A Package for Atomistic Simulations with Machine Learning
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 ›
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 ›
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 ›
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 »
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, …
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 ›
Mario Barbatti*, Mattia Bondanza, Rachel Crespo-Otero, Baptiste Demoulin, Pavlo O. Dral, Giovanni Granucci, Fábris Kossoski, Hans Lischka, Benedetta Mennucci, Saikat Mukherjee, Marek Pederzoli, Maurizio Persico, Max Pinheiro Jr, Jiri Pittner, Felix Plasser, Eduarda Sangiogo Gil, Lijljana Stojanovic. The Newton-X platform: new software developments for surface hopping and nuclear ensembles. J. Chem. Theory Comput. 2022, ASAP. DOI: 10.1021/acs.jctc.2c00804. …
The Newton-X platform for surface hopping and nuclear ensembles Read more »
Quantum Chemistry in the Age of Machine Learning, Pavlo O. Dral, Ed. Elsevier: 2022. Paperback ISBN: 9780323900492. Companion website to the book with complementary materials, data, codes, etc. Blog post ›
MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school. This school aimed at offering state-of-the-art training in quantum molecular dynamics (QMD), machine learning (ML), and quantum computing (QC) to early-stage …
Tutorial on ML in CECAM school MLQCDyn featuring MLatom@XACS Read more »
Armando de Rezende, Mahdi Malmali, Pavlo O. Dral, Hans Lischka, Daniel Tunega, Adelia A. J. Aquino, Machine Learning for Designing Mixed Metal Halides for Efficient Ammonia Separation and Storage. J. Phys. Chem. C, 2022, 126, 12184–12196. DOI: 10.1021/acs.jpcc.2c02586. Blog post ›