Comments URL: https://news.ycombinator.com/item?id=47083882

Points: 73

# Comments: 15

https://docs.google.com/document/d/1pe_gEbhVDgORtYsQv4Dyml8u...

Comments URL: https://news.ycombinator.com/item?id=46919361

Points: 7

# Comments: 0

The field of molecular and biological simulation is far more than simply "Newtonian mechanics". There is indeed a field called molecular dynamics (MD) that relies on "classical mechanics" yet it's defined usually in the Lagrangian formalism. Furthermore, there has been tons of work over the past few decades in developing more accurate numerical approximation algorithms. There is a ton of a theory in this field and if you're interested, the "MD Bible" is "Understanding Molecular Simulation" by Daan Frankel.

Now, MD is just the tip of the iceberg. Almost all chemistry simulations are built entirely from making subtle approximations to quantum mechanics and carefully building up frameworks. For example, Hartree-Fock theory (HF), Density Functional Theory (DFT), Couple Cluster theory (CCSD(T)), etc. Then there is a field known colloquially as semi-empirical methods which are a sort of combination of the above two methods. And that's just on the side of chemical simulations (i.e. I'm excluding physics-specific simulations etc).

And now, more recently there has been effort in building machine-learned interatomic potentials, machine-learned density functionals, equivariant graph neural networks, etc etc.

If you're still interested in these class of problems, consider trying to build a good model for OMol25: https://arxiv.org/abs/2505.08762

[0] https://neoneye.github.io/arc/?dataset=ARC-AGI-2

A similar exmaple is "Make something people want". This is generally true advice in focusing your efforts on solving customer's problems. Yet, this is disastrous advice if taking literally to the fullest extent (you can only imagine).