5 ms·
How can a computer model prove the existence of something physical? Wouldn’t it have to occur in real life before they can make this claim?
by snazz 8y ago
How can a computer model prove the existence of something physical? Wouldn’t it have to occur in real life before they can make this claim?
- Circuits 8y agoAs far as I am concerned, yes but who knows with scientists now a days. I feel like their all just flying by the seat of their pants.
- dang 8y agoEven if they are, could you please stop posting unsubstantive comments to Hacker News?
- andbberger 8y agoWe know the basic rules of physics, how all the little bits interact. We also know that if you combine enough of the little bits, you'll get a system with no analytic solution. Where, provably, all you can do to learn things about is simulate it. So it's possible to discover new and unexpected behavior of a system with modeling that hasn't yet been experimentally verified. You don't know if such a phenomenon is present in the real world until you do experimental validation, but that doesn't mean your model is wrong. Maybe a more accurate statement would be 'laws of nature found to give rise to new phase of matter'.
- thereisnospork 8y agoSorry but I think the verbiage needs cleaning up. A model can predict a new phase of matter, but not discover it. E.g. dark matter has been predicted, but not discovered. All models are by definition abstractions and approximations (except perhaps the lonely H atom) so there is absolutely room to be wrong about predictions.
- jstanley 8y agoI think this means "discovered" in the sense that a mathematical proof is discovered, rather than the sense that an ancient buried artefact is discovered.
- thereisnospork 8y agoSure, but chemical and thermodynamc models fall under far less rigorous logic than a mathematical proof. To again ape physics, would it not have been wrong to say gravitational waves were discovered when they first appeared in equation? I'm on mobile so I haven't read the actual paper yet but this isn't the sort of thing that can constitute 'discovery'.
- andbberger 8y agoWell it's all semantics at this point, but I think it's fair to say that showing gravitational waves arise from the equations constitutes discovery. It's just that we recently found out that there are conditions in the universe that produce detectable gravitational waves.
- jolmg 8y agoWhat I think thereisnospork is trying to say is that you can't give the same weight to "discoveries" obtained through simulation using current models as "discoveries" obtained through observations via empirical research. The models are estimated through the observations we make through experimentation on the real world. They're not necessarily right.
- andbberger 8y agoI agree, one should distinguish the experimental confirmation from theoretical predictions. Simultaneously, the "it's just a theory"-flavor rhetoric is tiresome and small-minded. The basic theories (GR, QM) have been experimentally verified with exquisite accuracy. Theories produced with those axioms as a foundation inherit the incredible effort that has been put into verifying the underlying rules. Experimental results showing that the predictions of such a theory are wrong would be a HUGE deal. That does not indicate a problem with the theory, as much of a fundamental gap in our knowledge of how the universe works. But sure, whatever, it's just a theory Edit: clarification, this only applies to theories that only take as axioms the _fundamentals_. No approximations.
- cjslep 8y agoFor example, you can use Molecular Dynamics simulations of atoms and their interactions over time in order to compute properties of the material through the statistical mechanics magic that is the Boltzmann constant, which bridges the microscopic with the macroscopic. That's how in University I got to play with melting high level nuclear waste in molten potassium and lithium salts without having to actually physically handle any of that yucky stuff.
- metalliqaz 8y agoIn the article you will see that they started with an experimental result and turned to simulation to figure out what was going on.
- gus_massa 8y agoI can't read the research article now, but from the press article, it looks like they are explaining a known experimental result that was unexplained (or confusing) until now. IIUC in a previous real life experiment other group put a tiny amount of Potassium under very high pressure, and they uses x-ray diffraction to "see" the structure of the sample. At low temperature they saw regular arrangement of atoms that has slots (like a "sponge", but a regular sponge). In these slots there were more atoms in some fixed positions. When they increase the temperature, the "sponge" part didn't change, but the atoms in the slots began to move like in a liquid. In the x-ray you get sharp point for the atoms that are essentially still and in a regular arrangement (the points are not the atoms), but the atoms that can move or are in irregular positions don't produce sharp point. So IIUC in a previous real life experiment other group saw a change. My guess is that the atoms that can move don't drip. They keep trapped inside the "sponge" part (like a small amount of water in a towel). The dripping part is probably a bad metaphor. --- In spite we know all the quantum mechanic rules to simulate this, the problem is that the runtime grows exponentially with the number of atoms. So an exact simulation is only possible with only a few small atoms (5-10 Carbons, perhaps 10-20 Hydrogens (but not 10 Carbons and 20 Hydrogens). Potassium has a lot of electrons, so it is more difficult.) (And the simulation is not as exact as a Mathematician would like to call it exact.) There are some tricks to avoid the exponential time. Like assuming that the atoms/electrons only interact with nearby atoms/electrons. It's quite true, but not 100% true. You can also use some model of how the electrons in one atom interact with the electrons in the other atom. But electrons like to wander, specially inside a conductor, so you must consider the electrons that live between the atoms. It's a lot of fun, there are plenty of model to approximate how the electrons behave in some situation, but each model is good for one situation and bad for other situations. If you want to go down the rabbit hole https://en.wikipedia.org/wiki/Density_functional_theory https://en.wikipedia.org/wiki/Density_functional_theory , but it is not the only method to try to solve the problem. There are a few rabbit holes, because the exact problem has exponential complexity. In this work, they use a neural network to approximate the behavior of the electrons in each atom (and between atoms). I guess that they are assuming that they interact almost locally, and hopefully they can get the result in linear time instead of exponential. I'm not sure if this can be classified as DFT or other technique, or they are digging a brand new rabbit hole. It's difficult to judge the work by reading only a press article, but the abstract of the research paper says: > Calculations necessitated the development of an interatomic forcefield using machine learning, which we show fully reproduces potassium’s phase diagram, including the chain-melted state and 14 known phase transitions. It looks very promising, specially because in each phase the conditions are different and that makes the use of a single model very difficult.
- colechristensen 8y ago>How can a computer model prove the existence of something physical? By having verified models of underlying physics. Many or most physical models these days rely on some empirical knowledge so experimental verification is necessary. Year after year science is getting better at simulation.
- uponcoffee 8y agoI think some of the sibling comments do a decent job of address the how, so I'll loop back to why. Material science research can be expensive due to: material costs, specialized equipment, specialized knowledge, dealing with hazardous waste, facility requirements//specialization, competition over the said resources, etc. A computational approach greatly reduces the barrier to entry and expands your reach at the same time. Let's talk phase diagrams as an example. We have some pretty good models for calculating the phase diagrams of bulk materials, but they breakdown as we attempt to describe smaller (I.e. nano) particles. While some experimentation was/is needed to get enough raw data to model additional terms/changes that need to be applied to make the general CALPHAD approach validate at the nanoscale, we can now cheaply test theories about how size/shape/composition of nanoparticles would influence their phase diagrams. This helps cheaply narrow and guide the search space, let's say for nanosolders, that researchers would want to follow up with physical experimentation. This makes a loop, the resultant data is used to make better models which in turn helps point out potentially interesting systems to study.