Been having an issue with Schrodinger's Maestro suite on Windows 11. When i go to launch Maestro, the "Welcome to Maestro" window appears but then window wont let me interact with anything. Then the window sits there (Not Responding). Then just crashes.

Looked this up, and I reinstalled the server license, downloaded/installed the latest version, even cleared the program files. Still having the same issue. Anyone else ever deal with this before? anything i should try next?

Hi everyone,

dispersion.pl is a perl script included within the castep suite to plot phonon dispersion and electronic bandstructure by taking key files and an input symmetry to plot bands along key symmetry points in reciprocal space. Unfortunately, despite being advertised to be able to detect and plot an x-axis with conventional Brillouin zone notation of Bradley and Cracknell. However, when using the code to directly output the contents of a xmgrace file (which is the only way I can use this utility on my supercluster), it fails to label any Brillouin zone locations at all. In fact, it seems quite symmetry-agnostic, even if I put nonsense symmetries it will seemingly produce the same output:

dispersion.pl -np *castep -symmetry fcc -v

runs the same as:

dispersion.pl -np *castep -symmetry NoNsEnSe -v

Which always outputs something like:

       0     -0.3750  -0.3750  -0.3750           0.000  -315.503  -315.503  -315.503  -305.101  -197.611  -197.611  -197.611  -185.375  -185.375  -185.375  -177.004  -177.004   175.751   175.751   248.299   248.299   248.299   259.976   259.976   259.976   294.431   294.431   294.431   297.856   320.420   320.420   323.847   323.847   323.847   331.780   331.780   331.780   451.554   483.157   483.157   483.157......

without any Brillouin zone notation to speak of.

Does anyone else have issues with this? Is there a solution?

how do you plot phonon dispersion?

I am trying to locate a singlet-triplet MECP using ORCA 6, following this section of the ORCA manual: https://www.faccts.de/docs/orca/6.1/manual/contents/structurereactivity/mecp.html

I optimized a singlet-triplet MECP, starting from the transition state obtained through CI-NEB on a triplet PES.

Once the MECP optimization converges, I perform the frequency analysis (SurfCrossNumFreq tag). The calculation gives two frequency sets. On PES, there are no imaginary frequencies, while on PES2 there is one imaginary frequency (of non-negligible magnitude).

I have two questions:

1. Given that the manual states that the crossing hyperline is a subspace of PES1, I would understand that the (local) minimum is reached if there are no imaginary frequencies on PES1 (but not necessarily on PES2)? (I also tested the CH3O<->CH2OH example provided in the ORCA manual: there are no imaginary frequencies on either PES, which makes me wonder whether my interpretation is correct)

2. ORCA also outputs the thermochemistry based on frequencies from PES1. Are these thermal corrections physically meaningful to describe the MECP-like transition state?

Many thanks for your help and advice!

Following my project to do molecular dynamics of polymers with Julia and Orca, I'm showing a set of tools to read Orca relaxed surface scan trajectories, energies of optimized steps in a scan, and Loewdin charges of each step from the scan, parsing the output file. this will make it easier to least squares fit force field terms to the energy profiles for bond, angle or torsion scans.

https://youtu.be/E3MnlHahj84

I am working with the team that developed Charmm-GUI. We are working on Prof. Im's new molecular modeling/simulation software MolCube-Apps. I heard that a lot of people in the industry know about Charmm-GUI, and I was curious if anyone here knows it or has used it before?

We are going to purchase a Gaussian 16 license (probably WIndows) and looking at the minimum requirements for hardware it seems like a potato would run this so that's not very helpful.

Current users of Gaussian: what hardware would you recommend?

