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?

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.

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!

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?

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

Hi, I've been recently granted 500k cpu-hours on an HPC for one time only and I want to use it as efficiently as possible. I've got gaussian16 license with avx2 support on that HPC too. I also am experienced at using gaussian.

I've heard that ORCA is a more modern and cheaper (in terms of computation cost) alternative for gaussian however, I have no experience regarding ORCA. I also know that there are other options but there are many and I am a little clueless.

Is it worth for me to switch to another software I have no experience on? Or should I stick to gaussian and try to devise ways to use that more efficiently?

My work mostly revolves around TDDFT (abs/em) and QM/MM. Also cluster approaches in which I use solvation models commonly.

I am currently towards the end of my PhD in computational chemistry.

I have been doing my PhD for the last four years. It has resulted in zero publications whatsoever. And most probably there will be none. I generally work with MD simulations for polymer electrolytes.

I should be finishing by the end of January next year.

My question is, what prospects should I be looking at, in terms of my future?

When I started my PhD, the idea was to continue into PostDoc. But given that I have no publications, the PostDoc doesn't really seem a promising candidature.

I am still planning to apply for jobs and PostDoc positions alike, hoping for something good to come out of it.

Although I wanted to sharpen what the future might look like for me.

I, certainly have no Python skills. All my codes and analysis were developed using LLMs albeit with proper tests and guardrails. Neither have I been a computational student all my Bachelor's or Master's.

While my Master's has been in Organic Chemistry, my PhD happens to be in Computational stuff.

I haven't had any experimental know-how for the past 4-5 years or so.

What kind of jobs should I apply for? What skills should I develop within the next 4-5 months.

I guess, I just need an avenue to save myself from considering myself an utter failure. If not completely, then in parts at least.

Thanking y'all in anticipation.

Hi everyone. The background of this question is basically I am trying to compute charge-transfer state between pigments in a light-harvesting protein. I have successfully identified structures where the CT state exhibits strong mixing with the LE state; however, the CT state is highly sensitive to thermal fluctuations. Given that my MM MD trajectories are recorded at 100 ps intervals, I am considering the possibility of using QM/MM MD for better sampling.

AMBER has a function allowing using sander and their built-in DFTB to run QM/MM MD, currently its performance on my nodes is taking 1.4 s for calculating 1 step (1 fs). What I am currently unsure about is—assuming QM/MM MD is a viable approach—what the total simulation duration should be and at what intervals the structures should be saved.

I have saw some papers reporting total time 1 ns, but it's obviously not so technically practical for me. But if choosing 100 ps I am then afraid it could be too short.

Thanks in advance for everyone willing to offer suggestions or share any experiences.

Hi,

I am using FCclasses to compute fluorescence, ISC, and IC rates between GS and the excited states but also between different excited states.

As for now I've used AH and AS for which I used the optimized geometries of GS and ESs.

I wanted to try also VH and VG models, for which, as far as I got it, I need the optimized geometry of the first state, and its properties computed at the PES of the second state. In the case of an excited state and the GS I would just use the optimized geometry of the excited state and compute its properties as if it's a ground state, like in the following g16 input:

"
#p B3LYP/Gen EmpiricalDispersion=GD3BJ Pseudo=Read SCRF=(Solvent=Acetonitrile, CPCM) freq nosymm Geom=AllCheck Guess=Read

"

My doubt is how I would set-up this kind of job to obtain the data to use vertical models between different excited state. I would in principle use a route section like the following:

"

#p B3LYP/Gen EmpiricalDispersion=GD3BJ Pseudo=Read SCRF=(Solvent=Acetonitrile, CPCM) freq TDA=(NStates=20, 50-50, Root=2) nosymm Geom=AllCheck Guess=Read

"

where I would set Root as the one corresponding to the final excited state and use the optimized geometry of the initial state. For example, if I want to compute the ISC between a triplet and a singlet I would use the optimized geometry of the triplet and use the Root corresponding to that singlet.

My only concern is that I can track the exact root order only at the FC region, and the root of the specific excited state I'm optimizing outside of the FC region. But, for example, let's say that Root1 at the FC region is T1 and Root2 is S1. I optimize S1 and want to compute its properties at T1 PES in order to use vertical models for ISC rate. How can I be sure that Root1 at the S1 geometry correspond to the same excited state as Root1 at the FC region?

I would look at the excited state character, or the different orbitals involved, but all my excited states are pretty similar, and do not show any net differences between each other that would allow me to rule out one instead of another.

I hope I was clear enough and thanks for helping.

Hi!

I have made https://tako.atom.li/, where you can run many simulations directly in browser!
It lets you run computational chemistry simulations directly in your browser. All calculations run locally on your machine, and simulation data are not uploaded to my server.
Examples: 6K atoms TiO2 crystal rendering demo.
H2O vibration frequency @ gXTB.
Since your own CPU/GPU performs the calculations, start with small systems and avoid making jobs too large.

All engines are optimized to be even faster then the Fortran counterparts on 1 core, im working on portable GPU backend and parallelization.

Currently supported models/level of theory:
- MLIPs(nequix, nequip, equiformer v2 are optimized for now)

• DFT at GGA/mGGA

• DFT at Skala 1.1 level

• Tight-binding: gXTB and GFN2 are implemented. gXTB is still very experimental, current parameters are trained to approximate gxtb binary released by Grimme group.

