* Because God said so
* Find out yourself and get a nobel prize
Either way, even if you don't know what the answers are, you can still do serious work at a higher level of abstraction.
so there is no way to extrapolate/interpolate, anything which was not directly measured is basically unknown since it could be yet another exception
or in programming language, the worse spaghetti code you could imagine, full of feature flags randomly enabled inconsistently
We know that mass of matter does not explain even half of the mass observed in the Universe; we haven't thrown out the explanations of mass and inertial gravity, but have a placeholder called "dark matter" to refer to the missing explanation.
I’m not a physicist, so I’ll let them pipe up on how much is in and out of the descriptive line, and how much is in and out of the theoretical explanation line. But I don’t know many physicists who think we’re close to “done” with either endeavor.
Dark matter is a great example.
Our understanding of gravitation didn't cleanly apply at ultra-large scales so we had to add a massive fudge factor.
You can't "go faster" than the speed of light, but space in between things can expand faster than the speed of light.
It seems like things that are "settled" regularly get an "ope, but except for this special case..." treatment.
Physics education sometimes aligns with historical evolution of the theories, mostly because that builds intuition and because the mathematical founsations of the improved theories need to be taught first. That leads to the "but in this case..." moments, but you need to realize that what you get taught as a "fix" is practically always a careful evolution that also reproduces all predictions from the less complete earlier theory.
You stopped reading after the 1800's? Schrödinger told us life is what feeds on negative entropy and that is pretty good.
Physics, whether at atomic level, or on a much larger scale, is simple enough that reductionism usually works and you can calculate behavior from first principles using a few memorized "laws"
Biology is well past the point of complexity where you can do this most of the time, unless perhaps you are at the level of aspects of cellular behavior that can be analyzed in terms of chemistry.
Chemistry is in-between physics and biology in terms of complexity. In simple cases chemistry can be explained in terms of physics, but as AlphaFold has shown when you get to a certain level of complexity (in this case protein folding) empiricism takes over and you need to perform experiments and memorize results.
I think modern science and philosophy has a reasonable understanding of what life is, even if you disagree. This is certainly more a matter of philosophy than science, but it seems the best definition of life is based on the ability of a system to actively maintain a boundary between itself and the external world, thereby combating the 2nd "law" (statistical tendency) of thermodynamics. Maybe an interesting/useful definition (which is somewhat arbitrary) also needs to involve something like consuming energy/resources from the environment.
Only in the most hand-wavey sense. Actual simulation of the cell interior with even comically coarse grained models is prohibitively expensive. It comes up a lot in neuroscience where it sure would be nice to be able to credibly simulate even a single neuron at the molecular level. (I'm several years out of date on the state of that field so it's possible someone managed to pull it off in the meantime but even if so the broader point still stands.)
Also, this is where Rutherford's "all science is either physics or stamp collecting" holds a lot of water. As you move up the science layers, the laws themselves become less mathematically rigid until by the time you get to the social sciences, explanations are all hand-waving, and all "laws" are statistical at best and empirical.
Math isn't attempting to describe a physical universe. It provides the substrate upon which such a description can be expressed and validated - found to be consistent with itself - but many valid descriptions do not describe our universe. Physics is the empirical search for the correct mathematical description of our universe.
thats just at the current state of the art...doesnt mean a complete maths cannot...its arguably debatable why physics follow some maths and why the specific constrains
Are there any papers where this possibility is explored? What does it mean to have a complete understanding of mathematics?
This is just a data problem though. From the perspective of a deterministic universe, creative works theoretically can be explained as a physics outcome (ignoring the impact of potential quantum randomness).
In other words, physics can explain Shakespeare's plays when you hand-wave away the biggest reason it cannot.
> theoretically
... meaning not in reality, but in an abstraction of reality that conveniently leaves out the hard part.
> This is just a data problem though.
The word "just" makes it sound like that data problem is a minor inconvenience, and not a fundamental obstacle.
Becoming a billionaire is simple, after all it's just a money problem.