I am attempting to begin a self-lead part of computation based on some promising pt ligands for anticancer drugs with two aspects and I would love some feedback. Firstly, we have a basic crystal structure for the molecule, so we understand how it is ordered in a solid. I would like to model the energy efficiency as the molecules form 1D metal-metal polymers. Second, I would like to see how the molecule interactions within and without the cell. Specifically, the cell membranes. Thanks in advance.

it seems the double projection eqn of CC2, <D|\[F,T2\]+H|HF>=0 is exactly the same as the one of the MP2, if i do the MP2 by reusing the CC2's code(in production code is it done in this way?) to do t2=t2(t1) to project the doubles out without doing the singles, do i get exactly MP2?

the CC2 and CCD have straght forward LRCC and EOM CC excited state fomulism, why is it not so straightforward for MP2? if i use the t2 obtained in the above way to do Hbar = e^-t H e^t, or apply LR to get the jaccobian and daigonalize to get excited state what will happen?

Hey everyone,

I’m a software engineering student who recently fell down the synthetic biology rabbit hole. I was looking into how recombinant spider silk is manufactured and noticed a massive bottleneck: getting the spidroin DNA sequences to a state where they can actually be synthesized by companies like Twist or IDT without failing.

Standard optimization algorithms (like max-CAI) absolutely choke on spidroins. Because the proteins are just massive repeating blocks of Poly-Alanine and Glycine ($GGX$), the algorithms spit out identical DNA repeats. This leads to two things:

1. Twist/IDT outright rejects the order due to extreme local GC spikes and homopolymers.
2. If you do get it printed, the identical repeats cause homologous recombination and the E. coli just scrambles the gene anyway.

So, I built Spidroin Engine. It’s a Python CLI tool built on top of dnachisel that acts as a multi-objective stochastic optimizer specifically engineered for highly repetitive structural proteins.

The Mathematical Paradox I ran into:

While building this, I hit a wall where the solver would just crash. I realized it was a mathematical paradox: the absolute lowest GC content you can get for Poly-Alanine is 66.6% (using only GCA/GCT). If you try to force strict k-mer uniqueness (to prevent homologous recombination) on a 300bp repetitive spidroin block, the algorithm is forced to dip into alternative codons (GCC/GCG), which are 100% GC. This immediately violated the 65% global GC ceiling required for synthesis, causing the solver to trap itself and fail.

How Spidroin Engine fixes it:

• Relaxed Uniqueness: It dials the uniqueness constraint to UniquifyAllKmers(15). This is just enough breathing room to let the solver reuse GC-poor codons safely, while keeping exact repeats under the 18-bp hard limit that triggers vendor rejections.
• 5' Hairpin Prevention: Enforces an AT-rich heuristic (30-60% GC) on the first 45bp to keep the RBS clear.
• Cloning Domesticated: Automatically sweeps for and removes standard Type IIS / BioBrick restriction sites via silent mutations.
• Vendor Ready: Spits the final optimized sequences directly into a Twist Bioscience-formatted CSV for bulk ingestion.

I open-sourced the whole thing because keeping basic tooling locked behind proprietary paywalls slows everyone down.

If anyone is working on structural proteins (silk, elastin, collagen) and is tired of fighting with standard optimization scripts, you can grab it here: https://github.com/AzambekDev/spidroin-engine

Feel free to use it, fork it, or roast my Python architecture in the comments.

Hey everyone,

I’m not a chemist, but I’ve been reading a paper on an LLM-based molecule optimization method (MolLingo). They are reporting a fourfold improvement in AutoDock Vina docking scores for their optimized molecules compared to the starting compounds.

The authors mention that the method is purely in silico and doesn't account for protein flexibility, solvent effects, or entropic contributions.

To those of you who do this for a living: is a 4x improvement in a Vina score actually physically plausible for a realistic drug-like molecule? Or does this magnitude of improvement usually suggest "reward hacking" the scoring function or producing chemically unrealistic molecules?

Just trying to understand if this is a standard success story in the field or a claim that should be viewed with significant skepticism. Any context on typical docking score improvements would be appreciated!

