Do you need to quickly determine if a molecule does internal conversion or want to show nonadiabatic dynamics for your #compchem students?

MLatom@XACS provides full Python pipelines for surface hopping with QM and #ML.

Check our JCTC with @PavloDral.
doi.org/10.1021/acs....

🚀 MLatom 3.18.3 released!
Updates include:
✨ MACE-OFF model zoo interface
✨ Raman spectra plots (Python)
✨ Extended ML model for 2-photon absorption spectra

📄 Notes: mlatom.com/docs/release...

🔗 aitomistic.xyz

bars by allowing the execution of a large ensemble of trajectories. Our implementations are available in open-source MLatom. [5/5 of https://arxiv.org/abs/2505.23344v1]

A quick video demonstrating the key steps in the manual #compchem calculation of the reaction barrier and energy with AIQM2 using #MLatom online on the #Aitomistic Hub ( aitomistic.xyz ).
Detailed tutorial: mlatom.com/docs/tutoria...
Paper on AIQM2: doi.org/10.1039/D5SC...

goals by exploiting fine-tuned open-source large language models (LLMs), rule-based agents, and a retrieval-augmented generation (RAG) system. Aitomia leverages the versatility of our MLatom ecosystem for AI-enhanced computational chemistry. This [3/5 of https://arxiv.org/abs/2505.08195v1]

Did you know that you can run advanced #ML and common DFT #compchem calculations on Aitomistic Hub at www.aitomistic.xyz via simple MLatom input files?
Submitting MLatom Python scripts or launching Jupyter notebook also works!

Detailed tutorials at mlatom.com/docs .

Two of my PhD students graduated!
Lina Zhang did an incredible job of progressing the ML surface hopping dynamics.
Fuchun Ge developed novel NN methods directly predicting MD trajectories, and did lots of work on MLatom and ML potentials.
Each of them have 10+ publications!
dr-dral.com/people

Want to run #compchem simulations with state-of-the-art AI models? It is very simple with MLatom input files.

For example, geom opt in MLatom with AIQM2 takes just 3–4 lines.

Run it online 👉 aitomistic.xyz
More: mlatom.com/docs/tutoria...

obtained from the machine learning model MLatom/AIQM2 is considered. The sinNN PES model obtained from MLatom/AIQM2 energies was able to reproduce available experimental fundamental vibrational transition energies with a root mean square error of [6/8 of https://arxiv.org/abs/2504.21381v1]

about 17 cm-1. sinNN combined with sparse grids sampling approach proves to be of practical use for the chosen system, both at refitting and direct fitting tasks. The ability of MLatom/AIQM models to accurately reproduce global PESs beyond equilibrium [7/8 of https://arxiv.org/abs/2504.21381v1]

It is always nice to see creative ways the users apply our methods and software (UAIQM & #MLatom) to solve their #compchem problems:

www.sciencedirect.com/science/arti...

You can use them online too at the @aitomistic.com
Hub.