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Publications

No. of Publications: 45

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A symmetry adapted high dimensional neural network representation of electronic friction tensor of adsorbates on metals

A symmetry adapted high dimensional neural network representation of electronic friction tensor of adsorbates on metals

Yaolong Zhang, Reinhard J. Maurer, Bin Jiang, J. Chem. Phys., just accepted (2019)

"In this work, we develop a new symmetry-adapted neural network representation of electronic friction, based on our recently proposed embedded atom neural network (EANN) framework. Unlike previous methods, our new approach can readily include both molecular and surface degrees of freedom, regardless of the type of surface. Tests on the H2+Ag(111) system show that this approach yields an accurate, efficient, and continuous representation of electronic friction, making it possible to perform large scale TDPT-based MDEF simulations to study both adiabatic and nonadiabatic energy dissipation in a unified framework."

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Kristof T. Schütt, Michael Gastgger, Alexandre Tkatchenko, Klaus-Robert Müller, Reinhard J. Maurer, Nature Commun. 10, 5024 (2019)

"Here we present a deep machine learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry."