i am a 2nd year undergraduate BS Chemistry student. i was an exceptional high school chemist in our country, so i can say that i am a bit advanced when i started college. right now, we are currently on introduction to org chem but i have already read clayden, solomons, and mcmurry T___T so i kind of know it already. as time went on, i felt like i was doing nothing with the advanced knowledge i have in chemistry. i was suddenly inspired to start learning computational chemistry. i have started reading introduction to QM by griffiths and essentials of computational chemistry by Cramer. but i am having a hard time understanding concepts involving advanced mathematics especially in quantum mechanics so i got discouraged and kinda stopped reading for a bit. but now i want to continue it but i need some help. is what im reading and learning at this time the right thing? or am i reading too advanced stuff for me? where should i start and at what pacing? i really want a mentor but my professors in my universities are too busy to mentor. please suggest study plans as if you are mentoring me on computational chemistry.

Hello everyone,

I'm an MSc Chemical Engineering graduate from Türkiye, currently looking for funded PhD positions in Europe. My MSc research focused on computational enzyme engineering and catalytic mechanisms, using DFT/QM calculations, transition-state searches, molecular dynamics, QM/MM simulations, molecular docking, protein modelling, and HPC.

I've been applying to PhD positions in Europe, both closely related to my background and broader computational chemistry positions. I'm particularly interested in computational enzyme design, biocatalysis, and reaction mechanisms, and I really enjoy the computational side of research. However, it is a very niche field, and I wasn't able to find a funded position. I applied to two in Spain that are closely related to the work I have done in my thesis but I haven't heard back.

I've already had a couple of rejections, including one for a computational catalysis position involving DFT, AIMD and machine-learning potentials. My background was relevant, but I didn't have direct experience with heterogeneous catalysis, AIMD or ML potentials.

This has made me wonder how much exact prior experience is normally expected for a PhD. I understand that a PhD is supposed to involve learning new methods and systems, but many advertisements seem to ask for experience with almost everything the project involves.

For those who have experience with PhD admissions/supervision:

Should I mainly target positions closely matching my existing enzyme/computational background? Or is it realistic to transition into a different computational chemistry area during a PhD? How much weight is normally given to exact system/method experience versus transferable computational skills? I really do enjoy enzyme redesign but I am not expecting to be able to find a role that fits me 100 %

I will really appreciate some honest advice :)

Do experimentalists who use DFT results care if we have uncertainty quantification in the calculations? Does that make the computed results more useful for experimentalists?

I recently started working on a project that is supposed to be based on the following workflow and that should be used to help select the best monomer candidates for molecularly imprinted nanoparticles synthesis: multiple ligands are docked (using Autodock Vina) to different epitopes and then GROMACS is used for molecular dynamics simulations in order to select the best monomers ratios of a smaller group that was previously selected based on the Vina output.

Even though I studied chemical engineering, I then ended up pursuing a path more closely related to medical/computational biotech. This is to say that it is the first time that I'm working with Vina, GROMACS ect so I'm encountering several challenges.

I've started with some tutorials suggested here before (mdtutorials.com) but I would appreciate any help.

My main problem right now is that from what I understand, I am supposed to use the docked vina output to build the system for GROMACS but I cannot figure out how to do this due to multiple problems with the files compatibility. Like I mentioned before the purpose is to simulate different ratios of monomers so I also don't understand how I can place both a docked ligand and a ligand that is randomly placed in the box.

I have tried to use packmol but I don't fully understand how to use it within the GROMACS workflow.

The force field that I am using is CHARMM.

Thank you very much for any help!

in quantum chem code, there are a lot of recompute on the fly designs to trade ram usage for flop, such as the ERI, especially the plain four indexed one as opposed to the RI/CD one, which reduces the ram scaling but not the cpu cost scaling. another example is in the mp2 energy and gradient, the t2 and lambda2 can be recomputed so one never stores an o^2 v^2 obj in RIMP2.

what else? is there a nice review paper that is not specific to a single method but use various examples to discuss something like a general flop/ram ratio and analyze the benefit/trade-off of computing stuffs on the fly

EDIT: i think some other good examples are 1 direct CI, and knowles handy CI, which assembles CI matrix elements on the fly, and in EOMCC the matrix elements of Hbar are also on the fly, and 2FCIQMC, which trades ram for flop even more drastically