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 ›
Roundup of MLatom’s Year 2022. What to Expect in 2023?
MLatom team wishes you Happy Holidays and all success in New Year 2023! We use this occasion to bring to your attention our roundup of how MLatom became better in 2022 and provide a preview of what to expect in …
Roundup of MLatom’s Year 2022. What to Expect in 2023? Read more »
New manual for MLatom@XACS
We are happy to announce that our brand-new manual for MLatom@XACS is now online at http://mlatom.com/manual/. Our new manual is re-designed to make it much easier to find what you need. We also greatly updated it with additional information, useful tips, …
A comparative study of different machine learning methods for dissipative quantum dynamics
Luis E. Herrera Rodríguez, Arif Ullah, Kennet J. Rueda Espinosa, Pavlo O. Dral*, Alexei A. Kananenka*. A comparative study of different machine learning methods for dissipative quantum dynamics. Mach. Learn. Sci. Technol. 2022, 3, 045016. DOI: 10.1088/2632-2153/ac9a9d. Blog post ›