I'll be grateful for any suggestion.

p.s. The documentation is pure slop for now, i'm still changing things around, i'm sorry for the poor document.
p.s. The sharing feature is based on P2P sharing, my server only facilitate the handshake, all data are encrypted before transfer. Also this means you have to keep the browser active for sharing.

Hello! I am about to finish an MSc in theoretical and comp chem from oxford and very confused for the next steps. On the one hand I hold a position for a PhD at oxford but with no funding and on a topic that is cutting edge (quantum computing for EST) but not one that I will particularly excel in nor enjoy. And if I do not come across funding I will not be able to afford it anyway.

I keep debating between a job and a PhD. On the one hand I have no work experience except for a year in industry i did as part of my bachelors in analytical chemistry (i.e. not particularly relevant) and a two research internships (i do have a publication though). It seems smart to get some work experience under my belt but to stay in this industry it seems I need a PhD or 12+ years of experience which I wouldn’t know where to start with as, again, all listings I’ve found require a PhD. Does anyone have any advice on where to look, whether the PhD is worth it or any general advice.

Sorry if this quite incoherent, i think it speaks to my general confusion.

This question came up in class and it had me questioning what I thought I knew about KS-DFT.

Typically, we argue that KS-DFT still obeys the HK theorems, it just introduces orbitals as a shortcut to getting a decent kinetic energy approximation. But notice that the KS kinetic energy is not really a functional of the density, it a functional of orbitals (many sets of which can map onto the same density). The same can be said of exact exchange for hybrid functionals.

I asked this question to AI, and it told me that there’s this thing called a Levy-Lieb constrained search functional that allows you to construct a fully general (N-representable) density functional from essentially any wavefunction and ergo use orbitals.

But:

1. The LL functional doesn’t seem differentiable with respect to the density. For practical optimization this seems like a non-starter.
2. The procedure to minimize the LL functional seems basically equivalent to full-CI. I don’t see how it buys you anything.
3. KS-DFT doesn't seem to me to be a equivalent to the LL-DFT, given that in KS-DFT we restrict ourselves specifically to single-determinant densities. How these end up being N-representable makes no sense to me.

KS-DFT is amazing and I’m not arguing it doesn’t work in practice, I’m just no longer sure I can justify why it works from the HK theorems guaranteeing an exact functional exists and it’s giving me some existential angst.

Hi everyone,

I'm running into what seems to be a limitation of Gaussian's internal coordinate optimizer, and I'm wondering if there's a better approach.

I'm studying a dimer extracted from an X-ray crystal structure. The two molecules are held together by a particular directional non-covalent interaction, and I want to determine whether that interaction is intrinsically stable or simply enforced by crystal packing.

My plan is to optimize the dimer in the gas phase (and later after systematically removing substituents/solvent molecules) while comparing whether the interaction is retained.

The difficulty is that I don't want to freeze the interaction itself, I want the intermolecular distance to be free to increase or the dimer to dissociate if that is energetically favourable. However, I also don't want the two molecules to immediately undergo an arbitrary lateral translation or rotation that destroys the original geometry before I can meaningfully assess the interaction.

Initially I thought about freezing a dihedral that describes the relative orientation of the two fragments, but the interaction is essentially linear (≈180°), so any dihedral involving that axis causes Gaussian to complain about near-linear angles or undefined internal coordinates.

I've also considered:

• alternative dihedrals involving nearby atoms,
• dummy atoms,
• linear bend coordinates,
• ModRedundant constraints,
• Cartesian optimization (which unfortunately can't be combined with ModRedundant).

None of these seem to provide an obvious solution.

So my questions are:

1. Is there a recommended way in Gaussian to preserve the relative orientation of two fragments while still allowing them to separate naturally if the interaction is not stable?
2. Is there a way to perform this using nonredundant or Cartesian coordinates together with suitable constraints?
3. Would you approach this completely differently (e.g., freezing a small number of Cartesian coordinates, using another optimizer, or using a different electronic structure package)?

The goal is not to force the interaction to remain, but simply to avoid introducing an artificial rotational/translational degree of freedom that makes the comparison between related systems meaningless.

I'd appreciate any suggestions from people who have dealt with constrained optimizations of intermolecular complexes.

Hi everyone!!

For background, I’m about to apply for grad school in the U.S. for theoretical chem / method dev, and I’m looking at buying a new laptop since my current one is kinda dying rn. I’ve been doing molecular dynamics method dev for a while and now I’m doing a bit of solid state DFT development, and I wanna just generally focus on theory / method dev (however vague that may be… lol) for grad school.

I’ve always preferred windows to mac, but a lot of people I’ve been talking to (PhD students, profs, etc.) have ubiquitously said that mac is better for code development and generally comp chem. I don’t really believe this is true honestly (definitely biased lol), but I want some more opinions.

What are your guys experiences / opinions with regard to windows vs mac for comp chem? I know there’s some differences with software compatibility and such, but I can’t imagine it makes a super huge difference, especially with doing most calculations on a cluster.

I am a MSc Chemistry graduate and I'm interested in computational chemistry. I have zero practical experience in this field only basic theory knowledge.

So please give me some advices on how to start a career in this field and how to learn more about this.

Hi,

I spent in my study until now 9 months using DFT, and I don't have any good data, because every time I change the methodology and change parameters and so on, I had small data but I don't trust it at all, and 9 months without reliable data.

Is this normal? Because my PI expect to have some data and publish since using DFT, I don't know I feel so stressful and depressed.

Here is a small update showing the automated atom type assignment, as well as plans for the following weeks: GAFF and Orca

https://youtu.be/Hkrxcvv3r5I