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 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.

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

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 :)

Hi! I am a second year in college majoring in math and chemistry. I’ve been doing theoretical chemistry research for around 6 months.

I understand that, as a second year, I can’t really get into novel method development or those sort of things. However, I am also bored by what I’m tasked to do: run calculations all day. I’ve been trying to understand the theory behind the calculations, reading Szabo & Ortlund. Still, however, I find my task to seem rather boring and not intellectually stimulating.

I am afraid that, if I end up pursuing theoretical chemistry, my day to day will look like this.

Is theoretical chemistry for me? Can any PhD students in theoretical chemistry give their insight?

I would appreciate it!

Hi. I need help with a CBS extrapolation. Can't ask anyone for help bc nobody I know can help me. Therefore:

I did DLPNO-CCSD(T1) calculations with orca (see sull inputs below). My system is a C-C dimer which breaks into two identical C radicals and I want to benchmark the electronic reaction energy.

I used cc-pVDZ and cc-pVQZ basis sets for the extrapolation and will extrapolate SCF energy (parameter 4.42; exponential fit) and correlation energy (parameter 2.46; polynomial fit) separately. I will use the formulas and fitting parameters of

Frank Neese, Edward F. Valeev; Revisiting the Atomic Natural Orbital Approach for Basis Sets: Robust Systematic Basis Sets for Explicitly Correlated and Conventional Correlated ab initio Methods?. J. Chem. Theory Comput. 11 January 2011; 7 (1): 33–43. https://doi.org/10.1021/ct100396y.

Now to the problem. I did the calculations with ORCA (input see below) and when I checked if E(SCF)+E(CCSD)+E(T)=E(final single point) I found that the value was off by -0.03378. It seems that for the MDCI ORCA did not use the final SCF energy but a "reference energy" which is exactly -0.03378 off of the SCF energy. (SCF energy: -585.730392772957; E(0) of the MDCI: -585.696614349) My LLM of choice could not sufficiently explain, where this comes from. The whole ORCA output file does not contain this exact value (I searched for 0.03378 and -0.03378).

Now to the questions:

1. What is this energy difference? Why is it here and where does it come from?
2. Which correlation energy should I use for the extrapolation? I would choose E(CCSD)+E(T) (values see below).
3. Which SCF energy should I use for the extrapolation? I would have used the "reference energy" E(0) which was used in the MDCI (values see below).

Examples for a radical monomer calculation:

Total SCF Energy:

----------------
TOTAL SCF ENERGY
----------------

Total Energy       :       -585.73039277295754 Eh          -15938.53429 eV
...

Correlation energy part 1 (CCSD iterations):

--- The CCSD iterations have converged ---

E(0)                                       ...   -585.696614349
E(CORR)(strong-pairs)                      ...     -1.982761334
E(CORR)(weak-pairs)                        ...     -0.002944788
E(CORR)(corrected)                         ...     -1.985706122
E(TOT)                                     ...   -587.682320471
Singles norm <S|S>**1/2                    ...      0.154748668 ( 0.062079079, 0.092669589)
T1 diagnostic                              ...      0.018629550
<S**2>(linearized)                         ...      0.7533235 (ideal value:      0.7500000)

Correlation energy part 2 (triples correction):

Triples Correction (T)                     ...     -0.086625089
    alpha-alpha-alpha ... -0.002628018 (  3.0%)
    alpha-alpha-beta  ... -0.041032413 ( 47.4%)
    alpha-beta -beta  ... -0.040372285 ( 46.6%)
    beta -beta -beta  ... -0.002592373 (  3.0%)
Final correlation energy                   ...     -2.072331210
E(CCSD)                                    ...   -587.682320471
E(CCSD(T))                                 ...   -587.768945560

ORCA Input for a radical monomer:

