[2023-01-19 update: there’s now an expanded version of this comment here.]
Note: I’ve edited this comment after dashing it off this morning, mainly for clarity.
Sure, that all makes sense. I’ll think about spending some more time on this. In the meantime I’ll just give my quick reactions:
On reluctance to be extremely confident—I start to worry when considerations like this dictate that one give a series of increasingly specific/conjunctive scenarios roughly the same probability. I don’t expect a forum comment or blog post to get someone to such high confidence, but I don’t think it’s beyond reach.
We also have different expectations for AI, which may in the end make the difference.
I don’t expect machine learning to help much, since the kinds of structures in question are very far out of domain, and physical simulation has some intrinsic hardness problems.
I don’t think it’s correct to say that we haven’t tried yet.
Some of the threads I would pull on if I wanted to talk about feasibility, after a relatively recent re-skim:
We’ve done many simulations and measurements of nanoscale mechanical systems since 1992. How does Nanosystems hold up against those?
For example, some of the best-case bearings (e.g. multi-walled carbon nanotubes) seem to have friction worse than Drexler’s numbers by orders of magnitude. Why is that?
Edges also seem to be really important in nanoscale friction, but this is a hard thing to quantify ab initio.
I think there’s an argument using the Akhiezer limit on Qf products that puts tighter upper bounds on dissipation for stiff components, at least at “moderate” operating speeds. This is still a pretty high bound if it can be reached, but dissipation (and cooling) are generally weak points in Nanosystems.
I don’t recall discussion of torsional rigidity of components. I think you can get a couple orders of magnitude over flagellar motors with CNTs, but you run into trouble beyond that.
Nanosystems mainly considers mechanical properties of isolated components and their interfaces. If you look at collective motion of the whole, everything looks much worse. For example, stiff 6-axis positional control doesn’t help much if the workpiece has levered fluctuations relative to the assembler arm.
Similarly, in collective motion, non-bonded interfaces should be large contributors to phonon radiation and dissipation.
Due to surface effects, just about anything at the nanoscale can be piezoelectric/flexoelectric with a strength comparable to industrial workhorse bulk piezoelectrics. This can dramatically alter mechanical properties relative to the continuum approximation. (Sometimes in a favorable direction! But it’s not clear how accurate simulations are, and it’s hard to set up experiments.)
Current ab initio simulation methods are accurate only to within a few percent on “easy” properties like electric dipole moments (last I checked). Time-domain simulations are difficult to extend beyond picoseconds. What tolerances do you need to make reliable mechanisms?
In general I wouldn’t be surprised if a couple orders of magnitude in productivity over biological systems were physically feasible for typically biological products (that’s closer to my 1% by 2040 scenario). Broad-spectrum utility is much harder, as is each further step in energy efficiency or speed.
[2023-01-19 update: there’s now an expanded version of this comment here.]
Note: I’ve edited this comment after dashing it off this morning, mainly for clarity.
Sure, that all makes sense. I’ll think about spending some more time on this. In the meantime I’ll just give my quick reactions:
On reluctance to be extremely confident—I start to worry when considerations like this dictate that one give a series of increasingly specific/conjunctive scenarios roughly the same probability. I don’t expect a forum comment or blog post to get someone to such high confidence, but I don’t think it’s beyond reach.
We also have different expectations for AI, which may in the end make the difference.
I don’t expect machine learning to help much, since the kinds of structures in question are very far out of domain, and physical simulation has some intrinsic hardness problems.
I don’t think it’s correct to say that we haven’t tried yet.
Some of the threads I would pull on if I wanted to talk about feasibility, after a relatively recent re-skim:
We’ve done many simulations and measurements of nanoscale mechanical systems since 1992. How does Nanosystems hold up against those?
For example, some of the best-case bearings (e.g. multi-walled carbon nanotubes) seem to have friction worse than Drexler’s numbers by orders of magnitude. Why is that?
Edges also seem to be really important in nanoscale friction, but this is a hard thing to quantify ab initio.
I think there’s an argument using the Akhiezer limit on Qf products that puts tighter upper bounds on dissipation for stiff components, at least at “moderate” operating speeds. This is still a pretty high bound if it can be reached, but dissipation (and cooling) are generally weak points in Nanosystems.
I don’t recall discussion of torsional rigidity of components. I think you can get a couple orders of magnitude over flagellar motors with CNTs, but you run into trouble beyond that.
Nanosystems mainly considers mechanical properties of isolated components and their interfaces. If you look at collective motion of the whole, everything looks much worse. For example, stiff 6-axis positional control doesn’t help much if the workpiece has levered fluctuations relative to the assembler arm.
Similarly, in collective motion, non-bonded interfaces should be large contributors to phonon radiation and dissipation.
Due to surface effects, just about anything at the nanoscale can be piezoelectric/flexoelectric with a strength comparable to industrial workhorse bulk piezoelectrics. This can dramatically alter mechanical properties relative to the continuum approximation. (Sometimes in a favorable direction! But it’s not clear how accurate simulations are, and it’s hard to set up experiments.)
Current ab initio simulation methods are accurate only to within a few percent on “easy” properties like electric dipole moments (last I checked). Time-domain simulations are difficult to extend beyond picoseconds. What tolerances do you need to make reliable mechanisms?
In general I wouldn’t be surprised if a couple orders of magnitude in productivity over biological systems were physically feasible for typically biological products (that’s closer to my 1% by 2040 scenario). Broad-spectrum utility is much harder, as is each further step in energy efficiency or speed.
Nice, I don’t think I have much to add at the moment, but I really like + appreciate this comment!