Pavlo O. Dral

Theoretical study on accurate and affordable molecular IR spectra calculations with the AIQM methods available on our Aitomistic Hub (aitomistic.xyz) was recently published in J. Phys. Chem. A.
Paper: doi.org/10.1021/acs....
Video recap: youtu.be/hkzM5qC8njI
#compchem #mlchem #aichem

Talk ✅ #iupac2025 #compchem #mlchem #aichem

Just landed in Kuala Lumpur to attend #IUPAC2025. If you are there and going to the conference dinner or otherwise want to meet - drop me a message ☺️

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

Presenting Aitomia (mlatom.com/aitomia/), available on @aitomistic.com Hub (aitomistic.xyz), at a huge MRS meeting in Xiamen.

Great conference to meet so many friends and colleagues and learn the latest trends in the field of materials design!

when two decades of experience in computational and quantum chemistry, and machine learning meet the powerful #LLM!

YouTube video by Prof. Pavlo O. Dral Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

#aichem #compchem intelligent assistant Aitomia for #Aitomistic (AI+aitomistic) & quantum chemical simulations
More info at: mlatom.com/aitomia

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning - npj Computational Materials npj Computational Materials - Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning

npj comp mat #CompChem 🧪 with @pavlodral.bsky.social

doi.org/10.1038/s415...

YouTube video by Prof. Pavlo O. Dral Meet OMNI-P2x — the First Universal NN Potential for Excited States! #AI #chemistry #neuralnetworks

Meet OMNI-P2x — the First Universal #ML Potential for Excited States! #compchem #aichem #mlchem #neuralnetwork

youtube.com/shorts/sMr7Z...

- Preprint: doi.org/10.26434/che...
- Tutorial: github.com/dralgroup/om...
- Aitomistic Hub: www.aitomistic.xyz

#MachineLearningScienceandTechnology game 👾: Who can find @kylecranmer.bsky.social (easy) and @pavlodral.bsky.social (a little harder) in this picture? 🤩

now my students make fun of me, after reading the preface to my online course on #AI in #compchem 😅

AI-driven discoveries: Machine Learning for the Physical Sciences workshop   - IOP Publishing AI-driven discoveries: Machine Learning for the Physical Sciences workshop IOP Publishing and Fudan University are organising a one-day international workshop, “AI-driven discoveries: Machine Learning...

I am pleased to give a plenary talk at the workshop “AI-driven discoveries” @ioppublishing.bsky.social. I will be giving the talk in-person this Sunday in Shanghai, but it will also be streamed online via Zoom and KouShare: ioppublishing.org/ai-driven-di...

AIQM2: Organic Reaction Simulations Beyond DFT Density functional theory (DFT) is the workhorse of reaction simulations but it either suffers from prohibitive cost or insufficient accuracy. In this work, we report AIQM2, the universal AI-enhanced QM Method 2, the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the outstanding speed of AIQM2, orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction.

#RobSelects preprint of the week #ChemRxiv: Combining semiempirical quantum chemistry with transferable neural network potentials and an atom-pairwise dispersion correction in AIQM2 #aichem https://doi.org/10.26434/chemrxiv-2024-j8pxp-v2

thanks for sharing!

Accurate and Affordable Simulation of Molecular Infrared Spectra with AIQM Models Infrared (IR) spectroscopy is a potent tool for identifying molecular structures and studying the chemical properties of compounds, and hence, various theoretical approaches have been developed to simulate and predict the IR spectra. However, the theoretical approaches based on quantum chemical calculations suffer from high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (e.g., semiempirical methods orders of magnitude faster than DFT). Here, we introduce a new approach, based on the universal machine learning (ML) models of the AIQM series targeting CCSD(T)/CBS level, that can deliver molecular IR spectra with accuracy close to DFT (compared to the experiment) and the speed close to a semiempirical GFN2-xTB method. This approach is based on the harmonic oscillator approximation with the frequency scaling factors fitted to experimental data. While the benchmarks reported here are focused on harmonic IR spectra, our implementation supports anharmonic spectra simulations via molecular dynamics and VPT2. These implementations are available in MLatom as described in https://github.com/dralgroup/mlatom and can be performed online via a web browser.

AIQM methods are a viable alternative to existing DFT and semi-empirical #compchem methods. #ML methods can be robust and trustworthy!

In our recent article, we show AIQM application for accurate and fast IR spectra simulations:

doi.org/10.1021/acs....

YouTube video by Prof. Pavlo O. Dral Theoretical interpretation of experiments is sensitive to the QM method and, often, ambiguous

The highlighted publication by the Czech group shows how sensitive the interpretation of the experimental results is to the choice of the #compchem method.

Paper: doi.org/10.1021/acs.....

#photochemistry #research #scienceexplained #science #researchpublication

www.youtube.com/watch?v=Z4qF...