The Futile Cycle

Chad Orzel at Uncertain Principles and Janet Stemwedel at Adventures in Ethics and Science are both having a go at describing the difference between chemistry and physics. Chad thinks it’s a matter of scales and subject (i.e. what’s being studied), while Janet thinks it’s more of a difference of methodology.

At some point, though, there’s not going to be a clear distinction between chemistry and physics. The difference between “chemical physics” and “physical chemistry” is largely a matter of the speaker’s biases and personal identification. People who think they’re physicists will talk about “chemical physics”, while people who want to be chemists will talk about “physical chemistry”; in the end, they’re all talking about the same thing.

Take low-energy nuclear physics. I definitely covered some basic nuclear physics in my inorganic chemistry and physical chemistry classes, such as the ideas of nuclear orbitals, nuclear decay, and so on, and chemists use nuclear physics for lots of things, with NMR being one of the most common ones. As another example, there was a chemist in my undergrad department who studied Bose-Einstein condensates, which definitely overlaps with Chad’s atomic and molecular physics. Thermodynamics too, is in physics and chemistry, especially statistical mechanics. What about protein folding and protein structure determination — Linus Pauling won the Nobel prize in chemistry, but does that make him a chemist for sure? Can’t he also be a physicist?

The distinction between chemistry and physics isn’t just the toolbox people use, or what they study. Obviously, some things are chemistry (like organic synthesis) and some things are physics (like the Theory of Relativity), but people can be studying the same thing from radically different directions, and methodologies can be swapped back and forth between fields. Physicists often do programming, but they’re not doing computer science (usually). I think the difference is in what questions they’re trying to answer: chemists want to know how to manipulate and make things, and physicists want to know how things interact. There is a lot of overlap still, of course, and there all I can say is “physichemistry.”

As for me, I’d like to go into computational, quantitative chemical biophysics. How’s that for interdisciplinary research?

For those of you who program in Mac OS X, once you upgrade to Leopard, you may have to resolve problems with duplicate man pages. I came across this issue when I noticed that hitting ls -l in Terminal started giving me this unfamiliar “@” symbol after the permissions column of some of the files (I had seen “+” there before, but not “@“)

Unfortunately, the man pages didn’t help, and it took me a little time to figure out that for some reason, the old man pages from Tiger were still around, and the system preferentially reads from those rather than the new Leopard ones. A simple Ruby script found here does the trick in removing the old man pages. This problem only appears to those who did an “Upgrade” installation of Leopard instead of a clean install or an “Archive and Install.”

The new Leopard man pages, of course, explain the “@” symbol. (It denotes that the file has extended attributes from the new metadata system. To see the attributes on a specific file, try typing xattr -l filename.)

It’s common sense, but strangely a lot of people don’t seem to get this:

“Linux is only free if your time has no value.” — Jamie Zawinski

From The Introduction to Software Carpentry.

I recently read Paul Graham’s essay How Not to Die, which is about how to keep your startup company from dying. His focus is on internet and tech startups in Silicon Valley, but as I read, working at a startup started to sound a lot like doing research in graduate school.

Here are some sayings that seem to apply to both working at a startup and doing research:

For us the main indication of impending doom is when we don’t hear from you. When we haven’t heard from, or about, a startup for a couple months, that’s a bad sign. If we send them an email asking what’s up, and they don’t reply, that’s a really bad sign. So far that is a 100% accurate predictor of death…When startups die, the official cause of death is always either running out of money or a critical founder bailing. Often the two occur simultaneously. But I think the underlying cause is usually that they’ve become demoralized.

This sounds a lot like how some projects go in graduate school. If the student doesn’t talk to the professor often enough, the project will probably die, the student will become despondent, and will probably not get his or her Ph.D. This quickly leads to the converse possibility:

Maybe if you can arrange that we keep hearing from you, you won’t die.

I find that I was especially productive in my rotation this time around, because my professor meets with everyone every week. Our lab is small enough that at our weekly lab meetings, each person gets up to talk about what he or she has done or tried this past week, to talk about possibilities, get advice, troubleshoot, or even draw greater conclusions. It’s a fantastic economic self-contract, where I pre-commit myself not to fail.

Running a startup can be demoralizing…I’ve been there, and that’s why I’ve never done another startup. The low points in a startup are just unbelievably low. I bet even Google had moments where things seemed hopeless….Another feeling that seems alarming but is in fact normal in a startup is the feeling that what you’re doing isn’t working. The reason you can expect to feel this is that what you do probably won’t work.

This is probably a familiar statement to all graduate students in science. There are times when things just get bad, and the point is that those who succeed are those who power through the times when it just doesn’t work. And frankly, if it was obvious that the research would work, then it probably isn’t worth doing.

The number one thing not to do is other things. If you find yourself saying a sentence that ends with “but we’re going to keep working on the startup,” you are in big trouble.

This is really interesting, and I don’t know how much it applies to graduate school. Perhaps the similarity diverges here. Or maybe this is actually sage-like advice for research. There is a certain amount of focus that’s necessary to complete some research topics, but one certainly spreads the risk out on at least two projects, so that if one fails the other can succeed. On the other hand, the spreading of risk and attention does lead to a lack of proper motivation to persevere on each project…

Founders are more motivated by the fear of looking bad than by the hope of getting millions of dollars. So if you want to get millions of dollars, put yourself in a position where failure will be public and humiliating.

I don’t know how much this applies, either, but in a sense, pre-committing to the professor on how well you’ll do is a good motivation to do work, as long as the professor understands if the project is very high risk.

All of you guys already have the first two. You’re all smart and working on promising ideas. Whether you end up among the living or the dead comes down to the third ingredient, not giving up.

So I’ll tell you now: bad shit is coming. It always is in a startup. The odds of getting from launch to liquidity without some kind of disaster happening are one in a thousand. So don’t get demoralized. When the disaster strikes, just say to yourself, ok, this was what Paul was talking about. What did he say to do? Oh, yeah. Don’t give up.

This is highly relevant. My professor and others keep telling me that the best predictor of graduate school success isn’t so much intelligence and whatnot as much as the ability to keep going when things fail. The ability to troubleshoot, manage errors as best as one can, and just get things done is the best predictor. So, in a sense, graduate students, throughout their Ph.D., are honing the same skills that startup founders are.

It’s an interesting parallelism. Maybe someday I can talk up a venture capitalist with this hypothesis!