Quantum and Computational Chemistry Discussion

  The not-so delicate balance between truncation error and roundoff error.

  Motivated by various conversations, e.g., https://sourcegraph.com/blog/the-death-of-the-junior-developer. Will add more later when I can find the links/motivation... An amusing analogy appears in startups. A Corin Wagen piece  linked to some common founder mistakes , and one of the many is choosing an obscure niche to avoid competition. So competition is good!? Ah, I am not a free market fundamentalist, but that is another topic. An interesting question is how this applies to academia: Derek Lowe commented  that interdisciplinary science is where many breakthroughs are had (and many will corroborate that) but there is also a fear that such pioneers, say "chemical biologist" grad students, will fail to achieve expertise in either field. But where is the argument? In a time when funding is especially tough, is there a pattern to reduced funding (aside the obviously targeted fields)? For instance, will interdisciplinary groups (say, enzymatic catalysis + ML) be bashed...

  Many professors in chemistry departments, especially theoretical faculty, have a PhD in physics. Some tend to like orders of magnitude, and also strange units, e.g., the  erg . Will elaborate later. 

  Ah, diffuse basis sets are important for the energies of anionic systems, but they may not even be that helpful for the geometries and cause a host of other problems. Frank Neese complains about this every now and then, and Tian Lu (aka Sobereva) also has a strong opinion .  In short, don't put diffuse functions on hydrogen (ahem, 6-311++G**) and don't use diffuse functions for cations. Oh, and if you're going to benchmark a bunch of functionals, don't add diffuse functions instead   of polarization; I will elaborate the roast later. 

See for yourselves! Gaussian: https://gaussian.com/man/ ORCA: https://www.faccts.de/docs/orca/6.1/manual/

  There is an amusing pattern in many forms of learning: " U-shaped development ." You learn a little something and are good (the real Newbie's Luck?), you learn more and your skills decline, then finally you get better again. Perhaps here is similar: you learn to calculate (manually) the standard enthalpy of formation (\Delta H_f), then  in quantum/computational chemistry you forget, then you need to calculate/report it for a collaborative work with synthetic chemists and learn it better...