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 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.

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.

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.

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.

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

Hi everyone,

I'm a chemistry student whose background is almost entirely in wet-lab organic chemistry, so I have little to no experience with computational chemistry. Recently, I've become interested in using ORCA to support my experimental work, but I'm not sure where to start or what level of calculations would be appropriate.

My research involves a hypothesis that a catalyst may form itseft form an intrahydrogen bonds and this cause the change in the selectivity. So I have some question:

• Whether a hydrogen bond is actually likely to form?

• How strong that hydrogen bond might be?

• Whether different solvents weaken, strengthen, or even disrupt that interaction?

Which ORCA tutorials, textbooks, YouTube channels, or online courses would you recommend?

Since I'm completely new to this field, I'm trying to avoid jumping into expensive calculations without understanding what I'm doing.

Thanks in advance!

I wanted to share a video on a project I've been working on, aiming at simple molecular dynamics of synthetic polymers using Julia and Molly.jl. The program (work in progress) is meant to automatically generate a topology given an XYZ file or string, and generate crude FF parameters based on automatically detected atom types. This information is collected in Molly formatted variables, allowing (in the future) a simulation with minimal user input. The purpose at this point is not to make accurate MD, but to generate better starting structures for subsequent MD simulations.

Please see the link below. All suggestions and comments are wellcome (especially in YT) !

Thanks!

https://youtu.be/OrYavnmyyOo

Hey guys, long-time viewer, first-time poster. My mate and I have been building an ADME-T properties prediction tool out of frustration with the current models, and we're looking for testers.

We're expanding the model I built last year, which placed top 5 in the OpenADMET benchmark and number 1 among models trained without proprietary data. So the underlying model has some credibility behind it.

It's currently in it's first iteration (we have logP, solubility, and pka, and have permeability, retention, bioavailability etc on it's way) and are looking for lab groups to test it. One thing I've added is that the model gives a confidence interval on each prediction rather than a single point estimate.

We made this account to post updates and will share new iterations based on your feedback. Short demo here: https://youtu.be/RSTNgo0o-FQ

Honestly, we're trying to solve the painpoints of these models. If you're interested in testing and providing feedback, you can message me or drop a comment.

Hi, posting this cookbook, hope it can help someone that's starting out, take whatever's useful from it and make it your own. It's a guide that walks through the whole workflow, from raw GC-MS compound data up until pre-docking, covering ADME/toxicity filtering, target prediction and network analysis along the way.

https://github.com/hierax00/network-pharmacology-cookbook

Any feedback is welcome, and if there's some specific topic I don't cover on the repo, happy to help out if I can.

I'm considering building a Threadripper workstation and was wondering if anyone has successfully run Quantum ESPRESSO (or other DFT codes like CP2K) on the newer Intel Arc Pro B60/B65/B70 GPUs.

I'm particularly interested in:

Performance compared to NVIDIA CUDA GPUs.

oneAPI/SYCL support and ease of setup.

Any code modifications or patches required.

Whether anyone has contributed to or is actively developing Intel GPU support for QE.

I'd love to hear about any real-world experience, benchmarks, or lessons learned.

Hey everyone,
I'm working on a science fair project using ssDNA aptamers and I'm stuck on the folding and docking workflow. The 3D nucleic acid folding web servers I tried keep crashing, so I'm not sure how to get a clean 3D model from a raw sequence string.
Once I get the 3D structures, my plan is to use something like HDOCK to run molecular docking against my target proteins to check the binding affinity scores.
Does anyone have advice on a reliable workflow or better tools I should use for ssDNA folding and docking? Any extra help with the project in general would also be awesome. Thanks!

I have developed MLIP Studio, a free web application for performing atomistic calculations on molecules and materials using universal machine-learned interatomic potentials (MLIPs).

For those unfamiliar with the terminology, MLIPs are models trained to reproduce quantities obtained from quantum-mechanical calculations, particularly energies and atomic forces. Many MLIPs are developed for a specific molecule, material, or chemical process and are reliable only within that relatively narrow domain.

MLIP Studio instead focuses primarily on universal or foundation MLIPs. These models are pretrained on large and chemically diverse datasets containing millions of DFT calculations, with the aim of making useful predictions across a broad range of molecules, solids, surfaces, and interfaces without requiring system-specific retraining.

They are order of magnitudes faster than DFT, although their reliability still depends on the model, the training data, and how far the system lies outside the model’s training distribution.

The idea behind MLIP Studio is to let users upload a molecular or crystal structure and explore these models directly in a browser, without first installing several computational chemistry packages and resolving incompatible software dependencies.

The app currently supports:

• energy, force, and stress calculations
• molecular and crystal geometry optimization
• vibrational frequency analysis
• cohesive and atomization energies
• equation-of-state and bulk-modulus calculations
• spin-state comparisons
• dipole moments and partial charges
• electronic density of states and gap prediction
• trajectory analysis and comparison against reference DFT data

The platform currently includes more than 60 universal MLIPs from model families such as MACE, FAIRChem/UMA, MatterSim, ORB, SevenNet, and PET. Users do not need to understand all the underlying architectures to try the basic workflows. Structures can be uploaded in common file formats or imported from PubChem and the Materials Project.

One application explored in the paper is the use of universal MLIPs to pre-optimize difficult starting structures before a more expensive DFT calculation. For the systems we tested, this substantially reduced the number of subsequent DFT optimization steps.

These models should not be treated as universally reliable replacements for electronic structure calculations. Predictions should be validated carefully, particularly for unusual bonding environments, reactions, charged systems, excited states, and chemical compositions that may be poorly represented in the training data.

The hosted version is free to use, and the source code is available on GitHub. Registration is currently required because calculations run on our server and accounts are manually approved.

Web app: https://mlipstudio.iisc.ac.in/
Preprint: https://arxiv.org/abs/2607.07606
Source code: https://github.com/mlipstudio/MLIP-Studio

I am one of the authors and the main developer, so this is a self-promotion post. I would genuinely appreciate feedback from both computational and experimental chemists, particularly regarding which calculations or workflows would make a platform like this useful to a broader chemistry audience.

PS: This is not the first software that I have developed for computational chemistry or materials science community. I'm one of the lead developers of TURBOMOLE and have also developed CrysX and PyFock.

Hi, so i have a PhD in computational chemistry. I have knowledge in performing ab initio calculations for molecular and periodic systems using various codes. I also have knowledge in global search methods for crystal structure prediction such as simulated annealing and genetic algorithms etc...

Although i know how to work on linux based HPCs I have no background in coding whatsoever. And i want to get into machine learning for materials discovery or other chemistry related applications ( i'm cool with other stuff) but i want a clear roadmap understand the basics and progress with it till i gain some profiency. Thanks alot

what are the open source packages that are most complete and advanced in terms of energy and gradient availability of EOM-CCSD and CC2 CC3 excited state