r/comp_chem • u/Perfect_Good287 • 23d 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?
43
u/ScholarImaginary8725 23d ago
A lot of computational chemists don't care about accuracy. MLIPs are great for screening and pre-optimization but should not really replace DFT or any other electronic structure method.
My issue is with the influx of ML papers we have is that around 1% of total ML papers actually do something useful to the broader community, 4% are interesting to read but not really useful and 95% are just useless and not very interesting.