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No. of Publications: 50

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Quantifying the breakdown of electronic friction theory for molecular scattering at metal surfaces

Quantifying the breakdown of electronic friction theory for molecular scattering at metal surfaces

C. L. Box, Y. Zhang, R. Yin, B. Jiang, R. J. Maurer, arXiv: 2010.05325 (2020)

"Vibrational state-to-state scattering of NO on Au(111) provides a testing ground for developing various nonadiabatic theories, including electronic friction theory. This system is often cited as the prime example for the breakdown of electronic friction theory, a very efficient model accounting for dissipative forces on metal-adsorbed molecules due to the creation of electron-hole-pair excitations in the metal. Here we present a comprehensive quantitative analysis of the performance of molecular dynamics with electronic friction (MDEF) in describing vibrational state-to-state scattering of NO on Au(111) and connect directly to fundamental approximations. Our analysis provides a firm baseline for the future development of nonadiabatic dynamics methods to tackle problems in surface chemistry and photocatalysis."

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

M. Gastegger, A. McSloy, M. Luya, K. T. Schütt, R. J. Maurer, J. Chem. Phys 153, 044123 (2020)

"We present an adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep convolutional neural network model [Nature Commun. 10, 5024 (2019)] for electronic wave functions in an optimised quasi-atomic minimal basis representation. For five organic molecules ranging from 5 to 13 heavy atoms, the model accurately predicts molecular orbital energies and wavefunctions and provides access to derived properties for chemical bonding analysis. Particularly for larger molecules, the model outperforms the original atomic-orbital-based SchNOrb method in terms of accuracy and scaling. "

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."