MLatom
A Package for Atomistic Simulations with Machine Learning
MLatom
A Package for Atomistic Simulations with Machine Learning
Developed by Dr. Pavlo O. Dral since September 10th, 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
Pavlo O. Dral, Mario Barbatti, Walter Thiel, Nonadiabatic Excited-State Dynamics with Machine Learning. J. Phys. Chem. Lett. 2018, 9, 5660–5663.
Read more ›
Self-Correcting Machine Learning and Structure-Based Sampling
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 ›
Read more ›
Correcting Differences with Machine Learning
R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld, Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. J. Chem. Theory Comput. 2015, 11, 2087–2096.
Read more ›
Read more ›