!UHF DLPNO-CCSD(T1) cc-pVDZ cc-pVDZ/C TightPNO

%scf
  maxiter 1000
end

%pal
  nprocs 12
end

%maxcore 8500

*xyzfile 0 2 a0of.xyz

ORCA Input for a closed shell dimer:

!DLPNO-CCSD(T1) cc-pVDZ cc-pVDZ/C TightPNO

%mdci
  UseFullLMP2Guess false
end

%scf
  maxiter 1000
end

%pal
  nprocs 12
end

%maxcore 8500

*xyzfile 0 1 A0of.xyz

Feel free to criticize my input files and point out bad practices. I like to learn and improve. And thanky for the help :)

Last night I released a video simply discussing what I have been doing lately with my program for molecular dynamics with Julia. It was different in that it was just me talking. I have thought for long on making more videos just discussing aspects of computational chemistry, research, academia, etc, but I always feel that I'm not expert enough in any of the particulars I want to talk about. However, I do have a lot of things I would like to say. So I was just wondering if anyone has some general questions that you would like me to address in future videos. It can be about methodology or more general philosophical types of questions. If I feel confident, after doing some reading I can perhaps answer them in short videos.

P.s. I won't post a link, and if you don't know who I am you can search in some of my other posts in the sub

I have a master’s in computational chemistry, and I’ve been wondering whether pursuing a PhD in this field is really worth it.

One thing that frustrates me is that computational chemistry seems to require a huge amount of training compared with the reward you get afterward.

A computational chemistry student may need to learn quantum mechanics, statistical mechanics, physics, mathematics, Linux/HPC, programming, and specialized software, on top of chemistry itself. The learning curve can be very steep. A new student may spend months just figuring out how to set up calculations correctly, understand the theory behind them, troubleshoot jobs, and decide whether the results are actually meaningful.

Of course, wet-lab fields like organic or analytical chemistry are also difficult and take years to master. But it seems to me that beginning wet-lab students can often become productive relatively quickly by working alongside experienced students and learning established experimental procedures. In computational chemistry, it can take much longer before a student can work independently because there are so many different technical and theoretical skills to learn.

What makes this feel unfair is that the job market doesn’t seem to reward that extra breadth of training. Dedicated computational chemistry positions are relatively limited, and many require a PhD plus a high level of expertise. A company may need a large team of synthetic, medicinal, or analytical chemists but only a few computational chemists supporting them.

So computational chemists may have to learn more different things, spend longer becoming independently productive, and reach a higher level of specialization—only to compete for fewer positions.

I’m not saying computational chemistry is harder than every wet-lab field, or that experimental chemists have easy jobs. I’m questioning the effort-to-reward ratio.

For people working in computational chemistry, do you think this perception is accurate? Is getting a PhD in computational chemistry still worth the time and effort, or would you choose a different field if you were starting again?

Wondering how many of you actually use machine learning in your research? Anything ranging from MLIPs for MD simulations to drug discovery tools as starting points for drug design.

Going through the sub‘s history, I saw that the main challenges were the extensive data requirements and false confidence of current ML tools. Are there any other key limitations that you guys consider prevalent in the field?

Just trying to scope out some opinions for a project. DMs are welcome and I would love to chat with any researchers for 5-10 minutes if possible (not trying to sell anything don’t worry). Thanks!

I'm interested in researching new molecule for organic semiconductor, but the DFT programs like Psi4 or gaussian is toooooo heavy for my macbook. I used the digitalocean droplet a bit, but it was not satisfying speed for me. Can anyone tell me what should I do?

the CC lagrangian generally takes the form L = E + lambda' R(t) + z' f(k) where t is the amplitude and the k is the orbtial rotation, the R is the residue eqn of the cc projection eqn and the f(k) is the term related to orbital response

to solve the multipliers lambda and z the full adjoint eqn is a system of 2 eqn and 2 unknowns, 0=pL/pt=pE/pt + lambda' pR/pt and 0= pL/pz =pE/pz +pR/pz + pf/pz

in practice typical code like psi4 does not genuinely solve this system of adjoint eqn together, but rather the lambda eqn first and then the response one, this is achieved by takes pR(t)/pk =0 so that z does not influence the lambda eqn

my question is, is pR(t)/pk =0 an approximation or is it exact? if exact, why?

