Tuesday, September 2, 2014

Killing comparisons II.

Previously I wrote about the danger of comparing oneself to your peers. It can easily lead to discouragement, anxiety, and a loss of self-confidence.

It is also unhelpful for students and postdocs to compare their present advisor/supervisor/mentor  to past advisors.
It is also unhelpful for a supervisor to compare their current students/postdocs to previous ones.

Early in his career Professor X had an absolutely brilliant student Y who made an important discovery. [Decades later X and Y shared a Nobel Prize for this discovery.] Apparently, X compared all his later students to Y, and could not understand why they could not be as good as Y. Sometimes he even let the students know this.

I have also known people who have had an exceptionally helpful undergrad/Ph.D advisor but then were always unhappy with their graduate/postdoc advisor because they just weren't as helpful.

Making comparisons is a natural human tendency. Sometimes I struggle with it. But, I try not to. It is only a route to frustration, to difficult relationships, and failed opportunities to help people develop.

Every individual has a different background, training, personality, gifting, and interests, leading to a diversity of strengths and weaknesses. Being a good student/postdoc involves innate intelligence, technical expertise, mathematical skills, computer programming, giving talks, getting along with others, writing, knowing the literature, time management, multi-tasking, planning, conceptual synthesis, understanding the big picture, being creative ....
Some of these will come very easily to some individuals. Some will really struggle.
The key is to accept each individual where they are at now and help them build on their strengths and realistically improve in their weak areas.

I think their are only two times when it is helpful and appropriate to make comparisons. Both should only be done in private. In writing letters of reference it may be appropriate and helpful to compare a student to previous ones. In deciding who to work with (e.g. for a postdoc) it is appropriate to research the advisor and compare them to other possible candidates. I meet too many students/postdocs who are struggling with an unhelpful advisor but never thought to find out what they were like before they signed up.

Monday, September 1, 2014

A course every science undergraduate should take?

Science is becoming increasingly inter-disciplinary.
Most science graduates do not end up working in scientific research or an area related to their undergraduate major.
Yet most undergraduate curricula are essentially the same as they were fifty years ago and are copies of courses at MIT-Berkeley-Oxford designed for people who would go on to a Ph.D and (hopefully) end up working in research universities.
Biology and medicine are changing rapidly particularly in becoming more quantitative.
How should we adapt to these realities?
Are there any existing courses that might be appropriate for every science major to take?

Sometimes my colleagues get upset that advanced physics undergrads don't know certain things they should [Lorentz transformations, Brownian motion, scattering theory, ....]. But, my biggest concern is that they don't have certain basic skills [dimensional analysis, sketching graphs, recognising silly answers,  writing clearly, ....]. These skills will be important in almost any job that has a quantitative dimension to it.

For the past few years Phil Nelson has been teaching a course at University of Pennsylvania that I think may "fit the bill." The associated textbook Physical Models of Living Systems will be published at the end of the year.

Previously, I have lavished praise on Nelson's book Biological Physics: Energy, Information, Life. I used it in a third year undergraduate biophysics course PHYS3170 and think it is one of the best textbooks I have every encountered. Besides clarity and fascinating subject material it has excellent problems [and solutions manual], makes use of real experimental data, has informative section headings, often discusses the limitations of different approaches, and uses nice historical examples.

The new book covers different material and focuses on some particular skills that any science and engineering major should learn, regardless of whether they end up working in biology or medicine or biophysics. Below I reproduce some of Nelson's summary.

Readers will acquire several research skills that are often not addressed in traditional courses:
  • Basic modeling skills, including dimensional analysis, identification of variables, and ODE formulation.
  • Probabilistic modeling skills, including stochastic simulation.
  • Data analysis methods, including maximum likelihood and Bayesian methods.
  • Computer programming using a general-purpose platform like MATLAB or Python, with short codes written from scratch.
  • Dynamical systems, particularly feedback control, with phase portrait methods.
[Here modeling does not mean running pre-packaged computer software but developing simple physical models].

All of these basic skills, which are relevant to nearly any field of science or engineering, are presented in the context of case studies from living systems, including:
  • Virus dynamics
  • Bacterial genetics and evolution of drug resistance
  • Statistical inference
  • Superresolution microscopy
  • Synthetic biology
  • Naturally evolved cellular circuits, including homeostasis, genetic switches, and the mitotic clock.
This looks both important and fascinating. The Instructors Preface makes an excellent case for the importance of the course, including to pre-medical students. The Table of Contents illustrates not just the logical flow and interesting content but again uses informative section headings that summarise the main point.

