Nobel Prize Predictions for 2026

This week Nobel Prizes will be announced. Predicting the awardees is a fun annual exercise. It is also good to reflect on what has been achieved, including outside our own areas, and big advances from the past we may now take for granted.

Before writing this I looked at suggestions from readers of Doug Natelson's blog, Nanoscale views, an article in Physics World, predictions from Clarivate based on citations, and recent recipients of the Wolf Prize.

Not surprisingly, this post is not that different from last year's.

Although we know little about how the process actually works or the explicit criteria used, I have a few speculative suggestions and observations.

1. The Wolf Prize is often a precursor. According to Wikipedia, for physics, "from the 26 prizes awarded between 1978 and 2010, fourteen winners have gone on to win the Nobel Prize, five of those in the following year." For chemistry, 13 awardees have subsequently won a Nobel.

2. Every now and then, the Swedes surprise us. Sometimes this may be because we now take for granted something discovered thirty or forty years ago.

3. Every few years, the physics committee seems to go for something technological, sometimes arguably outside physics, perhaps to remind people how important physics is to modern technology and other areas of science.

4. They seem to spread the awards around between different areas of physics.

5. Theory only gets awards when it has led to well-established experimental observations and new experimental fields. Brilliant theoretical discoveries that motivate large research enterprises (more theory and unsuccessful or ambiguous experimental searches) are not good enough. This is why predictions based on citation numbers may be misleading. High citations (particularly for a specific paper) are usually a necessary, but not sufficient, condition for a prize.

6. Once an award has been made on one topic, it is unlikely that there will be another award for a long time, if ever, on that same topic. In other words, there is a high bar for a second award.

7. I don't think the logic is to pick an important topic and then choose who should get the prize for the topic. This approach works against topics where many researchers independently made contributions that were all important. The awardee needs to be a standout who won't be a debatable choice.

8. The prize can go to at most 3 individuals and does not go to groups. This works against the large collaborations in particle physics and cosmology that produce significant work.

What do you think of these principles?

For some of the above reasons, I discuss below why I am sceptical about some specific predictions.

My top prediction for physics is Metamaterials with negative refractive index, going to John Pendry (theory) and David Smith (experiment). I know little about this topic.

Is it just a matter of time before twisted bilayer graphene wins a prize? This might go to Allan MacDonald (theory) and Pablo Jarillo-Herrero (experiment). They recently received a Wolf Prize. One thing that convinced me of the importance of this discovery was a preprint on moiré WSe2 with beautiful phase diagrams such as this one.

The level of control is truly amazing. Helpful background is the recent Physics Today article by Bernevig and Efetov.

This is big enough to overcome 6. and the earlier prize for graphene.

The prediction of Berry and Aharonov for topological phases in quantum mechanics is reasonable, except for questions about historical precursors in optics and quantum chemistry.

The prediction of topological insulators goes against 6. and the award to Haldane in 2016.

Predictions of a prize for quantum algorithms (Shor, Deutsch, Brassard, Bennett), conformal field theories, and braiding statistics go against 5. 

Chemistry 

I don't know enough chemistry to make meaningful predictions. On the other hand, in 2019 I correctly predicted John Goodenough for lithium batteries.  I do like the prediction (last year) from Clarivate for Biomolecular condensates (Brangwynne, Hyman, and Rosen). I discussed them briefly in my review article on emergence.

What do you think about my "principles"?

What are your predictions?

What are the assumptions of science?

 Science makes assumptions about the nature of the world and the nature of humanity. We are so used to these ideas that we may overlook that they are assumptions. Before the emergence of modern science, people did not necessarily believe these things were true. 

Existence of regularity and order

There are patterns and regularity in nature, even though at first glance this may not appear to be so. Nature is not completely random, chaotic, or unpredictable. Not every object or event is different from every other object or event. Some things are similar. Finding and describing this regularity is a challenge that requires persistent effort. Scientists assume the patterns are there and eventually they will discover them.

Existence of laws of nature

The order and regularity can be encoded in laws that describe diverse systems and situations. Often, the laws can be succinctly stated and may be written in a simple mathematical form.

Uniformity (universality)

 Many laws are believed to apply everywhere and for all time. For example, the laws of physics apply on earth and in outer space. In contrast, Aristotle claimed there were different principles involved in the motion of objects on earth and in the “heavens.” Things may change, but there are also laws describing how they change. Even though some laws have a limited range of applicability, that range can often be explicitly defined.

Intelligibility and rationality

Although humans have finite and fallible minds, they have the ability to discover and understand the laws of nature.

