How relevant are solvable models such as 2D Ising to real systems?

In summary, the conversation discusses the importance of solvable models in understanding real-world physics. While computer simulations can provide quick answers, they often introduce approximations that can be difficult to assess. Exact solutions of models eliminate these uncertainties and provide benchmarks for approximation techniques. Additionally, many materials can be accurately modeled by 2D or 1D models, making these solvable models highly relevant. The conversation also highlights the usefulness of exact solutions in comparing and validating calculation methods. Overall, while the effort to find an exact solution may be significant, it has proven to be valuable in understanding real-world systems.
  • #1
petergreat
267
4
It surprises me how much mathematics is needed to solve the innocent-looking 2D Ising model, and the Heisenberg model is quite an exercise even in 1D. I begin to doubt whether solving these models is really worth the effort, given that these systems don't look very realistic, and computer simulations give you answers much more quickly. Some calculations are useful due to universality, but others are not. So what's your opinion regarding whether solvable models of statistical mechanics are important to understanding real-world physics?
 
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  • #2
I'd say these are highly relevant.

Computer simulations generally introduce approximations of some sort, and it can be difficult to accurately assess the effects of these approximations. Monte Carlo simulations can give more-or-less exact results within statistical error, but may behave badly under certain conditions. For instance, most thermodynamic Monte Carlo simulations have difficulties at low temperatures, requiring very long computation times. Hirsch-Fye QMC behaves very badly when the interaction parameter is large. There is also the infamous sign problem for many types of QMC calculations. On the other hand, when models are exactly solved, you gain knowledge of things like the fact that the 2D isotropic Heisenberg model has no phase transition at finite temperature. It eliminates any debate about the accuracy of certain approximation methods, etc. and whether results obtained are accurate or even qualitatively correct. Also it prevents people from spending time on trying to just get a more accurate answer (xxx is only known to 3 decimal places, let's throw a big supercomputer at it and see if we can get up to 5 decimal places!). Also, an answer which is exactly known can be used to benchmark a particular approximation technique, which is especially useful if that technique can be applied to both a model for which the exact solution is known and another model for which there is no known exact solution.

Many materials display qualities that can be reasonably modeled by 2D or 1D models. The cuprates for instance have planes of CuO layers that are separated vertically by other atoms, and one can rather accurately take a 2D model of the CuO planes for examining certain types of properties. There are several materials that have chains of atoms magnetically coupled, and so are effectively modeled by a 1D model.

Just yesterday I was looking at some calculations I have done for an unusual system, the model for which under certain conditions can be reduced to look like a simple Heisenberg model. I found it very useful to be able to look up exact results for such models to compare to without having to consider any ambiguities that may arise to the the calculation methods other people used.

As is always the case with research, one cannot accurately claim a priori that a particular research endeavor will be useless. But I think these sorts of things have proven their worth. Also, keep in mind, the tremendous amount of effort that goes into an exact solution is only done once. After that, for everyone else in the future the effort is just a literature search.
 

1. How do solvable models like the 2D Ising model help in understanding real systems?

Solvable models such as the 2D Ising model provide simplified mathematical representations of complex real systems. They allow scientists to study the behavior of these systems under controlled conditions, providing insights into the underlying principles and mechanisms at work.

2. Can the results from solvable models be applied directly to real systems?

While solvable models can offer valuable insights, they are not always directly applicable to real systems. This is because real systems are often more complex and include additional factors that cannot be captured by the simplified model. Therefore, the results from solvable models should be interpreted with caution when applied to real systems.

3. How accurate are solvable models in predicting the behavior of real systems?

The accuracy of solvable models in predicting the behavior of real systems depends on the level of complexity of the system and the assumptions made in the model. In some cases, solvable models can provide very accurate predictions, while in others, they may only offer a general understanding of the system's behavior.

4. What are the limitations of using solvable models to study real systems?

One limitation of solvable models is that they often cannot capture the full complexity of real systems, which can lead to oversimplified or inaccurate results. Additionally, solvable models may rely on certain assumptions or parameters that may not accurately reflect the real system, leading to biased or limited conclusions.

5. How can solvable models be improved to better reflect real systems?

To improve the accuracy and applicability of solvable models to real systems, scientists can incorporate more complex factors and parameters into the model, validate the model's predictions with experimental data, and continuously refine and update the model as new information becomes available.

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