im kinda new in QE. I relaxed my structure. These are the new infos:

CELL_PARAMETERS (alat= 5.80712838)

0.862642072 -0.498046633 0.000000000

0.000000000 0.996093265 0.000000000

0.000000000 0.000000000 3.257006109

ATOMIC_POSITIONS (crystal)

Si 0.0000000000 0.0000000000 0.1879688714

Si -0.0000000000 0.0000000000 0.6879688714

Si 0.3333333333 0.6666666667 0.4379113697

Si 0.6666666667 0.3333333333 0.9379113697

C -0.0000000000 0.0000000000 0.0002675223

C -0.0000000000 0.0000000000 0.5002675223

C 0.3333333333 0.6666666667 0.2498522367

C 0.6666666667 0.3333333333 0.7498522367

I want to input this in a new .in file: How to read this so I won't mix up the conversion? I need it in this format:

&SYSTEM

ibrav = 0

nat = 8

ntyp = 2

ecutwfc = 30

celldm(1) = 5.80712838

/

CELL_PARAMETERS alat

0.863257294 -0.498401831 0.000000000

0.000000000 0.996803662 0.000000000

0.000000000 0.000000000 3.259720036

ATOMIC_POSITIONS crystal

Si 0.0000000000 0.0000000000 0.1879688714

Si -0.0000000000 0.0000000000 0.6879688714

Si 0.3333333333 0.6666666667 0.4379113697

Si 0.6666666667 0.3333333333 0.9379113697

C -0.0000000000 0.0000000000 0.0002675223

C -0.0000000000 0.0000000000 0.5002675223

C 0.3333333333 0.6666666667 0.2498522367

C 0.6666666667 0.3333333333 0.7498522367

Do i just copy paste? Thanks in advance!

Edit: When I do calculation='vc-relax', i have the explicit cell_parameters in the .out file. Meanwhile, in calculation='relax', I don't. Do I use the same CELL_PARAMETERS from the .in file? However, the variable in $SYSTEM / is A instead of celldm(1), so i'm kinda confused with the conversion.

Hi everyone,

Over the past few months, I’ve been building VORA-X—an open-source, local-first biomolecular AI platform designed to predict 3D protein structures entirely on local infrastructure without relying on paid external cloud APIs.

Why I Built This

While cloud structure prediction platforms are powerful, early-stage drug discovery and biotech research often require complete data privacy for proprietary targets, zero recurring per-prediction API costs, and full local control over the underlying neural architecture.

Key Architectural Components:

• Sequence Representation: Uses local Meta ESM-2 (fair-esm) transformer embeddings extracted directly from raw FASTA inputs.
• 3D Structural Engine: Score-based SE(3) diffusion backbone modeling rigid-body spatial transformations (SO(3) unit quaternions + 3D coordinate translations).
• Local Operations: Built entirely on PyTorch tensor math and open model weights (incorporating Chai-1 parity principles).
• Validation & Metrics: Evaluated using Kabsch alignment for RMSD structural comparison and internal pLDDT residue confidence scoring.
• Stack: Python, PyTorch, SciPy, NumPy, with a FastAPI backend and Streamlit / Web UI.

Context: about to start a masters in comp chem, pretty certain I’ll want to continue into a PhD etc. in the field but my current laptop (2020 MacBook Air with intel cores) is on its last legs and am planning on getting a new computer.

Currently choosing between getting another mac (pro this time) which would hugely improve on my current laptop. Alternatively I could get a Neo and build a pc for home use.

Any suggestions about any of these options would be wonderful (including pc recs)

One last thing is that I should have access to an HPC quite easily as comp at my uni is very well established.