Objectivity

All observations and their analysis, interpretation, and communication involve human subjects. Yet, it is assumed that with due diligence, the results can be independent of human subjectivity.

Induction

Deduction is the rational process of making specific assumptions and then using the rules of logic to draw a conclusion. In contrast, induction is the process of moving from particular examples to making universal statements. For example, every day of my life, I have noted that the sun has risen. Everyone I ask agrees that this is true in their lives as well. Historical records confirm this to be true. And there are solid scientific reasons that verify this reality. From these observations, I induce the general proposition that for the rest of my life, and probably for much longer, the sun will continue to rise each day. Science is built on induction. Scientists make specific observations about the natural world, and from these observations they hypothesise a general proposition to hold for a wide range of circumstances beyond the initial observation.

Induction is not logically justified. Nevertheless, the success of science testifies to the value of inductive reasoning. But induction can fail when data is limited or difficult to obtain, mistakes are made in gathering or interpreting the data, or prejudice blinds someone when deciding what data to accept or reject. A famous example of the failure of induction was the existence of black swans.  In seventeenth-century Europe, this was considered an impossibility as all observed swans were white. However, in 1697 a Dutch expedition to Western Australia discovered black swans. The philosopher John Stuart Mill (1806-1873) wrote: “No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.”

On the one hand, the success of science suggests that making these assumptions is reasonable. On the other hand, why do we assume they will continue to be true, even when we have experiences, individually and collectively, where we fail to find patterns or make sense of things? In other words, we have empirical evidence that is consistent with the assumptions not always being true.

Max Planck stated that, “Anybody who has been seriously engaged in scientific work of any kind realizes that over the entrance to the gates of the temple of science are written the words: 'Ye must have faith.'” 

Do you agree these are assumptions? Would you restate any of them? Can you think of other things that scientists assume?

For my blog Google Analytics has gone weird

I occasionally look at Google Analytics for my blog in the slim hope it might tell me something about the level and nature of interest (or lack thereof) from my audience.

Until a few months ago, I thought the data was somewhat meaningful and believable. It gives data on the number of views for the whole blog and for individual posts. It also provides information for specific countries.

Until a few months ago, there were (supposedly) about one thousand page views per day. A typical post would attract a few hundred views within a week of posting and sometimes up to a few thousand after a few years.

However, the graph below shows how daily traffic increased dramatically at the beginning of August, rising to several hundred thousand views per day!

[Apologies for the poor quality of the screenshot... click on it for higher clarity]

Of course, at first I thought that this was exciting and flattering. However, I quickly realised this was ridiculous. This can be seen by looking at the breakdown by country.

Of the 10 million views from the past 3 months, 1.1 million came from Brazil, and about 300 K each from Ukraine, Bangladesh, Saudi Arabia, and Venezuela. Somehow, I don't think there are that many people in those countries interested in my blog!

Perhaps more importantly, if the traffic was real, it should translate into an increased volume of blog comments, personal emails, speaking invitations, enquiries from potential students... I have seen none of that.

The traffic must be driven by some sort of bots, troll farms, or AI site scraping. I asked Google Gemini whether AI could have increased my traffic. It responded

your traffic surge is a direct result of leveraging the content creation boom and capturing high-intent organic traffic ahead of your competitors [1].When ChatGPT launched, it created an immediate divide between blogs that adapted quickly and those that ignored the technology.

This is wrong, as I completely ignored the technology! I have never used it to write content. 

It also provided me with an unrequested "Step-by-step plan to protect my gains." [Shameless AI self-promotion].

A bit more data. The number of views of individual posts has not changed dramatically and still seems reasonable. Overall traffic on my other blog has also increased, but not in such ridiculous and unbelievable ways.

Overall, this now makes me sceptical about whether any of the Google Analytics data is meaningful. It also makes me more doubtful about the massive traffic claimed by some prominent bloggers.

This is all weird. Do you know of other bloggers with similar experience?

Lecture on degenerate Fermi gases at low temperatures

Here are the slides for an undergraduate lecture I gave today. The slides also include the derivations I gave by hand on the document viewer.

For historical background, I found a book chapter from Out of the Crystal Maze, interesting and helpful.

Some fascinating context concerning Arnold Sommerfeld. Over a period of 32 years, he built up an amazing school of theoretical physics in Munich. He mentored seven future Nobel laureates. However, sadly, when he retired in 1934 from a Chair in Theoretical Physics, it took four years to replace him because of interference from the Nazi Party. Even worse, in the end, Wilhelm Muller, a Nazi loyalist, was appointed. According to Wikipedia

"Müller, an aerodynamicist, had not been thought of as a theoretical physicist before this time, and opposed the "new" theoretical physics promoted by scientists such as Albert Einstein. His appointment is seen by historians as political, and during his tenure he would teach only classical physics"

What is so amazing about the degeneracy pressure for Fermi liquids?

