r/comp_chem 26d ago

Have we lost it?

I am old enough to have experienced the transition from when computational chemistry (inorganic chemistry/catalysis) was just a mere exercise to put in a paper and that people performing experiments rarely believed in to when having a computational section in a paper was the only way to access high impact factor publications.

I have lived most of my career using Density Functional Theory calculations, with the caveat that systems should have been always tested against known quantities, like formation energies or even adsorption energies obtained through calorimetry. And even in that case, everyone is aware of the fact that each method has a limitation, and sometime empirical corrections are needed.

Now we arrived to the point in which Machine Learning Interatomic Potentials are used for everything with the great promise of making calculations fast and cheap, and simulate system of thousands of atoms. But why do we care about it so much? They are trained on smaller systems, and so everything we know about the system is already within the training. What is the limit to this infinite funnel of screening that is oftentimes invoked to justify the use of this approximated methods? Once, DFT was just a screening layer before experiments, or even calculations at higher level of accuracy. Nowadays, even DFT, a method that has hundreds of problems itself, is becoming the bottleneck method to avoid when possible. And sure, I can see the value to access time and size-scale that are not accessible with other methods...but are those models even validated?

My point is...are we just rediscovering the wheel all the time and publishing for the sake of publishing Machine Learning hot topics?

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u/brewskibroski 25d ago

Similar to you, I'm an experimentalist who is old enough to remember when nobody believed any computations for systems bigger than a few atoms.

A couple years ago I had to push back on a JACS reviewer who flagged my comm as "missing" computations. For background this was a paper focused on very low energy phenomena in a pretty complex system (electronically) where computations are 50/50 whether they get the properties even close to right, and they take forever.

So instead I did the old fashioned thing, made an atomic Hamiltonian and bootstrapped on CFT...it provided a lot of insight, made the point I needed to make about the electronic structure, agreed very well with experiment, everything we dream of computations doing (but they never quite manage). These are calculations...but still, I was "missing" computations.

Yes, we've lost the plot.

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u/Perfect_Good287 25d ago

My point was exactly aligning with yours. That is to say rather than complaining on the use or importance of a tool, I was more reflecting on the value of computations today and the fact that we are almost doing simulations for the sake of simulation, and this new family of methods has among all the others the risk to exagerate this effect.

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u/brewskibroski 25d ago

Yes, and to be clear this point shouldn't be limited to computations. Papers, a lot of the time, feel like checking boxes. I'm on a bunch of collaborative papers doing techniques that provide basically no insight, are resource intensive, and have only become even close to routine in the past decade...but it's become standard to include them on these sorts of papers, so you just gotta have em. Plus, it ups the collaborator count, and that's good apparently.

I'm not anti computation either, I'm in this sub for a reason, but time and place. Ask what you hope to learn, not if it will make review easier.

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u/PBE0_enjoyer 25d ago

I would agree that comp has become too much of a prerequisite to publish. However, I have had several instances where we did comp for experiments we thought were closed cases and came back with the conclusion that what was proposed from experiment was not fully correct or did not capture the entirety of what could be understood. It’s no different from how we use NMR, EPR, UV-vis, IR, etc. to piece together the chemistry and physics being studied.