Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Propertiesphysics.chem-ph
Artificial neural networks reduce computational costs for predicting excitation energy transfer properties in light-harvesting systems.
problem Computational limitations in predicting excitation energy transfer properties in light-harvesting systems.
method Use of artificial neural networks to bypass the computational limitations of established techniques.
result Artificial neural networks predict transfer times and transfer efficiencies with similar or higher accuracy than frequently used approximate methods.