A Package for Atomistic Simulations with Machine Learning
We are very happy to announce that MLatom joins Xiamen Atomistic Computing Suite (XACS) which allows us to provide much better service to the theoretical and computational chemistry community. We marked the inception of XACS by holding a local workshop …
Max Pinheiro Jr, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti. Choosing the right molecular machine learning potential. Chem. Sci., 2021, 12, 14396–14413. DOI: 10.1039/D1SC03564A. Blog post › | Tutorial ›
Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5. Read more ›
Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310. Read more ›
P. O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004. Read more ›
P. O. Dral, M. Barbatti, W. Thiel, Nonadiabatic Excited-State Dynamics with Machine Learning. J. Phys. Chem. Lett. 2018, 9, 5660–5663. Read more ›
P. O. Dral, A. Owens, S. N. Yurchenko, W. Thiel, Structure-Based Sampling and Self-Correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels. J. Chem. Phys. 2017, 146, 244108. Read more ›
