Wednesday, December 30, 2020

End of the year, looking back and looking forward

 A few odds and ends at the close of 2020:

  • This was not a good year, for just about anyone.  Please, let's take better care of each other (e.g.) and ourselves!  
  • The decision to cancel the in-person 2020 APS March Meeting looks pretty darn smart in hindsight.
  • Please take a moment and consider how amazing it is that in less than a year, there are now multiple efficacious vaccines for SARS-Cov-2, using different strategies, when no one had ever produced a successful vaccine for any coronavirus in the past.  Logistical problems of distribution aside, this is a towering scientific achievement.  People who don't "believe" in vaccines, yet are willing to use (without thinking) all sorts of other scientific and engineering marvels, are amazing to me, and not in a good way.  For a compelling book about this kind of science, I again recommend this one, as I had done ten years ago.
  • I also recommend this book about the history of money.  Fascinating and extremely readable.  It's remarkable how we ended up where we are in terms of fiat currencies, and the fact that there are still fundamental disagreements about economics is both interesting and sobering.
  • As is my habit, I've been thinking again about the amazing yet almost completely unsung intellectual achievement that is condensed matter physics.  The history of this is filled with leaps that are incredible in hindsight - for example, the Pauli principle in 1925, the formulation of the Schroedinger equation in 1926, and Bloch's theorem for electrons in crystals in 1928 (!!).  I've also found that there is seemingly only one biography of Sommerfeld (just started it) and no book-length biography of Felix Bloch (though there are this and this).  
  • Four years ago I posted about some reasons for optimism at the end of 2016.  Globally speaking, these are still basically valid, even if it doesn't feel like it many days.  Progress is not inevitable, but there is reason for hope.
Thanks for reading, and good luck in the coming year.  

Saturday, December 19, 2020

The physics of beskar

 In keeping with my previous posts about favorite science fiction condensed matter systems and the properties of vibranium, I think we are overdue for an observational analysis of the physics of beskar.  Beskar is the material of choice of the Mandalorians in the Star Wars universe.  It is apparently an alloy (according to wookiepedia), and it is most notable for being the only material that can resist direct attack by lightsaber, as well as deflecting blaster shots.   

Like many fictional materials, beskar has whatever properties are needed to drive the plot and look cool doing so, but it's still fun to think about what would have to be going on in the material for it to behave the way it appears on screen.  

In ingot form, beskar looks rather like Damascus steel (or perhaps Valyrian steel, though without the whole dragonfire aspect).  That's a bit surprising, since the texturing in damascene steel involves phase separation upon solidification from the melt, while the appearance of beskar is homogeneous when it's in the form of armor plating or a spear.  From the way people handle it, beskar seems to have a density similar to steel, though perhaps a bit lower.

Beskar's shiny appearance says that at least at optical frequencies the material is a metal, meaning it has highly mobile charge carriers.  Certainly everyone calls it a metal.  That is interesting in light of two of its other obvious properties:  An extremely high melting point (we know that lightsabers can melt through extremely tough metal plating as in blast doors); and extremely poor thermal conductivity.  (Possible spoilers for The Mandalorian S2E8 - it is possible to hold a beskar spear with gloved hands mere inches from where the spear is visibly glowing orange.)  Because mobile charge carriers tend to conduct heat very well (see the Wiedemann Franz relation), it's tricky to have metals that are really bad thermal conductors.  This is actually a point consistent with beskar being an alloy, though.  Alloys tend to have higher electrical resistivity and poorer thermal conduction than pure substances.  

The high melting temperature is consistent with the nice acoustic properties of beskar (as seen here, in S2E7), and its extreme mechanical toughness.  The high melting temperature is tricky, though, because there is on-screen evidence that beskar may be melted (for forging into armor) without being heated to glowing.  Indeed, at about 1:02 in this video, the Armorer is able to melt a beskar ingot at the touch of a button on a console.  This raises a very interesting possibility, that beskar is close to a solid-liquid phase transition that may be tuned to room temperature via a simple external parameter (some externally applied field?).  This must be something subtle, because otherwise you could imagine anti-beskar weapons that would turn Mandalorian armor into a puddle on the floor.  

Regardless of the inconsistencies in its on-screen portrayal (which are all minor compared to the way dilithium has been shown), beskar is surely a worthy addition to fictional materials science.  This is The Way.


