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No. of Publications: 50

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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."

Role of Tensorial Electronic Friction in Energy Transfer at Metal Surfaces

Role of Tensorial Electronic Friction in Energy Transfer at Metal Surfaces

Mikhail Askerka, Reinhard J. Maurer, Victor S. Batista, John C. Tully, Phys. Rev. Lett. 116, 217601 (2016)
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We use time-dependent perturbation theory to calculate the full electronic friction tensor and study its relevance in the simulation of dynamics at surfaces.


Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111)

Thermal and electronic fluctuations of flexible adsorbed molecules: Azobenzene on Ag(111)

Reinhard J. Maurer, Wei Liu, Igor Poltavsky, Thomas Stecher, Harald Oberhofer, Karsten Reuter, Alexandre Tkatchenko, Phys. Rev. Lett., 116, 146101 (2016)

Ab Initio molecular dynamics simulation of the free energy of desorption reveals collective electronic and thermal fluctuations that define the finite-temperature energetics of complex adsorbates.