MLatom 3.1.0 is released
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.
MLatom 3.0.1 is released
We have released the version 3.0.1 of MLatom with minor upgrades that improve the stability and user experience. In this update, we fixed some bugs in the previous version. Also, we included the docstrings in the code, which might be …
Postdoc Position for Extending MLatom to Machine Learning-Based Simulations of Condensed Matter
Location: Xiamen University, China Duration: 2 years (with possible 1-year extension) Xiamen University is offering a fully-funded 2-year postdoctoral research position in the field of machine learning-based simulations of condensed matter. This opportunity is available within the research group of Prof. Pavlo …
Documentation for MLatom Python API is out!
The new MLatom 3 release comes with the versatile Python API. We are happy to announce the release of its documentation which is available at http://mlatom.com/docs.
MLatom 3: 10-year anniversary edition is released!
We are happy to announce that on the occasion of its ten-year anniversary, we have released on September 12 a brand new MLatom 3 with tons of new features including more simulation options, Python API for user-customized workflows, and its …
MLatom 3: 10-year anniversary edition is released! Read more »
SMLQC-2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry
The second edition of the International Symposium on Machine Learning in Quantum Chemistry will be held in person in Uppsala from 29 Nov. to 1 Dec. 2023. More updates to follow on the Symposium website smlqc2023.com!
(p)KREG Models for Accurate Molecular Potential Energy Surfaces
Yi-Fan Hou, Fuchun Ge, Pavlo O. Dral. Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces. J. Chem. Theory Comput. 2023, 19 (8), 2369–2379. DOI: 10.1021/acs.jctc.2c01038. Blog post ›
WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets
Max Pinheiro Jr*†, Shuang Zhang†, Pavlo O. Dral, Mario Barbatti*. WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets. Sci. Data 2023, 10, 95. DOI: 10.1038/s41597-023-01998-3. Blog post ›
Explaining and Predicting Two-Photon Absorption with Machine Learning
Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou*, Pavlo O. Dral*, Cheng Wang*. Interpretable Machine Learning of Two-Photon Absorption. Adv. Sci. 2023, 2204902. DOI: 10.1002/advs.202204902. Blog post ›
Evaluating AIQM1 on Reaction Barrier Heights
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo Dral*. Benchmark of General-Purpose Machine Learning-Based Quantum Mechanical Method AIQM1 on Reaction Barrier Heights. J. Chem. Phys. 2023, in press. DOI: 10.1063/5.0137101. Blog post ›