Thursday, December 17, 2020

Brief items

Here are a few interesting links as we look toward the end of a long year:

  • Brian Skinner of Gravity and Levity has a long and excellent thread on twitter about cool materials.
  • Subir Sachdev at Harvard has put his entire semester's worth of lectures on youtube for his course on Quantum Phases of Matter
  • New data on stellar distances makes the Hubble constant problem even worse, as explained in this nice article by the reliably excellent Natalie Wolchover.
  • In case you were wondering, we are nowhere near done with magnetic tape as a storage medium, especially since it's comparatively cheap and can now hold 317 Gb/in2.
  • If you aren't impressed by SpaceX's initial flight test of their latest rocket, I don't know what to say to you.  They were trying several brand new things at the same time, and almost got it all to work on the first try.  FYI, the green exhaust at the end is from the engine running hot and fuel-deprived, so the oxygen is burning the copper alloy engine lining.
  • This paper uses nanomechanical resonators immersed in fluid to study Brownian motion.  The resonator is getting kicked randomly by collisions with the fluid molecules, and looking at the noise in the displacement is a neat probe of the fluid's dynamics.  
  • In this paper, the authors are able to resolve inelastic electron tunneling spectra even above room temperature.  That's actually very surprising!
  • Here is a perspective article about plasmonic catalysis, trying to drive chemical reactions by optical excitation of collective electronic modes in conductive nanostructures.  

Thursday, December 10, 2020

Photonic quantum supremacy, protein folding, and "computing"

In the last week or two, there have been a couple of big science stories that I think raise some interesting issues about what we consider to be scientific computing.

In one example, Alphafold, a machine learning/AI approach to predicting protein structure, has demonstrated that it is really good at predicting protein structure.  Proteins are polymers made up of sequences of many amino acids, and in biological environments they fold up into complex shapes (with structural motifs like alpha helices and beta sheets) held together by hydrogen bonds. Proteins do an amazing amount of critical things in organisms (like act as enzymes to promote highly specific chemical reactions, or as motor units to move things around, or to pump specific ions and molecules in and out of cells and organelles).  Their ability to function in the wet, complex, constantly fluctuating biological environment is often dependent on minute details in their folded structure.  We only know snapshots of the structures of only some proteins because actually getting the structure requires crystallizing the protein molecules and performing high precision x-ray diffraction measurements on those crystals.  The challenge of understanding how proteins end up in particular functional structures based on their amino acid sequence is called the protein folding problem.  The statistical physics of folding is complex but usefully considered in terms of free energy landscapes.  It is possible to study large numbers of known protein structures and look for covariances (see here), correlations in sequences that show up commonly across many organisms.  Alphafold was trained on something like 100,000 structures and associated data, and is now good enough at predicting structures that it can actually allow people to solve complex x-ray diffraction data that was previously not analyzable, leading to new solved structures.  

This is very nice and will be a powerful tool, though like all such news stories one should be wary of the hype.  It does raise questions, and I would like to hear from experts:  Do we actually have greater foundational understanding of protein structure now?  Or have we created an extraordinarily effective interpolational look-up table?  It's useful either way, but the former might have more of an impact on our ability to understand the dynamics of proteins.  

That's a lot of optical components!
The second big story of the week is the photonic quantum supremacy achievement by a large group from USTC in China.  Through a very complex arrangement of optical components (see image), they report to have used boson sampling to determine statistical information about the properties of matrices at a level that would take an insanely long time with a classical computer.  Here, as with google's quantum supremacy claim (mentioned here), I again have to ask:  This is an amazing technical achievement, but is it really a computation, as opposed to an analog emulation or simulation?  If I filmed cream being stirred into coffee, and I analyzed the images to infer the flow of energy down to smaller and smaller length scales, I would describe that as an experiment, not as me doing a computation to solve the Navier-Stokes equations (which would also be very challenging to do with high precision on a classical computer).  Perhaps its splitting hairs, and quantum simulation is very interesting, but it does seem distinct to me from what most people would call computing.

Anyway, between AI/ML and quantum information sciences, it is surely an exciting time in the world of computing, broadly construed. 

(Sorry for the slow posting - end of semester grading + proposal writing have taken a lot of time.)