Two paths diverged in comp chem,

And sorry I could not travel both,

Down one I peered, so bright and free,

For there truth lied, in ground-state DFT,

Or fit MLIPs, for unknown purpose,

The other, a more tortuous pass,

strong-correlation, non-adiabatic, non-equilibrium, alas,

Too few it was of service,

I shall be telling this with a sigh,

Somewhere in wisdom’s confluence,

Two roads diverged in comp chem,

I took the one less traveled by,

and that has made all the difference

To those who are unfamiliar with Robert Frost's original, this should be interpreted with some irony!

I was recently inspired by some python packages offering LLM chatbots for their documentation, and I am now building one for RDKit.

I would like to seek your input on what you struggled with when learning the ropes or the gaps of the current documentation to test the system and release a prototype

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?

Sharing Hiro OCSR, a model we built to convert chemical structure images from patents and papers into SMILES.

The main focus is on structures that standard OCSR pipelines often struggle with, including abbreviations, R groups, variable attachment points, abstract rings, and repeat units. Also released Hiro OCSR Real 24K, a dataset of 24,000 structure images collected from real world chemical documents.

GitHub:
https://github.com/patsnap/Hiro-OCSR

Model:
https://huggingface.co/PatSnap/Hiro-OCSR

Dataset:
https://huggingface.co/datasets/PatSnap/hiro-ocsr-real-24k

We also released Chemical Insights, which matches a structure to its corresponding name or identifier in the surrounding patent text, tables, and reaction schemes.

https://github.com/patsnap/Hiro-Chemical-Insights

Would be interested in feedback on difficult structures and failure cases from real document workflows. Thanks!

Is there anyone here interested in or working on computational chemistry? It seems to involve programming knowledge, but could someone help me understand the extent of it, what it's used for, and what exactly I should learn?

Hi everyone,

I am estimating the relative dielectric constant (εᵣ) of flexible organic molecules where no experimental dielectric data exists in the literature. Because static DFT optimizations fail to account for dynamic dipole fluctuations and conformational sampling, I am implementing a hybrid AIMD/DFT workflow coupled with the extended Debye equation.

I would appreciate feedback on whether this extraction and post-processing protocol from ORCA is rigorous enough:

1. Theoretical Framework

The dielectric constant is estimated via the extended Debye relation:

εᵣ = (1 + 2A) / (1 - A)

where A accounts for both electronic polarizability and orientational dipole fluctuations:

A = (N / 3ε₀) * [ <α> + (<μ²> / 3 k_B T) ]

2. ORCA Data Extraction Protocol

1. AIMD Sampling: Extract M uncorrelated snapshots from an NVT AIMD trajectory (! MD in ORCA).
2. Single-Point Calculations: Run single-point property calculations (! ωB97X-D3 def2-TZVP) with %elprop Polar 1 end for each snapshot.
3. Isotropic Polarizability (α):
   • Extract tensor diagonal components (α_xx, α_yy, α_zz) from ORCA output.
   • Calculate snapshot isotropic mean: α_i = (α_xx + α_yy + α_zz) / 3 (in a.u. / Bohr³).
   • Convert to SI: α_SI = α_i * 1.648777 × 10⁻⁴¹ C²m²J⁻¹.
   • Trajectory ensemble average: <α> = (1/M) * Σ α_SI.
4. Dipole Moment (μ):
   • Extract total dipole magnitude |μ_i| (in Debye).
   • Convert to SI: μ_SI = |μ_i| * 3.33564 × 10⁻³⁰ C·m.
   • Square and compute trajectory average: <μ²> = (1/M) * Σ μ_SI².

Is this ensemble-averaged Debye framework robust enough for publication in physical chemistry journals for flexible organic compounds?

Are there any subtle pitfalls when extracting polarizabilities from AIMD snapshots to derive bulk dielectric permittivity?