I mean, you're right in that (leaving out quantum randomness), you could predict macroscopic outcomes based on a physics simulation that includes all elementary particles explicitly, if you assume that such a simulation can be scaled from <10 particles to macroscopic numbers. But there is no evidence that this assumption is true, so it remains an interesting thought experiment that gets confused with reality because people like to slap the "in theory" label on it.
Edit: and less universal. Physics underlies biology, chemistry, nuclear tech & more. Biology (so far) only applies to carbon-based life as we know it on Earth.
Yes, this is key in my mind. It's not really that the laws and definitions become less strict of themselves, it's that the subjects under study become less uniform. It's fine to study a few atoms in isolation and describe their features, but if you put a lot of them together they'd better be in a uniform lattice or your calculations will take more than a lifetime to complete. If you want to describe the interaction in a drop of water, you don't use the Standard Model to integrate over 3e22 baryon fields.
Yes, physics underlies all other fields. But fundamental physics is also completely untractable to solve problems in those other fields, even if Heisenberg would allow it.
DFT works in many cases, but in some cases it doesn't estimate the energy right, due to how it bypasses some correlation calculations. Bonds are extremely sensitive to energy calculations, so you need to get super close to the actual energy in order to get useful results.
Anyways, someone with more experience here could probably add more, but that's what I've picked up so far.
Right now the lab is having me get comfortable using software like Gaussian and ORCA by simulating a bifurcating reaction. This is a reaction that, depending on the catalyst's momentum, will change what site it bonds to (it makes either a 6-membered or 7-membered ring). I'm finding the intermediate states (where the molecule is most stable) and transition states (the tipping point), and then running trajectories to see which output is more likely.
Once I've finished simulating that, I should be comfortable enough with the process to jump on the bigger project, which is machine learning interatomic potential (MLIP) model distillation. There's a lot of exciting work around speeding up DFT methods by using machine learning (note this is not generative AI, it's merely predicting the molecule energy based on atomic positions). So my one year goal is to get on that project and start contributing.
My five year goal is to, well, graduate. But then I'll probably do a PhD in computational chemistry, since I'm really interested in ways to speed up and scale existing methods. My big dream is to simulate large biological systems while still having bond formation and breaking, to automatically elucidate biochemical pathways, but there's still a lot of steps in-between.
I assume you are familiar with:
https://matt.might.net/articles/phd-school-in-pictures/
I hope and pray that your research helps to make the world a better place and that the rest of us can use your knowledge to help to make the world a place which merits your research.
I haven't seen that website before, but it sounds pretty accurate from what I've heard. It's insane how high of a mountain needs to be climbed just to catch up to the state-of-the-art, and how much work is needed to push through to figure out something truly new.
Here's to making the world a better place!
To simulate a water molecule you do so with a weakly coupled SU(1) gauge theory (light does not interact with itself at tree order) problem where the masses of all constituents are orders of magnitude above the relevant energy scales (you can think of it as the electrons and nuclei and particles coming in and out of existence are contained in a renormalization scheme).
We have "good simulation models" of both, but the former is extraordinarily complicated compared to the latter for the reasons stated above.
To a useful level of accuracy we can certainly simulate water. And we can do the same for a single proton for some definitions of useful (but not other definitions).
truly ab initio methods involve figuring out electronic properties from scratch like ionization energy or bandstructure. the real issue is that we dont have exact relations for the exchange and correlation terms. we can know the kinetic energy and charge screening, but we dont know how the electrons are interacting with each other. generally the xc term is treated as a function of electron density or its gradient (see: lda, gga, meta-gga) but there are so many different ways to approximate that. different models are good for different applications also, like transition metals vs organics. and then theres the issue of basis sets (most people use gaussian basis sets that have been tuned over many years but theres also plane waves and finite element methods) which can also change results. and even once u have a decent approximation of density you can try perturbative methods (GW family, delta scf i count also) to try and improve the approximation. i am rambling and typing this on my phone. essentially yes, but often calculations are a little inaccurate. but more accuracy has a higher computational cost, which makes it hard to run larger simulations. tradeoffs of engineering. hope this was coherent.
Of course, they could still do a much better job useful providing pointers into this knowledge, instead of just handwaving over it and insisting on rote memorization.