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
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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