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Anton (Computer)

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31–40 of 48 posts

Re: Anton (Computer)

#31
post #17

Earlier quoted context omitted.

Me as well. It does seem that the whole point of being a billionaire is to do whatever you want. I can't imagine why so many seem to stick to managing their creations, which after a while can't be much fun.

Most billionaires got their wealth through inheritance. All they know is to manage the wealth creation agent that was handed down to them. Most of the self made ones too have spent a large chunk of their lives perfecting the wealth generation agent which made them rich. It would be like asking a pro NBA player to also take a shot at being a pro NFL player. It’s not what they trained for; they would need to learn a ne…

> It would be like asking a pro NBA player to also take a shot at being a pro NFL player.

I meant, the point of being rich is that you don't have to be a pro anything. You can just learn to grow radishes, or whatever suits your fancy. Or just sit and read the great books.

Re: Anton (Computer)

#32
post #29
post #28

Earlier quoted context omitted.

This explanation is interesting. Thanks for sharing it. While reading it, I got the impression that the simulation is not fully quantum mechanical, but rather classical with select quantum mechanical effects. Which parts of quantum mechanics are idealised away and how do we know that not including them won't significantly reduce the quality of the result? Are you possibly using stochastical noise in the simulations a…

That's a good question and there are a number of ways to try to tackle this. One of the main reasons you cannot do QM simulations directly is that the high quality methods can cost Omega(n^6/eps) to get eps. relative accuracy (you can do better with DFT, but then you're making your life hard in other way). At a high-level (and I mean, 50,000 ft. level), here are the simplest way: 1) Do quantum mechanics simulations o…

> Upshot: You use quantum mechanics to decide a classical potential for you (e.g. you chose the classical potential that factors into pairs such that each pair energy is 'closest' in the Hilbert space metric to the quantum rest) - Downside: You're missing the variance.

Couldn't the quantum mechanical state become multimodal such that the classical approximation picks a state that is far away from the physical reality?

And, couldn't this multimodality excaberate during the actual physical process and possibly arrive at a number of probable outcomes which are never predicted by the simulation? Is there more than hope that that doesn't happen?

Re: Anton (Computer)

#36
post #32
post #29

Earlier quoted context omitted.

That's a good question and there are a number of ways to try to tackle this. One of the main reasons you cannot do QM simulations directly is that the high quality methods can cost Omega(n^6/eps) to get eps. relative accuracy (you can do better with DFT, but then you're making your life hard in other way). At a high-level (and I mean, 50,000 ft. level), here are the simplest way: 1) Do quantum mechanics simulations o…

> Upshot: You use quantum mechanics to decide a classical potential for you (e.g. you chose the classical potential that factors into pairs such that each pair energy is 'closest' in the Hilbert space metric to the quantum rest) - Downside: You're missing the variance. Couldn't the quantum mechanical state become multimodal such that the classical approximation picks a state that is far away from the physical reality…

Yes, for sure. In practice (and not at the 50,000 ft. level), you do try to include the multimodalities — you don't _really_ just use E[quantum_energy(r)]. But you ARE still reliant on some computable/smooth/Lipschitz moment and/or expectation from the quantum surface. The semi-heuristic argument for why you get away with this in biological simulation is somewhat heuristic, but of the following form:

- Most quantum field theories are described by of the form L(E), where E is an energy level ["effective field theory"] and the Lagragian changes as E change.

   - When E is low, L(E) is classical mechanics & EM

   - When E is around 1GeV, L(E) is the aforementioned plus QED

   - When E is around 100GeV, L(E) has the aforementioned plus some QCD

   - When E is at 1 TeV, L(E) has the aforementioned plus Higgs-like stuff

Now biology is on the lowest end of that scale, so you mainly have to deal with QED and perturbative electronic expansion. These electronic expansions are the most important part — you need them to get hydrogen bonding + electrodynamic molecular interactions correct — BUT they are highly local.

This locality is what you take advantage of when you normalize — you find from QM that the potentials only matter when the two charged/polar molecules are close, so you try to make a classical potential that has quantum 'jumps' when these things are close.

Do you miss the purely quantum stuff? Aharanov-Bohm, Chern classes, and the like? Of course. But from a practical standpoint, you do get the structures that you measure from experiment to be correct because the 'cool' quantum with 'tons' of states is less important for pedestrian things at low energy scale.

It is still hard to get right though! There's a lot of entropy you need to localize correctly and in some sense, you have to make sure you get the modes as a function of local particle positions correct.

The final thing to point out is that the Wick rotated path integral stuff works for biology much better than for real HEP-type of stuff because molecules are contained at low energies — those tunneling probabilities are O(h E), and log(E) is still dwarfed by -log(h) so you _can_ safely ignore them.

This is not true for things like circuits, however, because the lithography at EUV scales (3nm -_-) does have tunneling issues at high field strengths.

tl;dr: Biology has some saving graces that give you good approximations. Are they perfect? No, but if you find a time that I have to compute a vanishing first Chern class in a noisy, ugly biological system, then you deserve a Nobel Prize!

Re: Anton (Computer)

#37
post #9

“The performance of a 512-node Anton machine is over 17,000 nanoseconds of simulated time per day for a protein-water system consisting of 23,558 atoms.[5] In comparison, MD codes running on general-purpose parallel computers with hundreds or thousands of processor cores achieve simulation rates of up to a few hundred nanoseconds per day on the same chemical system.” 17,000 ns of simulation per day sounds crazy small…

Using highly optimized code for that system size, I used to get ~500 ns per day using a single 2080. Some references: 10^-3 ns - Hydrogen bond vibrations, 100+ ns - Protein side chains moving, 1000 - 10000 ns is the timescale of protein folding

This thing was made around the time of the nvidia 300 series.

Re: Anton (Computer)

#38
post #9

“The performance of a 512-node Anton machine is over 17,000 nanoseconds of simulated time per day for a protein-water system consisting of 23,558 atoms.[5] In comparison, MD codes running on general-purpose parallel computers with hundreds or thousands of processor cores achieve simulation rates of up to a few hundred nanoseconds per day on the same chemical system.” 17,000 ns of simulation per day sounds crazy small…

I don't think it's been very helpful, since computational modeling in general doesn't seem to be a cure all for drug discovery challenges

It probably would be a cure all, but the computers are still _way_ too slow!

Re: Anton (Computer)

#39

All these years running laps around everyone else doing MD simulations - what do they have to show for it in terms of discoveries?

It is strange that their academic output isn't on par with some of the more prominent bio-molecular simulations research groups. But I don't know much about their internals, perhaps, they're leasing a good bit of computer time to biotech companies.

I’d say this is very much on purpose (I worked there for two summers, also with one of the people in this thread). DESRES is very, very particular about the papers it puts out, so, while there is an incredible amount of great science with brilliant people who were mostly poached from academia, only the very top papers ever get published. Many more are written or kept as internal documents, but the firm is very particular about only publishing very impactful research.

Unlike in academia, there isn’t a push to publish only okay or average quality research since funding is not public and there are no metrics to push.

Re: Anton (Computer)

#40
My master's thesis work was on MD simulations. My setups had around 150k atoms each, and it took months and hundreds of cores to finish any meaningful simulation. I was incredibly jealous of that machine.

But frankly I am still not convinced of the usefulness of the MD studies except for a few cases (docking studies, etc.).

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