Thermodynamics and emergence

Novelty. 

Temperature and entropy are emergent properties. Classically, they are defined by the zeroth and second laws of thermodynamics, respectively. The individual particles that make up a system in thermodynamic equilibrium do not have these properties. Kadanoff provided an example illustrating the qualitative difference between macro- and micro-perspectives. He pointed out how deterministic behaviour can emerge at the macroscale from stochastic behaviour at the microscale. The many individual molecules in a dilute gas can be viewed as undergoing stochastic motion. However, collectively they are described by an equation of state such as the ideal gas law.

 Primas gave a technical argument, involving C* algebras, that temperature is emergent: it belongs to an algebra of contextual observables but not to the algebra of intrinsic observables.44 Following this perspective, Bishop argued that temperature and the chemical potential are (contextually) emergent.

Intra-stratum closure. 

The laws of thermodynamics, the equations of thermodynamics (such as TdS = dU + pdV), and state functions such as S(U,V), provide a complete description of processes involving equilibrium states. A knowledge of microscopic details, such as the atomic constituents or forces of interaction, is not necessary for the description.

Irreducibility. 

A common view is that thermodynamics can be derived from statistical mechanics. However, this is contentious. David Deutsch claimed that the second law of thermodynamics is an “emergent law”: it cannot be derived from microscopic laws, like the principle of testability.

Lieb and Yngvason stated that the derivation from statistical mechanics of the law of entropy increase “is a goal that has so far eluded the deepest thinkers.”  In contrast, Weinberg claimed that Maxwell, Boltzmann, and Gibbs “showed that the principles of thermodynamics could in fact be deduced mathematically, by an analysis of the probabilities of different configurations… Nevertheless, even though thermodynamics has been explained in terms of particles and forces, it continues to deal with emergent concepts like temperature and entropy that lose all meaning on the level of individual particles.” (Dreams of A Final Theory, pages 40-41)

I agree that thermodynamic properties (e.g., equations of state, the temperature dependence of heat capacity, and phase transitions) can be deduced from statistical mechanics. However, thermodynamic principles, such as the second law, are not thermodynamic properties. Furthermore, these thermodynamic principles are required to justify the equations of statistical mechanics, such as the partition function, that are used to calculate thermodynamic properties. 

Macro hints of microscopics.

The Sackur-Tetrode equation for the entropy of an ideal gas hinted at the quantisation of phase space. The Gibbs paradox hinted that fundamental particles are indistinguishable. The third law of thermodynamics hints at quantum degeneracy.

Lamenting the destruction of science in the USA

I continue to follow the situation in the USA concerning the future of science with concern. Here are some of the articles I found most informative (and alarming).

Trump Has Cut Science Funding to Its Lowest Level in Decades (New York Times). It has helpful graphics.

On the proposed massive cuts to the NSF budget, the table below [courtesy of Doug Natelson] is informative and disturbing. At the end of the day it is all about people [real live humans and investment in human capital]

APS News | US physics departments expect to shrink graduate programs [I was quite surprised the expect shrinking isn't greater].

From an Update on NSF Priorities

Are you still funding research on misinformation/disinformation?

Per the Presidential Action announced January 20, 2025, NSF will not prioritize research proposals that engage in or facilitate any conduct that would unconstitutionally abridge the free speech of any American citizen. NSF will not support research with the goal of combating "misinformation," "disinformation," and "malinformation" that could be used to infringe on the constitutionally protected speech rights of American citizens across the United States in a manner that advances a preferred narrative about significant matters of public debate.

The Economist had a series of articles in the May 24 issue [Science and Technology section] that put the situation concerning research and universities in a broader context. The associated editorial is MAGA’s assault on science is an act of grievous self-harm, featuring the graphic below.

I welcome comments and suggestions of other articles.

Pattern formation and emergence

Patterns in space and/or time form in fluid dynamics (Rayleigh-Bénard convection and Taylor-Couette flow), laser physics, materials science (dendrites in the formation of solids from liquid melts), biology (morphogenesis), and chemistry (Belousov-Zhabotinsky reactions). External constraints, such as temperature gradients, drive most of these systems out of equilibrium. 

Novelty. 

The parts of the system can be viewed as the molecular constituents or small uniform parts of the system. In either case, the whole system has a property (a pattern) that the parts do not have.

Discontinuity. 

When some parameter becomes larger than a critical value, the system transitions from a uniform state to a non-uniform state. 

Universality. 

Similar patterns, such as convection rolls in fluids, can be observed in diverse systems regardless of the microscopic details of the fluid. Often, there is a single parameter, such as the Reynolds number, which involves a combination of fluid properties, that determines the type of patterns that form. Cross and Hohenberg highlighted how the models and mechanisms of pattern formation across physics, chemistry, and biology have similarities. Turing’s model for pattern formation in biology associated it with concentration gradients of reacting and diffusing molecules. However, Gierer and Meinhardt showed that it is sufficient to have a network with competition between short-range positive feedback and long-range negative feedback. This could occur in a circuit of cellular signals.

Self-organisation. 

The formation of a particular pattern occurs spontaneously, resulting from the interaction of the many components of the system.

Effective theories. 

A crystal growing from a liquid melt can form shapes such as dendrites. This process involves instabilities of the shape of the crystal-liquid interface. The interface dynamics are completely described by a few partial differential equations that can be derived from macroscopic laws of thermodynamics and heat conduction. A helpful review is by Langer. 

Diversity. 

Diverse patterns are observed, particularly in biological systems. In toy models, such as the Turing model, with just a few parameters, a diverse range of patterns, both in time and space, can be produced by varying the parameters. Many repeated iterations can lead to a diversity of structures. This may result from a sensitive dependence on initial conditions and history. For example, every snowflake is different because, as it falls, it passes through a slightly different environment, with small variations in temperature and humidity, compared to others.

Toy models. 

Turing proposed a model for morphogenesis in 1952 that involved two coupled reaction-diffusion equations. Homogeneous concentrations of the two chemicals become unstable when the difference between the two diffusion constants becomes sufficiently large. A two-dimensional version of the model can produce diverse patterns, many resembling those found in animals. However, after more than seventy years of extensive study, many developmental biologists remain sceptical of the relevance of the model, partly because it is not clear whether it has a microscopic basis. Kicheva et al., argue that “pattern formation is an emergent behaviour that results from the coordination of events occurring across molecular, cellular, and tissue scales.” 

Other toy models include Diffusion Limited Aggregation, due to Witten and Sander, and Barnsley’s iterated function system for fractals that produces a pattern like a fern.

Here is a beautiful lecture on Pattern Formation in Biology by Vijaykumar Krishnamurthy

 

Remembering the student protestors who died 36 years ago

Memorial plaque in the Great Court of the University of Queensland today.