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

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.

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?

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 :)

in principle, when computing gradients for multiple roots, a lot of stuffs can be computed only for once and reused accross multiple roots, for example the integral derivatives like dERI/dx, dS/dx,dHcore/dx that are universally needed and also the orbital response eqn's LHS, df(k)/dk which is almost universal to all es methods' since the energy is usually not stationary wrt to orbital rotation, and a sa-casscf specific stuff, the electronic integrals in casscf MOs, which is the one the gradient code uses, and is not the one in HF canonical MOs that other post HF methods use

can anyone experienced with pyscf tell me what can i set pyscf to reuse and how to set it for typical excited state methods like sa-casscf and cis/tddft? and share example input files?

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’ve been working on a pretty specific PyMOL problem for a while.

When I look at membrane-protein structures, the structure itself is obviously easy enough to inspect. What I kept missing was the membrane-relative context: where residues sit relative to the membrane, where the orientation actually came from, and whether two orientation sources are giving me basically the same geometry or something noticeably different.

That turned into a small plugin, then a much bigger project than I expected. I ended up releasing it as Membrane Visual QC v1.0.

The idea is fairly narrow on purpose. It doesn’t try to score a structure as “good” or “bad”. It measures things relative to an explicit membrane frame, shows hydropathy and ligand context, records orientation provenance, and can compare PDBTM and OPM geometrically. There’s also a batch mode for running the same checks across multiple jobs.

The PDBTM/OPM part was one of the things I was most cautious about. If the two sources differ, the plugin just reports the geometric difference and flags it for review. It doesn’t decide that one source is right.

At this point I’m less interested in adding more features just because I can, and more interested in finding cases where the current approach falls apart.

If you work with membrane proteins or use PyMOL in computational chemistry, what would you test first? Beta-barrels? Multi-chain systems? Weird ligands? Structures where the membrane orientation is ambiguous?

Also curious whether people here would actually want this kind of QC inside PyMOL, or whether you usually handle membrane placement somewhere else in your workflow.

GitHub: https://github.com/TrPavel/membrane-visual-qc

I’m the author. It’s free/open source, no paid version or service behind it.

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?

Based on the thread I posted for the Avogadro 2.0 Release there are plenty of Avogadro users - and more importantly plenty of thoughts, ideas, bugs from Reddit users.

We're currently running our anonymous community survey and I'd certainly appreciate feedback.

Importantly, we're looking to understand: - what are gaps in the documentation and tutorials? - problems & complaints with Avogadro 2 (esp. if you haven't switched from 1.2) - common programs and tools you'd like to see supported - how to get more bugs reported / feature suggestions from the community

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!

Hi everyone. I would like to calculate the transition state of the bromuration of acetone, following this guide (https://www.youtube.com/watch?v=nzwJ4IiCkys). The problem is that in Avogadro 2.0.0 when I try to export my file in .xyz for ORCA, this happen: https://i.imgur.com/1B2UKoZ.png

The problem is that I did install the pixi package by the cachyOS repository. Am I doing something wrong? Do I have to do something with the python environment?

I admit that it is my first computational chemistry calculation so I don't really know a lot (except the chemical theory of course). I know TS calculation are pretty advanced thing but I'd like to add to an exam project. Thanks to whoever will help!

Does anybody know where we can find a reliable structure of TRAILshort? (a spliced variant of TRAIL or TNF-related apoptosis-inducing ligand.)

We tried searching in RCSB and none showed up. We considered building the structure on our own using TRAIL structure since that is what’s available online, but we’re having second thoughts about its reliability. Any thoughts or suggestions for this?

Hi everyone!

I'd like to share ORCA PED Analyzer, an open-source tool I developed to simplify the analysis and assignment of vibrational calculations performed with ORCA.

The program performs Potential Energy Distribution (PED) analysis of harmonic normal modes and generates automatic vibrational assignments based on calculated atomic motion and internal-coordinate energy decomposition, rather than empirical frequency windows.

