
FermiNet: State of the art approx of molecular orbitals - jagiammona
https://twitter.com/pfau/status/1169981588412817409
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elcritch
I’m really excited to read the paper for this! I’ve been pondering for a while
now how well DNN’s would model complex QC wave functions. Much high level
quantum chemistry research involves human theoreticians finding “quirks” and
other features which allow computing specific properties like say estimating
quantum tunneling in photosynthesis. These involve high level symmetries or
various green’s functions which help correlate to different domains. But most
simulation only use DFT (roughly discretized orbitals) or other “naive”
optimization methods over a pure numerical solution of the ache of infer
equations.

Still running true ab initio QM simulations of a few atoms can take months on
a single computer (never had a chance to run simulations on a cluster or GPU).
DNN’s however have the ability to find higher dimensional patterns difficult
for humans to find but which could significantly speed up QM simulations.

Currently doing QM simulations of chemical reactions for any number of
reactions is in feasible but if work like FermiNet could make it feasible for
small teams to simulate more complex chemical reactions it could open up an
entire field of chemical/industrial processes to startups. As in you could
reasonably simulate chemical processes sufficiently to optimize current
process or find entirely novel reactions. This would significantly reduce the
capital expenses most research in these areas require.

In short if I were a VC I would be _very_ keen I’m watching this field. There
tremendous value hidden behind this general problem.

~~~
zinclozenge
> In short if I were a VC I would be _very_ keen I’m watching this field.
> There tremendous value hidden behind this general problem.

As someone who used to be in this very space and even tried to get a startup
off the ground based on it, I can tell you with absolute certainty that this
will lead absolutely nowhere.

The short of it is that literally no business will accept data that is
generated this way until someone shows that every neural net model trained in
this way produces solutions that are mathematically equivalent to a validated
method.

At best it might be used as a filter step in some pipeline, but that's not
going to have much of an effect, and certainly not something on which to bet
the success of a startup.

~~~
jhrmnn
This approach is guaranteed to give better answers than pretty much anything
from standard quantum chemistry. Its limitations are (i) computational cost
and (ii) the fact that many material/drug design questions cannot be answered
with quantum chemistry only.

~~~
densefunction
The problem is that you don't know what the sources of error are. In methods
like density functional theory (DFT), or other post Hartree-Fock calculations,
the sources of error are well understood. We know where the predictions break
down and we know what can be relied upon.

Methods like this are difficult to verify. You don't know where the weaknesses
in the model actually are and you don't know what is reliable. This is an
interesting idea but has limited application and, even if the model can be
understood well-enough to determine the limitations, this will not replace
methods like DFT due to the cost of the calculations.

DFT is imperfect due to the limitations of the functionals and basis-sets but
we know what it does well and that is a lot. It is reliable when used by
someone that understands the sources of error and how to apply the methodology
to the the target system in the most appropriate way.

~~~
jhrmnn
Are you familiar with quantum Monte Carlo? This is just variational QMC, a
well established method, with a neural network as an ansatz. Traditionally QMC
uses ad-hoc ansatzes anyway, so this is not different.

Also, I‘ve spent last seven years doing DFT calculations, and although
sometimes one can explain the failures, more often than not it‘s just
intransparent. QMC is actually in the core of DFT, because QMC calculations on
the uniform electron gas have been used to parametrize LDA in DFT.

~~~
densefunction
I'm not particularly familiar with QMC, although I do know of it.

I was just adding a comment in support of this not being a particularly
revolutionary methodology because, although it may achieve spectacular results
for certain systems, the limitations and mistakes of this kind of approach are
completely hidden behind an opaque ML methodology of an NN.

ML has a place but NN are notoriously difficult to even grey-box and a black
box model doesn't do much to actually advance the field. It certainly doesn't
allow for a well-rounded assessment of failures.

As for the limitations of DFT, unless you are referring to convergence issues,
I think you are completely wrong to claim that the issues with the method are
not well-known and understood. We know precisely where the methodology has
limitations and we also know how the functionals have been parametrised and we
also know the assumptions / theoretical models upon which they are based and
their limits. That is enough information to know where confidence can be
placed.

I would also dispute that QMC is in the core of DFT just because it is used to
parametrise LDA. As I am sure you are aware, LDA is not used for any reliable
modelling. GGAs and Hybrids (And maybe meta-GGAs if we're feeling
charitable...) are what make DFT a useful theory. Prior to that the results
just sucked for the majority of systems!

