Getting Cheaper

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). I’ve always found this to be a bit uninspiring, though. “Interdisciplinary research” brings to mind a fashion collaboration, where the project is going to end up being some strange chimera, with goals and methods taken at random from two unrelated fields.

Domain Arbitrage

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. Professors need publications to get tenure, graduate students need publications to graduate, postdocs need publications to get jobs, and research groups need publications to win grants.

Industry Research Seems Underrated

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!

Response to Comments on "Against Carbon NMR"

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.

Low-Code Conformational Searching

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.

Against Carbon NMR

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.” Metascience, the central topic of the essay, is science about science.

Structural Diversity in Science

In my experience, most computational chemists only know a handful of basic Bash commands, which is a shame because Bash is incredibly powerful. Although I'm far from an expert, here are a few commands I frequently find myself using: 1.

 sed

For Find-and-Replace. $ sed -i “s/b3lyp/m062x/” *.gjf If you want to resubmit a bunch of transition states at a different level of theory, don't use a complex package like cctk!

Four Handy Bash One-Liners

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&amp;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

Singleton Saturday: Nitration of Toluene

While IR spectroscopy is still taught in introductory organic chemistry classes, it has been almost completely replaced by NMR spectroscopy and mass spectrometry for routine structural assignments.

The Apotheosis of 2D Infrared Spectroscopy

Talent

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

Book Review: Talent

Abhishaike Mahajan recently wrote an excellent piece on how generative ML in chemistry is bottlenecked by synthesis (

 disclaimer: I gave some comments on the piece, so I may be biased

). One of the common reactions to this piece has been that self-driving labs and robotics will soon solve this problem—this is a pretty common sentiment, and one that I’ve heard a lot.

Robots Won't Solve Organic Synthesis

In this post, I’m trying something new and embedding calculations on Rowan alongside the text. You can view the structures and energies right in the page, or you can follow a link and view the full data in a new tab.

Looking Back on the Norbornyl Cation

Since the ostensible purpose of organic methodology is to develop reactions that are useful in the real world, the utility of a method is in large part dictated by the accessibility of the starting materials.

How Common Are Different Functional Groups?

The concept of p

 K


 a

is introduced so early in the organic chemistry curriculum that it’s easy to overlook what a remarkable idea it is.

 Briefly, for the non-chemists reading this: p

K*

 a

is defined as the negative base-10 logarithm of the acidity constant of a given acid H–A:* p

 K


 a

:= -log

 10

([HA]/[A-][H+])

 Unlike pH, which describes the acidity of a bulk solution,

pKa and Nonpolar Media

An important problem with simulating chemical reactions is that reactions generally take place in solvent, but most simulations are run without solvent molecules. This is a big deal, since much of the inaccuracy associated with simulation actually stems from poor treatment of solvation: when gas phase experimental data is compared to computations, the results are often quite good.

Machine Learning for Explicit Solvent Molecular Dynamics

You are a scientist, not a lab monkey. You ought not to view your degree as “six years of hard labor in the chemistry mines.” Always make time to go to interesting seminars, talk with other people about their research, and read the literature. Otherwise, what’s the point of being a scientist? Only one person is really looking out for your best interests: you.

Opinionated Advice for Incoming Graduate Students

Modeling ion-pair association/dissociation is an incredibly complex problem, and one that's often beyond the scope of conventional DFT-based techniques.

Introducing quick_fuoss

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

Plotting Chemical Space with UMAP and Molecular Fingerprints

This is the first in what will hopefully become a series of blog posts focusing on the fascinating work of Dan Singleton (professor at Texas A&amp;M). My goal is to provide concise and accessible summaries of his work and highlight conclusions relevant to the mechanistic or computational chemist.

A central theme in mechanistic chemistry is the question of concertedness: if two steps occur simultaneously (“concerted”) or one occurs

Singleton Saturday: Dynamics at the Border Between General- and Specific-Acid Catalysis

Scientific software can be confusing, particularly when you're doing something that the software isn't primarily intended for. I often find myself wanting to run quick-and-dirty molecular dynamics simulations on small organic molecules, but I've struggled to find an easy way to do this using open-source tools.

Running Simple MD Simulations

Pure mathematics has all sorts of unexpected connections to other fields, and chemistry is no exception. One example of this is group theory: while I never delved deeply enough into math to actually study group theory as its own field, I've had to learn how to assign point groups to three-dimensional objects for several inorganic chemistry classes.

Molecular Symmetry Analysis Made Easy

In Wednesday’s post, I wrote that “traditional physical organic chemistry is barely practiced today,” which attracted some controversy on X. Here are some responses:  “POC has evolved in many directions and its concepts are widely used, e.g., in host-guest chem, org syn, materials sci, drug discovery.” - Bill Jorgensen  “There is still a lot of absolutely gorgeous classical phys org done with organometallic and enzymatic reactions.