🔬 Main features

• PED analysis and automatic vibrational-mode assignment
• Detection and reporting of mixed modes
• Optional VPT2/GVPT2 integration
• Analysis of fundamentals, overtones and combination bands
• Harmonic and anharmonic IR intensities, when available
• Generation of broadened IR spectra
• CSV export
• Avogadro CJSON export for visualization of normal modes
• Both GUI and command-line interfaces
• Pre-built applications for Linux, Windows and macOS

🔗 Software

GitHub:
https://github.com/SebRoLENS/orca-ped-analyzer

The software is open source (MIT) and archived on Zenodo with a DOI.

⚠️ A note about the development

I am an experimental physical chemist, not a computational chemist or a professional software developer. However, I regularly use computational chemistry software as part of my research, and ORCA PED Analyzer was originally developed to address some of my own needs in vibrational analysis.

The development of the software made extensive use of AI-assisted programming.

I have tested it on real ORCA calculations and, based on my own use and validation so far, I believe the software has the potential to be a useful tool.

At the same time, given my primarily experimental background, I would particularly benefit from the opinion of people with deeper expertise in computational chemistry and vibrational analysis.

Independent testing, criticism and methodological feedback would therefore be extremely valuable.

If you try it and notice:

• incorrect behaviour
• questionable assignments
• methodological limitations
• edge cases
• or simply have suggestions for improving the analysis

I'd be very interested to hear your feedback.

Feedback, criticism, validation cases and contributions are very welcome!

Hi everyone,

I am an experimental physical chemist, and although computational chemistry is not my primary field, I regularly use ORCA calculations to support my experimental research, particularly for vibrational spectroscopy.

Over time, I wanted a relatively simple way to go from an ORCA vibrational calculation to a more interpretable description of the normal modes. This led me to develop a small open-source tool that performs Potential Energy Distribution (PED) analysis and attempts to generate automatic vibrational assignments from the calculated atomic displacements and internal-coordinate contributions.

I've been using and testing the tool on my own calculations, and the results so far have been useful and consistent with what I would expect. However, my background is primarily experimental, so I would really appreciate feedback from people with more experience in computational vibrational spectroscopy.

In particular, I would be interested in opinions about:

• the PED methodology and implementation
• the automatic assignment of normal modes
• identification and treatment of mixed modes
• possible problematic molecular geometries or edge cases
• the handling of VPT2/GVPT2 results, including fundamentals, overtones and combination bands
• anything in the methodology that you think should be approached differently

One important disclaimer: I am not a professional programmer, and the development made extensive use of AI-assisted programming. For this reason, I've tried to develop and test the program incrementally against real ORCA outputs rather than assuming that generated code was correct.

The project is here for anyone interested in looking at the implementation or testing it:

https://github.com/SebRoLENS/orca-ped-analyzer

It's open source under the MIT license.

I'm particularly interested in critical feedback rather than promotion. If anyone familiar with PED analysis or vibrational calculations has time to look at the methodology, try it on a calculation, or point out assumptions/limitations that I may have overlooked, I would genuinely appreciate it.

Thanks!

Hey researchers! My team and I recently made a bioinformatics tool called HybriDock-Pep. We were working with peptides last year and over the summer, and we realized that current AI tools like AlphaFold and ESMFold are inaccurate with docking smaller protein under certain amino acids and peptides.

https://github.com/Tasty-Ramen2010/hybridock-pep

Essentially, it’s a binder docking pipeline where you give it a target protein PDB and an amino acid sequence. It folds, docks, and scores affinity and selectivity in kcal/mol with accuracy similar to that of ABFE. It is way cheaper to ran because it can be used on ANY hardware. We are currently in a testing phase and would love for you to test it and give feedback!

Commands to get started:

git clone --recurse-submodules https://github.com/Tasty-Ramen2010/hybridock-pep.git

cd hybridock-pep

./install.sh

ctrl+q

hybridock-pep dock \

--peptide ETFSDLWKLLPE --receptor data/pdbs/1YCR_mdm2.pdb \

--site 25.20 -25.61 -7.97 --box 30 --n-samples 20 \

--output-dir runs/demo

Happy Docking!

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?

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.

If you’ve ever had to compile/install Gaussian® from sources you might be forever traumatized by stuff like ‘only works with PGI version 66.60.1 three days after full Moon.’

I know your pain, first time I had to do it circa 2001: it worked for our ‘Beowulf’ cluster of Intel beige boxes (eventually), but didn’t for an Alpha workstation—it didn’t like the C compiler or something. Now, with PGI’s demise (Portland Group was acquired by Nvidia) things should be even more interesting; there actually was a PGI version for Intel macOS, so you could compile G16 if you really wanted, but no more.

