# Publications

No. of Publications: 49

See also Google Scholar

## 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, **arXiv**: 2005.06979

##### "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. "

## 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."

## Hot-electron effects during reactive scattering of H2 from Ag(111): assessing the sensitivity to initial conditions, coupling magnitude, and electronic temperature

## Hot-electron effects during reactive scattering of H2 from Ag(111): assessing the sensitivity to initial conditions, coupling magnitude, and electronic temperature

Yaolong Zhang, Reinhard J. Maurer, Hua Guo and Bin Jiang, **Faraday Discuss.** 214, 105-121 (2019)

##### "Using molecular dynamics simulations with electronic friction, we systematically study the effect of hot electrons on measurable state-to-state scattering probabilities of molecular hydrogen from a (111) surface of silver. We find that dynamic scattering results and the ensuing energy loss are highly sensitive to the magnitude of electronic friction."

## Hot-electron effects during reactive scattering of H2 from Ag(111): the interplay between mode-specific electronic friction and the potential energy landscape

## Hot-electron effects during reactive scattering of H2 from Ag(111): the interplay between mode-specific electronic friction and the potential energy landscape

Y.Zhang, R.J.Maurer, H.Guo, B.Jiang, **Chem. Sci. **10**, **1089-1097 (2019)

#### "The breakdown of the Born-Oppenheimer approximation gives rise to nonadiabatic effects in gas-surface reactions at metal surfaces. However, for a given reaction, it remains unclear which factors quantitatively determine whether these effects measurably contribute to surface reactivity in catalysis and photo/electrochemistry. Here, we systematically investigate hot electron effects during H2 scattering from Ag(111) using electronic friction theory."

## Molecular simulation of surface reorganization and wetting in crystalline cellulose I and II

### Molecular simulation of surface reorganization and wetting in crystalline cellulose I and II

R. J. Maurer, A. F.. Sax, V. Ribitsch, **Cellulose** 20, 25-42 (2013)

#### Reconstruction and wetting of cellulose strongly modifies the hydrogen bonding network

## Molecular Dynamics of cellulose crystal surfaces with ChemShell

### Molecular Dynamics of cellulose crystal surfaces with ChemShell

R. J. Maurer, A. F. Sax, **Procedia Comput. Sci.** 1, 1149-1154 (2010)