 I just finished preparing my undergraduate lecture on ideal Fermi gases at zero temperature. Here are my slides. 

(Note. The derivations are not included, as I sketch them by hand on a document viewer.) (Aside. Personally, I would prefer to do derivations on the whiteboard, but this is viewed unfavourably as the video recording of the lecture will not capture that.)

A few things I find amazing about the lecture content.

Degeneracy pressure is a macroscopic quantum effect, since it vanishes in the limit Planck's constant goes to zero.

Degeneracy pressure is relevant to astrophysics. I enjoyed learning more about how it prevents the collapse of white dwarfs and neutron stars. For the first time (to my memory) I derived the Chandrasekhar limit for white dwarfs. I also enjoyed seeing how this limit is essentially the same physics as the Tolman-Oppenheimer-Volkoff limit for neutron stars.

An earlier post discussed some things I find amazing about Chandrasekhar's expression for the upper.

A few historical notes I found interesting.

It is the 100th anniversary of Fermi-Dirac statistics and the distribution function. It is impressive how Ralph Fowler immediately saw how the degeneracy pressure could explain the stability of white dwarfs. He was the PhD supervisor of Dirac, Chandrasekhar, and Mott. He was also the first to define the zeroth law of thermodynamics.

Chandrasekhar's limit was not accepted for a long time because of the opposition of Arthur Eddington. In hindsight, some consider this was a setback for astrophysics, particularly interest in the possibility of black holes, for decades. This opposition to the profound new ideas of a young PhD student by a legendary senior figure reminded me of Bardeen's opposition to Josephson. However, in that case, within a few years, experiments showed that the establishment figure was wrong.

What is your experience of using AI for research in condensed matter theory?

 I have been dabbling a little with using AI (at a very basic level) to help me with some research problems. For example, in a recent preprint, I did the maths in the Appendix with help from AI.

I am curious to hear from the experience of others of using it for research in condensed matter theory and theoretical chemistry. 

Peter Woit made a similar request to the high-energy theory community. The responses are helpful and worth reading. An earlier post about AI in math drew the following concrete suggestions from "Greg"

"In math, one thing I found useful to get my bearings was to go through some of my published papers and ask it to try and improve the main result.

Another is to do an adversarial audit of a paper you are very familiar with (yours if you have the stomach for it, someone else’s if not) and then see which of the complaints are pedantic and which are actual.

A third is to give you a history and references/citations for an argument that it provides.

A fourth is to suggest different ways to simplify an argument, even if they are only half-formed in your mind or if you are having trouble making them precise. (Try to be as precise as possible, but don’t use the same filter that you would use with a colleague. Sometimes vague is the best you can do.)

If you are thinking of proposing a question or conjecture in a grant application or preprint, ask it to stress test the conjecture. Don’t lean too much in one direction when prompting it and asking repeated questions (unless there is a particular thread you are trying to untangle, the use your nose, of course), but try to have it explore the positive direction in one query and the negative direction in a follow-up."

[A year ago, I posted about a benchmarking paper for condensed matter theory. I have not seen any updates on that work. For example, have the latest AI models improved significantly on these benchmarks?]

About a month ago, my office mate, Henry Nourse, gave a nice talk, "Is my skillset safe in an AI future?" at the UQ Condensed Matter group meeting. Henry gave a very concrete example of how he used it for a problem involving Raman scattering from quantum spin chains.

Henry stimulated me to see whether AI could help with a problem I was struggling with. It was basically a mathematics problem involving a Fourier transform. There were things that I thought were probably true, but after days of messing with algebra, my aging brain could not prove them. Google Gemini gave the answer in less than a minute. On the one hand, that is impressive. On the other hand, the absolute key was knowing precisely what question to ask it and knowing whether the anwer made sense. Furthermore, it took hours for me to check the result and to refine it, sometimes through further prompts. Besides getting the result, a nice benefit is that AI can quickly generate a LaTeX file that describes the mathematical argument and provides relevant references. Nevertheless, there are still hours more work of checking and polishing the text.

[Aside. The proof made use of various Bessel function identities that I had forgotten. I had used them extensively in a paper about thirty years ago.]

This success motivated me to use Google Gemini to develop, start to solve, and analyse a "simple" classical two-dimensional model of springs interacting with Ising spins. It is simple enough that I think most of the analysis can be done analytically. I have been working on related models on and off for the last couple of years. However, again due to my aging brain, I often get stuck on algebra or the subtleties of the theory of elasticity. AI does the algebra quickly. Furthermore, all the equations are formatted in LaTex. Great!