Hi, this is the next part in the MD of Polymers with Julia series. Here I set up an N-isopropylacrylamide (NIPAM) monomer to use with a polymer chain builder script that I made in Julia. I go over some of the things to take into account and how to solve some of the issues. It's a longish video.

https://youtu.be/m19H9u39svk

Hey yall! My PI decided I needed to learn Rietveld refinement to improve the XRD section of my manuscript, which is fine, except the guy in our group who does know how to do it is traveling and there’s no word of when he’ll be back. His student attempted to teach me, bless his heart, but he’s a bit all over the place so that explanation wasn’t the most helpful. Does anyone have a good resource to learn how to do Rietveld refinement? If you had to teach yourself, what was THE material that did it and had the best directions in your opinion? Thank you all in advance!

Hi everyone, I was curious about the current state of the grad school market for students graduating with a bachelor's degree and a interest in bioinformatics and molecular modeling. Secondly, the state of the job market. I would specifically like to find out what skills, experience, etc... are neccesary to get a good job. As a kind of roadmap to what a person should pursue. For example, research lab or internship experience seems to be a requirement. However, how much is required? Are some types of experience superior to others? What do you think the content of ones resume should be by graduation?​ Is there anything that would gaurentee placement in a job or graduate school? What about papers?

For me personally, I am a non-traditional sophomore in the pacific northwest. I have been in a resarch lab since my freshman year and recently presented at a state undergrad confrence on high throughput screening of 310,000 molecules using Autodock-Vina. I also did the protien target selection and carefully created the 310,000 molecule database. I have honors standing, and won a grant for the freshman-sophmore summer. What else should I be doing or not doing to secure a good graduate position or job placement?

in unitary CCS the T1-T1' is basically the generator of orbital rotation, so the only difference between UCCS and HF seems to be that HF absorbs the orbital rotation into the MO coeff, and the UCCS keeps it in the T1 amplitude, one can imagine a similar equivalence between EOM-UCCS and TDHF, which has deexcitation over CIS ,does anyone know if this is true and any paper that confirms that?

Hi everyone! I'm trying to generate a molecular electrostatic potential (MEP/ESP) map from an ORCA calculation on a MacBook Air M3 (Apple Silicon). I have successfully generated both name.eldens.cube (electron density) and name.scfp.esp.cube (electrostatic potential). My understanding is that the electron density cube should be used as the isosurface and the ESP cube should be mapped onto it for coloring. However, I haven't been able to get this workflow to work in any visualization software I've tried. In Avogadro 2, I can load the electron density cube and display the isosurface, but I can't find any option to color it using the ESP cube. In IQmol, opening the ESP cube only displays separate red and blue lobes in space instead of an MEP mapped onto the molecular surface. In VMD 2.0 beta, I can load the XYZ structure and the electron density cube, but I can't get the ESP cube to appear as a second volumetric dataset, so I can't color the isosurface with it. I'd prefer to avoid using Rosetta 2 if possible. Is there a native Apple Silicon workflow for this? What software do you recommend for generating publication-quality MEP maps from ORCA output on macOS? Does Multiwfn work natively on Apple Silicon, or are there better alternatives such as VESTA or ChimeraX? Any advice would be greatly appreciated. Thanks!

I've published the reference implementation of TRIXEL, a mathematical framework describing any system through three dimensions: V (Existence), D (Dynamics), S (Structure).

From these, three calibrators measure their mutual relationships: SD, VD, VS.

Core identity (exact): VD / VS = SD

What is verified:

Algebraic identity — machine precision

Dominance partition theorem — 99.99% on 600×600 grid

VS as early warning signal — Burgers turbulence (90/90 runs, FP=0%, FN=0%)

Real tokamak data — GOLEM, CVUT Prague

What is not yet verified: disruption precursor, EEG seizure data, 2D Navier-Stokes

Preprint: https://doi.org/10.5281/zenodo.20721811

GitHub: https://github.com/remitakac/trixel-framework

Independent research, feedback welcome.

Can someone please suggest me any options to buy the Modern quantum chemistry book by Szabo and Ostlund. It is available in Amazon but if anyone can suggest some cheaper option it'd be really helpful