So, what do you think?
Is this a course (almost) every science undergraduate should take?
Are there specific courses you think all students should take?

Thursday, August 28, 2014

Hard questions about glasses

A recent book Dynamical heterogeneities in glasses, colloids, and granular media contains a fascinating chapter where four experts [Jorge Kurchan, James Langer, Thomas Witten, and Peter Wolynes] give their answers to the questions below.

I think we need more of these kind of frank discussions about scientific topics. I am slowly working through the answers. The most fascinating bit so far is Peter Wolynes inspiring response to Q9, including "I believe a young physicist who wants to work on any challenging problem in physics will eventually have to learn about glasses."
Q1) In your view, what are the most important aspects of the experimental data on the glass transition that any consistent theory explain? Is dynamical heterogeneity one of these core aspects?  
Q2) Why should we expect anything universal in the behavior of glass-forming liquids? Is the glass-transition problem well defined?  
Q3) In spin-glasses, the existence of a true spin-glass phase transition has been well established by simulations and experiments. Do you believe that a similar result will ever be demonstrated for molecular glasses? 
Q4) Why are there so many different theories of glasses? What kind of decisive experiments do you suggest to perform to rule out at least some of them? 
Q5) Can you briefly explain, and justify, why you believe your pet theory fares better than others? What, deep inside, are you worried about, that could jeopardize your theoretical construction?  
Q6) In the hypothesis that Random First-Order Theory [RFOT] forms a correct skeleton of the theory of glasses, what is missing in the theoretical construction that would convince the community?  
 Q7) Exactly solvable mean-field glass models exhibit an extraordinary complexity requiring impressive mathematical tools to solve them. 
 Q8) In your view, do the recent ideas and experimental developments concerning jamming in granular media and colloids contribute to our understanding of molecular glasses, or are they essentially complementary?  
Q9) If a young physicist asked you whether he or she should work on the glass problem in the next few years, would you encourage him or her and if so, which aspect of the glass problem would you recommend him or her to tackle 
 Q10) In twenty years from now, what concepts, ideas or results obtained on the glass transition in the last twenty years will be remembered?  
Q11) If you met an omniscient God and were allowed one single question on glasses, what would it be?
I thank Peter Wolynes for bringing this to my attention.

Wednesday, August 27, 2014

Why are quasi-particles interesting?

Last week I had a phone call from a journalist Andrew Grant at Science News, asking about quasi-particles. Why are they interesting?

1. Quasi-particles exist. It is not at all obvious why they should exist in strongly interacting quantum systems. Yet they are rather robust and found in diverse systems, ranging from atomic nuclei, to magnets, to metals, to neutron stars… Thus, they are an important organising principle in quantum many-body physics.

2. Occasionally quasi-particles have different quantum numbers to the constituent particles of a system. The most striking example is the fractional charge and statistics of quasi-particles in the fractional quantum Hall effect.

3. Many electronic materials of current interest [e.g. high-temperature superconductors] are “bad metals” that do not seem to have quasi-particles [except at low temperatures].

I find all of these are profound and surprising. They illustrate emergence.

Tuesday, August 26, 2014

A challenging ingredient in teaching

Francis Su is a mathematics Professor at Harvey Mudd College. He is teaches a course in Real Analysis, the lectures of which you can watch on Youtube. Last year he received the Haimo award from the Mathematical Association of America for excellence in teaching.

In receiving the award he gave a deeply personal talk
The Lesson of Grace in Teaching: From weakness to wholeness, the struggle and the hope

I actually wanted to post about his talk when I first read it months ago, but was hesitant to because I feel I struggle so much with the issues he talks about. Finally posting it was prompted by two events. I got my latest student teaching evaluations and they were pretty good [more on that later]. (Sadly, this also illustrates how I am struggling with what Su is talking about: performance based identity). A friend is taking a course on teaching and he told me they had a whole session discussing clarification of personal values and how they shape your own teaching philosophy and interactions with students. I thought Su was an excellent example of this.

Monday, August 25, 2014

How good should parameterisation of simple models be?

