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Found 3 papers, 2 papers with code
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.
Automatic differentiation in quantum chemistry with an application to fully variational Hartree-Fock
Automatic Differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions.
Chemical Physics
PHOENICS: A universal deep Bayesian optimizer
In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.