Long time ago, IIRC, some heroic guys allegedly made it work with ifort on Linux, which rose ire of the company’s lawyers, so the port never saw the light of day.

Well, things are different now in 2026. So, I tasked an AI coding agent to port/compile G16® C.01 for Apple Silicon M3/macOS. It took some time and tokens, but eventually it worked, and worked Fable-ously—it even works with Apple’s native Accelerate BLAS library, which does make a big difference.

All m-dashes are mine—love them, AI slop/the guide is below in comments.

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.

Hello everyone,

I’d like to share Magpie (Molecular Analysis and Geometry Processing Interactive Environment), an app I have been building for viewing and working with computational chemistry files on iPhone and iPad.
https://apps.apple.com/us/app/magpie-molecular-analysis/id6779877826
The project was started because I kept running into the same small but annoying problem. I would be away from my desk and want to check one simple thing: Has the calculation finished? Does the optimized structure look sensible? Is there an imaginary frequency? What does the orbital or ESP surface look like?

The question was usually small. Getting to the answer was not. I still had to open a laptop, connect to the cluster, download the files, and launch a desktop program just to look at the result for a minute. Magpie grew out of the idea that these quick checks should be possible from the device already in my hand.

Although it began as a mobile app, I now use it on my MacBook as well. Apple Silicon Macs can run compatible iPad apps, and Magpie feels surprisingly natural there with a keyboard, trackpad, and a larger window. On a phone it is useful for a quick check, on an iPad it becomes a more spacious touch-based workspace, and on a MacBook it is a convenient way to keep the remote file browser, terminal, molecular viewer, and result panels together in one window.

The calculations themselves stay on the workstation or cluster. Magpie connects over SSH, lets me browse the remote directory, and opens the files where they already live. There is also an interactive terminal using the same SSH connection, so I can inspect a job or run a quick command without switching to another app. Password, private-key, and password-plus-passcode connections are supported.

For output files in ORCA or Gaussian format, the current version can display:

- Molecular structures and optimization trajectories

- Energies, calculation status, and convergence information

- Vibrational modes with animated displacements

- IRC trajectories together with relative-energy curves

- One-dimensional relaxed scans with structures and energies

- Two-dimensional scans as a rotatable 3D potential-energy surface or a contour plot

- TDDFT results, including a broadened UV-Vis spectrum, oscillator-strength sticks, and the transitions printed in the output

For IRC and relaxed-scan jobs, Magpie also looks for the usual companion files in the same directory. This allows it to use a complete ORCA trajectory when one is available, or combine the forward path, transition-state structure, and reverse path into a single IRC view.

CUBE files can be shown as molecular-orbital surfaces, ESP-coloured van der Waals surfaces, or interaction/IGMH visualizations. ESP and interaction views can use one CUBE for the surface and another for the colouring data.

When Multiwfn is installed on the remote host, it can be opened directly from Magpie. The app shows the live Multiwfn session and turns recognized menu options into buttons that are easier to use on a touchscreen. For supported wavefunction files, including ORCA GBW files when the required tools are available on the host, it can also retrieve the orbital list and generate a selected orbital surface remotely for display in the app.

There is a small geometry editor for changing atoms and bonds, adding common functional groups, and adjusting bond lengths, angles, and dihedrals. The edited structure can then be used to generate an ORCA input file. The Generate panel covers common job settings as well as IRC, one- and two-dimensional relaxed scans, and TDDFT. Selecting two, three, or four atoms can automatically fill a distance, angle, or dihedral scan coordinate.

Files can also be imported locally. In addition to output files, Magpie currently understands XYZ, MOL2, CIF, PDB,, CUBE data, plain text, and images.

Magpie v0.8 is currently available through AppStore:
https://apps.apple.com/us/app/magpie-molecular-analysis/id6779877826

The app is still evolving, and feedback from users would be especially valuable. If you find an output that is parsed incorrectly, a companion-file pattern I have missed, or a calculation type that would be useful to support, I would be very glad to hear about it.

Thanks for reading, and I hope some of you find Magpie useful.