We’re glad to share that, following multiple requests, the July 2025 Edition of the hands-on online course Computational Chemistry & AI by Pavlo O. Dral is now available free of charge for academic users. 🎓

📚 The first edition (June 2024) was well received — one participant shared:

“Dr. Dral offers a fantastic introduction to the concepts around machine learning in chemistry!”

The course is based on the author’s experience teaching university courses and running hands-on workshops. Since the field is evolving rapidly, the course has already been updated several times.

2. 📂 Click the course icon in the left pan

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✅ If you meet the eligibility criteria — especially having a verifiable academic affiliation (such as using your university email when registering) — your application is usually approved within 24 hours.

If you find the course helpful, we’d really appreciate your feedback, suggestions, and testimonials (contact@aitomistic.com).

And if you know colleagues, students, or friends who might benefit from it, please share this announcement with them.

Thank you for helping us support the academic community! 💬📨

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The July 2025 Edition of the hands-on online course Computational Chemistry & AI by Pavlo O. Dral is now available free of charge for academic users.

Free Academic Access to Computational Chemistry & AI (July 2025 Edition)

Researchers at Aitomistic has published a 

 in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface hopping dynamics, while tackling many key issues making machine learning of excited states difficult. The protocol introduces a new machine learning interatomic potential based on multi-state learning and accelerated sampling of the critical regions near conical intersections. All the methods, together with the end-to-end active learning are available in 

Quantum chemical simulations of molecular excited states are important for understanding the photochemical and photophysical processes, but they are very computationally intensive, especially when used for nonadiabatic dynamics. Machine learning is an attractive tool to tackle this problem by replacing expensive quantum chemical calculations, however, the progress in ML for excited states is much slower than for ground states due to the complexity of the former (see, e.g., 

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To address the key challenges in machine learning of excited states, this work has reported a comprehensive and robust protocol for ML based on an eight-year-long (!) in-depth research. This protocol is based on a multi-state learning model, MS-ANI, capable of learning excited states of various molecules with unprecedented accuracy. The core idea is to take the state number as an input feature, concatenated with the ANI atomic environment vectors, and passed to the neural network for the corresponding element type. Impressively, the accuracy of learning excited states is better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. 

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One of the key ingredients to train a robust potential for nonadiabatic dynamics is accelerated sampling of the regions near conical intersections. We have achieved this in this work by introducing gapMD, which ensures enhanced sampling of the low-gap regions by propagating trajectories with the gradient energy difference between adjacent surfaces when needed.

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Together, these advances enabled end-to-end active learning for generating machine learning models to perform nonadiabatic simulations.

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For simple cases such as fulvene, this active learning is very fast and converges just after three days on a single GPU card.

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Active learning also works for more complex cases and, e.g., can be used to calculate photoreaction quantum yields. We have also discovered long-time-scale oscillations in cis-azobenzene photoisomerization.

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In this work, we have also shown for the first time that a single model can be used to perform the nonadiabatic dynamics of different molecules. This has laid the foundation for our follow-up study on the first-ever universal excited-state potential OMNI-P2x.

Video lecture about the theory and life-recorded tutorial:

Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska*, Mario Barbatti*, 

 npj Comput. Mater. 2025, 11, 132. DOI: 10.1038/s41524-025-01636-z.

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The protocol introduces a new machine learning interatomic potential based on multi-state learning and accelerated sampling of the critical regions near conical intersections. 

npj Comput. Mater.: Efficient Machine Learning Protocol For Accelerating Trajectory Surface Hopping Dynamics

Do you want to perform computational chemistry simulations but struggle with complex codes and lack computational resources? Are you conducting experiments without theoretical guidance and feel lost? No worries, the help is on the way!

We are pleased to announce Aitomia: a user-friendly intelligent assistant with expert knowledge in AI-driven atomistic (“aitomistic”) and quantum chemical simulations that is designed to help you in setting up, running, and analyzing atomistic simulations. It leverages the power of our existing software ecosystem 

, imparting Aitomia an impressive range of capabilities encompassing geometry optimizations, spectra simulation, molecular dynamics, and reaction modelling, and much more.

Aitomia is the first intelligent assistant publicly accessible online on a cloud computing platform for atomistic simulations of broad scope—

—where you can register and try it out for free! In a video example below, the user on Aitomistic Hub asks Aitomia in Chinese what the structure of hexanol is. The Aitomia returns the structure which the user can analyze in the Hub’s GUI. Later, the user asks to calculate the IR spectrum of hexanol, which Aitomia promptly calculates and delivers to the user in the form of a spectrum. Remarkably, the spectrum simulations are not just fast but also rather accurate compared to the experiment due to the state-of-the-art AIQM models.

If you are interested in installing Aitomia (and more!) locally to ensure full privacy of your calculations, it is also possible. Please contact us at contact@aitomistic.com, if you have any inquiries. You can also 

 to the news about Aitomia, its upcoming releases and subsequent upgrades, and other news by Aitomistic.

The details about Aitomia are described in a 

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A user-friendly intelligent assistant with expert knowledge in AI-driven atomistic (“aitomistic”) and quantum chemical simulations.

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Are universal machine learning potentials for excited states possible?

Such a potential would be a major breakthrough — enabling key applications like the design of advanced photomaterials.

We’ve already seen successful universal potentials for ground states — ANI-1ccx, MACE-OFF, our own 

 — you name it. Some of these go back to 2017.

But 8 years later, not a single universal potential exists for excited states. So… are there fundamental obstacles preventing them?

 with quality approaching TD-DFT — but in milliseconds, orders of magnitude faster.

 — the simulation finishes in a flash. You can try it yourself online, either on Aitomistic Hub or with a Jupyter notebook on the 

affordable excited-state molecular dynamics

, something existing ML models struggle with.

Want us to go deeper? A full tutorial or even a live Q&A session? Let us know in the comments!

As with any new approach, questions remain —

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Are universal machine learning potentials for excited states possible? We’re excited to share: they are possible. We’ve built the first-ever universal excited-state potential — OMNI-P2x.

Meet OMNI-P2x — the First Universal ML Potential for Excited States!

 with on-demand resources such as AI models, QM methods, ML and QM software for aitomistic simulations, GUIs, learning materials, and much more. On the Hub, you can run aitomistic simulations without installing any software and collaborate with your colleagues. Aitomistic Hub is a perfect starting point, with free trial resources subject to their availability.

We understand that the trial resources might not satisfy your aitomistic simulation needs. Hence, we offer flexible solutions based on your requirements and resources. You may choose appropriate subscriptions to more resources, i.e., CPU and GPU time, storage, and unlocking more features.

If you like Aitomistic Hub but want to use your hardware recourses with the local, customized, and private version of Aitomistic Hub, we have got you covered too! Check out our Aitomistic Lab powering the Hub.

Last, but not least: do you have a cool software that you want to showcase to others and make available for calculations online? Contact us, and it might be integrated into the Aitomistic Hub.

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Beta version is live! Try it now and be among the first to explore the latest features—early adopters who provide feedback will get a chance at free perks and discounts for future versions!

News

Free Academic Access to Computational Chemistry & AI (July 2025 Edition)

npj Comput. Mater.: Efficient Machine Learning Protocol For Accelerating Trajectory Surface Hopping Dynamics

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Meet OMNI-P2x — the First Universal ML Potential for Excited States!

Aitomistic Hub Launched!