.. _tutorial_mdtrajnet: Learning molecular dynamics: MDtrajNet ======================================== Back in March 2022, we introduced a novel concept of directly learning dynamics via **4D-spacetime atomistic AI models** (4D models for short). The idea is to predict the nuclear coordinates as a continuous function of time. The model GICnet was published in JPCL in 2023: - Fuchun Ge, Lina Zhang, Yi-Fan Hou, Yuxinxin Chen, Arif Ullah, Pavlo O. Dral*. Four-dimensional-spacetime atomistic artificial intelligence models. *J. Phys. Chem. Lett*. **2023**, 14, 7732–7743. DOI: `10.1021/acs.jpclett.3c01592 `_. However, this proof-of-concept work is not perfect and has many limitations. One major problem is that the GICnet model is not generalizable, i.e., it can only be trained and used for a specific molecule. Now we get a better choice, MDtrajNet, which overcomes these limitations. We also present MDtrajNet-1, a foundational model that directly generates MD trajectories across the chemical space. MDtrajNet -------------- MDtrajNet combines equivariant neural networks with a Transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories for both known and unseen systems. The errors of the trajectories generated by the foundational model MDtrajNet-1 for various molecular systems are close to those of the conventional *ab initio* MD. The model's flexible design supports diverse application scenarios, including different statistical ensemble, boundary conditons, and interaction types. See our preprint for more details: - Fuchun Ge and Pavlo O. Dral*. Artificial intelligence for direct prediction of molecular dynamics across chemical space. *ChemRxiv*. **2025**. DOI: `10.26434/chemrxiv-2025-kc7sn `_. .. note:: In this tutorial, we only talk about the Python API. Currently, the usage of MDtrajNet via input file/command line is not supported. Now, let's see how to use MDtrajNet in MLatom! Prerequisites --------------------- - ``MLatom 3.17.4`` or later - ``e3nn 0.4.4`` (no guarantee for other versions) .. note:: MLatom will download MDtrajNet-1 models for you. If the download fails, you can download it by yourself by following the error message. Tutorial ---------- Get started with examples on how to use it (:download:`notebook file ` and :download:`model file `) .. raw:: html :file: tutorial_files/tutorial_mdtrajnet/mdtrajnet.html