MLatom
A Package for Atomistic Simulations with Machine Learning
Developed by Pavlo Dral since 2013 for practical and efficient application of machine learning in computational chemistry. MLatom is optimized for parallel computing.
Featured Publications
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way
Pavlo O. Dral, Quantum Chemistry Assisted by Machine Learning. In Advances in Quantum Chemistry: Chemical Physics and Quantum Chemistry, Volume 81, 1st ed.; Kenneth Ruud, Erkki J. Brändas, Eds. Academic Press: 2020; Vol. 81.
Machine Learning Accelerates Excited-State Dynamics
P. O. Dral, M. Barbatti, W. Thiel, Nonadiabatic Excited-State Dynamics with Machine Learning. J. Phys. Chem. Lett. 2018, 9, 5660–5663.
Machine Learning of Semiempirical Parameters
P. O. Dral, O. A. von Lilienfeld, W. Thiel, Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations. J. Chem. Theory Comput. 2015, 11, 2120–2125.
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