Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine learning potentials. It shortens molecular simulation time to a couple of days which could have taken weeks of pure quantum chemical calculations.
The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in collaboration of Professor Pavlo O. Dral (Xiamen University) and Assistant Professor Arif Ullah (Anhui University) was published in Digital Discovery. In this blog, Arif Ullah highlights this …
Physically-consistent quantum dissipative dynamics simulations with neural networks Read more »
A year ago, we released MLatom 3, making MLatom the fully-fledged Python package. This shift endowed MLatom with much of its current flexibility that also allowed us to rapidly do method development and new implementations: in one year we had …
XACS team in collaboration with Mario Barbatti and groups in Warsaw University and Zhejiang lab has recently published a paper in JCTC about the versatile Python implementation of surface-hopping dynamics. This implementation is based on a powerful MLatom ecosystem for …
JCTC: Surface hopping dynamics with QM and ML methods Read more »
Institution: Department of Chemistry, Xiamen University, China PI: Prof. Dr. Pavlo O. Dral (http://dr-dral.com) Duration: 1–3 years Application deadline: 15 June 2024 The group of Prof. Dr. Pavlo O. Dral is seeking an excellent postdoc for one of the topics: …
Postdoc in method development for materials or molecular simulations Read more »
A machine learning potential with low error in the potential energies does not guarantee good performance for the simulations. One of the reasons is that it is hard to train machine learning potentials with balanced descriptions of different PES regions, …
I have presented on March 20, 2024, the ongoing journey towards making excited-state simulations more accessible with the help of AI/ML. The video recordings and abstract of my talk at VISTA are now available online. About VISTA The bi-weekly seminar …
VISTA: Towards more accessible excited-state simulations with AI Read more »
MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum mechanical methods, machine learning, and their combinations. We are happy to announce that we will release the new upgraded version of MLatom 3.3.0 that …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more »
MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide variety of representative potentials. These potentials include: You can train and optimize the hyperparameters of these machine learning potentials using flexible options. We also …
Training and using machine learning potentials with MLatom@XACS Read more »
We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release notes for details. How to get it? Download zip, check this version on PyPI and GitHub. pip install mlatom==3.2.0 What’s new? This is a major release with many new features, …