Physical Organic Chemistry: Alive or Dead?

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. One interesting question that I’m revisiting is the following: when protonating benzaldehyde, what is the H/D equilibrium isotope effect at the aldehyde proton? This question was relevant for the H/D KIE experiments we conducted in our study of the asymmetric Prins cyclization.

Isotope Effects in Aldehyde Protonation

Quantum computing gets a lot of attention these days. In this post, I want to examine the application of quantum computing to quantum chemistry, with a focus on determining whether there are any business-viable applications today.

Quantum Computing for Quantum Chemistry: Short-Term Pessimism

(in the spirit of Dale Carnegie and post-rat etiquette guides)

Scientists, engineers, and other technical people often make fun of networking. Until a few years ago, I did this too: I thought networking was some dumb activity done by business students who didn’t have actual work to do, or something exploitative focused on pure self-advancement.

Networking, For Skeptics

ICYMI: Ari and I announced our new company, Rowan! We wrote an article about what we're hoping to build, which you can read here. Also, this blog is now listed on The Rogue Scholar, meaning that posts have DOIs and can be easily cited.

Conventional quantum chemical computations operate on a collection of atoms and create a single wavefunction for the entire system, with an associated energy and possibly other properties.

EDA Scares Me

I first encountered organic chemistry on Wikipedia, my freshman year of high school. The complexity and arcanity of the field instantly attracted me: here was something interesting that I didn’t know about and which didn’t require years of mathematical training to approach (unlike most of physics).  I soon started reading about organic chemistry more and more, albeit with no rhyme or reason to my study.

A Farewell To Lab

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.

Models and Oracles

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

The Importance of Integral Screening

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

Who Will Optimize The Optimizers?

Now that our lab's site-selective glycosylation has been
[published](https://www.nature.com/articles/s41586-022-04958-w), I
wanted to share some reflections from the computational portion of the
work.

As one might expect, finding transition states for these large and
flexible catalysts was a substantial challenge. We ended up using an
ONIOM-type scheme to model most of the catalyst using PM7, which meant
only the thioureas and the "northern" amide (which acts as a general
base) needed to be computed using DFT. Even with this approximation, it
took three months to find the first glycosylation transition state.
Although we ended reoptimizing everything with all-atom DFT for
publication, using PM7 for non-essential atoms was crucial in
accelerating the initial transition-state search.

Working on this project also gave me a new appreciation for all of the
work done to develop linear-scaling DFT methods. The size of the full
system meant that even routine Hessian calculations with a double-zeta
basis set took most of a week; without all the work that's been done to
speed up calculations on large systems (e.g. the [fast multipole
method](https://www.science.org/doi/10.1126/science.271.5245.51)), these
computations would not even have been possible. Hopefully, calculations
on systems of this size will become routine in the coming decade.

After finding the (1,2) and (1,3) transition states, we were surprised
(and disappointed) to find that the predicted selectivity was completely
backwards from that observed experimentally. Closer observation of the
transition states showed that the hydrogen-bonding network in the
unprotected acceptor was quite different between the two structures,
leading us to suspect that this energetic difference might be an
artifact of implicit solvation. Since the reaction is run in diisopropyl
ether, a hydrogen-bond acceptor, we reasoned that the unprotected
hydroxyls would be able to form hydrogen bonds with solvent ether
molecules and not with the donor.

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To test this idea, we decided to attempt explicit solvent calculations.
Although a full *ab initio* molecular dynamics study of this system was
clearly out of the question, we were able to run molecular dynamics
using Grimme's GFN2-xtb method for the catalyst, donor, and acceptor and
the GFN-FF polarizable forcefield for the solvent. Examination of the
pre-(1,3) complex shows that the C4 hydroxyl is indeed solvated by a
diisopropyl ether, meaning that the donor--acceptor hydrogen bond
predicted by DFT is just wrong. (As a bonus, we found that no (1,2)
preference exists in the ground state, in agreement with the
experimental observation that *K~M~* is not lower for more selective
catalysts.)

Although the coolest solution would have been to do free energy
perturbation in explicit solvent to get an accurate ∆∆G between the
(1,2) and (1,3) transition states, technical barriers meant we had to
settle for modeling the C4-protected acceptor, which indeed favored the
correct product. Still, I think this approach demonstrates the
computational insight that explicit solvent calculations can give even
when a full, high-level treatment of the system is unreasonable. We've
been developing [software](https://github.com/corinwagen/presto) to make
this sort of molecular dynamics more routine---if you're interested in
using this in your research, please contact me!