Over the past few years I have advocated a simple diabatic state model to describe hydrogen bonding in a diverse range of molecular complexes. In my first paper I suggested the following parameterisation of the matrix element coupling the two diabatic states

with two free parameters Delta1 and b, which describe the energy scale and length scale for the interaction.
R1 is just a reference distance ~ 2.4 A, introduced so that the prefactor Delta1 corresponds to a physically relevant scale.
The two parameter values I chose give a quantitative description of a wide range of properties [bond lengths, vibrational frequencies, and the associated isotope effects, when the quantum nuclear motion is taken into account.

Last week I found this nice paper
Solvent-Induced Red-Shifts for the Proton Stretch Vibrational Frequency in a Hydrogen-Bonded Complex. 1. A Valence Bond-Based Theoretical Approach 
Philip M. Kiefer, Ehud Pines, Dina Pines, and James T. Hynes

It uses a similar two-diabatic state model and references earlier work of Hynes going back to 1991. A parameterisation like that above is used.

Below is a plot of Delta (kcal/mol) vs. R (Angstroms), comparing my parametrisation to Hynes.

The curve with the smaller slope is the parameterisation of Hynes.

I found this agreement very satisfying and encouraging. I have mostly been concerned with symmetrical complexes [where the proton affinity of the donor and acceptor is equal] and bonds of strong to moderate strength [R ~ 2.3-2.6 Angstroms] and have compared the theory to experimental data for solid state materials. In contrast, Hynes has been mostly concerned with asymmetric complexes in polar solvents with weaker bonds [R ~ 2.7-2.8 Angstroms].

I also felt bad that I had not referenced Hynes work. Then I went back and checked my first paper. To my relief, I found I had explicitly stated that the parameterisation in his 1991 paper was comparable to mine. It is amazing how quickly I forget stuff!

But the main point of this post is to raise two general questions.

1. Should I really be so happy? Aren't I missing the point of simple models: to give insight into the essential physics and chemistry and describe trends in diverse set of systems. All that matters is that the parameters are "reasonable", i.e. not crazy.

2. What is a reasonable expectation for consistent parametrisation of simple models? At what point does one abandon a model because it requires some parameters that are "unreasonable"? For example, if Hynes parameters differed by a factor of ten or more I would say there is a serious problem with the model. But I would not be that concerned by a 50 per cent discrepancy.

Here is a concrete example for 2. At a recent Telluride meeting, Dominika Zgid lampooned the fact that for cerium oxides, people doing DFT+U calculations have used values of U ranging from 1 to 10 eV in order to describe different experimental properties. To me this clearly shows that there is physics beyond DFT+U in these materials.

I welcome answers. I realise that the answers may be subjective.

Saturday, August 23, 2014

Seeing enzyme catalysis with the naked eye

For my latest celebrity scientist speaking gig [at a small church youth group] my glamorous assistant [my wife] found a new demonstration to add to my repertoire, Elephants toothpaste. It is described in this Journal of Chemical Education paper.

Hydrogen peroxide is thermodynamically unstable. However, you can buy bottles of it and they will remain useful for months. It will slowly decompose into water and oxygen.
H
2
O
2
 → 2 H
2
O
 + O
2


However, if you add some iron chloride it acts as a catalyst and increases the decomposition rate by a factor of a thousand. You will see some amount of "bubbling" due to the oxygen gas produced. If blood [which contains haemoglobin] is added the rate increases by a factor of a million. Even better, if you add the enzyme catalase, the rate increases by a factor of a billion. In the demonstration the catalase is present in the yeast that is added. Catalase is one of the fastest catalysts known. It performs an incredibly important biochemical function, that is essential to life existing. Hydrogen peroxide is a strong oxidant  that could destroy many biomolecules. It is also an unwanted byproduct of many biochemical reactions. Biological systems use catalase to rapidly destroy the hydrogen peroxide before it can do harm.

The demonstration I did (and described in the JCE article) makes use of a dilute aqueous solution [a few per cent] of hydrogen peroxide. The spectacular video below makes use of a highly concentrated solution that is quite dangerous because it can cause chemical burns of the skin.


The above discussion follows the beautiful introduction to enzymes in chapter 11 of my favourite biochemistry text by Matthews, van Holde, Appling, and Anthony-Cahill 
It contains the figure below, illustrating the key idea of how catalysts work: by lowering the energy barrier [the transition state] for a chemical reaction.