Again, it bears repeating that a thorough knowledge and intuition about the underlying physics is probably essential for knowing what prompts to make, and whether the answers make sense and are useful. The fact that I had struggled with related models offline beforehand really guided my interaction with AI.

Again, everything still needs to be checked. Sometimes AI makes suggestions that I think may be "hallucinations" [e.g. suggesting connections to the Svetitsky-Yaffe conjecture for lattice gauge theory] that may have arisen from earlier questions about unrelated topics. When I ask similar questions on a later day, it sometimes gives a different answer. For example, once it neglected to include the effect of one term in the Hamiltonian.

Initially, I struggled to keep records and manage the workflow. Eventually, I found it helpful to have a single LaTex file which is like a rough draft of a paper, into which I insert results. I found my engagements with AI both interesting, stimulating, and stressful. I now limit my sessions to short periods of time and then polish and edit the LaTex document in between sessions. This helps me consider what the next steps are. I am glad I am retired and can do all this at a leisurely pace.

What I am doing is incredibly basic compared to what may be possible. I am just using the free version of Google Gemini.

My limited experience raises all sorts of questions about the potential and pitfalls of graduate students using AI in research. Will it enhance or hinder their education? Potentially both?

There are some great quotes from Terence Tao in a recent New York Times article

“The effort needed to solve problems [without AI] is often very instructive. It teaches you something. It’s like going to the gym and having a goal to lift a weight a hundred times. Now, A.I. can solve questions without really getting any value out of them. It’s like having machines that can lift weights for you at the gym.”

“It feels to me like a really clever student who has memorized everything for the test but doesn’t have a deep understanding of the concept,..”

“This has some value,... But once someone shows a specific path to the waterfall, people just take that path. They don’t spend as much time looking for other paths.”

I also recommend Tao's recent lecture at the International Congress of Mathematicians.

For now, I would love to hear from others, particularly in condensed matter theory or theoretical chemistry.

What do you use AI for? What have you learned from the experience? How do you go about it? For example, how do you decide what prompts to use? How do you manage workflows? How do you document your work? What is it good at? What is it bad at? What potential benefits do you see? What pitfalls?

What is so amazing about the Fermi energy?

In two weeks, I am giving two lectures about degenerate Fermi gases in a third-year undergraduate course on statistical mechanics. The textbook is the beautiful book by Schroeder.

I want to highlight a few things that are amazing about the Fermi energy.

1. It is given by an incredibly simple expression.

E_F =

ℏ²

2m

(3π²n)^2/3

2. Besides fundamental constants, it is only determined by the number density of fermions n.

3. It is relevant to diverse systems: electrons in a metal, liquid 3He, electrons in a white dwarf star, neutrons in a neutron star, ultracold fermionic atoms such as 6Li. The table below shows that for all of these systems many of them are in the regime where the temperature is much less than the Fermi temperature. In other words, the Fermi energy (temperature) is so "large" that "low" temperatures can be very "high". 

4. Quantum effects can occur at surprisingly high temperatures. Most experiments that probe quantum effects are performed at low temperatures, often on the scale of a few Kelvin, or even less. Yet, inside a star, the quantisation of energy levels and quantum statistics can dominate the physics of the system, even at temperatures of millions or billions of Kelvin.

Note, how the temperatures of these systems span 18 orders of magnitude!

System	Typical Number Density (n/V)	Typical Temperature (T)	Fermi Temperature (TF)
Electrons in a metal	~1022 – 1023 cm-3	~300 K (Room temp)	~5 × 104 K
3He atoms in liquid 3He	1.6 ×1022 cm-3	< 1 K (Cryogenic)	~2 – 5 K
Nucleons in atomic nuclei	1.6 ×1038 cm-3	~0 K (Ground state)	4.4 × 1011 K
Electrons in a white dwarf star	~1029 – 1030 cm-3	~107 K (Interior)	~108 – 109 K
Neutrons in a neutron star	~1038 – 1039 cm-3	~108 – 109 K	~1011 – 1012 K
Fermionic atoms in an ultracold gas	~1012 – 1014 cm-3	~10 – 100 nK	~100 – 1000 nK

The table was made with Google Gemini.

Two asides.

A. An old blog post considered the interesting question of why people call these systems "degenerate."

B. A more profound question is why neglecting the interactions between the fermions (which involves energies comparable to the Fermi energy in most of these systems) can give a theory that is so successful, both qualitatively and reasonably quantitatively. Landau's Fermi liquid theory provides the answer.