AI-enhanced computational chemistry
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 »
MLatom 3.4.0 has been released on 29.03.2024. This release is a major release with usability improvements: Contributed to this release: Pavlo O. Dral, Yuxinxin Chen, Fuchun Ge, and Yi-Fan Hou.
We have held online broadcast on April 24, at 15:30 Beijing time/9:30 am CET on the XACS Youtube channel at https://www.youtube.com/watch?v=TOVmwgId-eA. In the broadcast, we have demonstrated how MLatom@XACS can be used for accelerating expensive quantum chemical simulations via efficient building …
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, …
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, …
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We have developed a robust protocol and demonstrated its feasibility for calculating stimulated emission contributions in transient absorption pump–probe and 2D electronic spectra of pyrazine. …
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou. MLatom 3: A …
MLatom 3 for AI-enhanced computational chemistry: JCTC paper and online tutorial Read more »
We have released the version 3.1.1 of MLatom with improvements in the performance and bug fixes.
Equivariant potentials are the (relatively) new kid on the block with promising high accuracy in published benchmarks. One of them is MACE which we now added to the zoo of machine learning potentials available through the interfaces in MLatom. See …
We have released the version 3.1.0 of MLatom with new interface to MACE, one of state-of-the-art machine learning potentials that feature equivariant message passing neural networks.