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Chemistry Computational Surface Science Group

Publications

No. of Publications: 70

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Perspective on integrating machine learning into computational chemistry and materials science

Perspective on integrating machine learning into computational chemistry and materials science

Julia Westermayr, Michael Gastegger, Kristof T. Schütt, Reinhard J. Maurer, J. Chem. Phys. 154, 230903 (2021)

"As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML."
Mon 21 Jun 2021, 13:03 | Tags: Ab Initio Molecular Dynamics, Excited States, Materials structure search, machine learning, SchNet, Density Functional Theory, SchNOrb, DFTB+, computational materials science

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. "
Tue 19 May 2020, 13:41 | Tags: molecular dynamics, machine learning, SchNet, Density Functional Theory, SchNOrb

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