Physically inspired deep learning of molecular excitations and photoemission spectra

Julia Westermayr, Reinhard J. Maurer, Chemical Science 12, 10755-10764 (2021)

"In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations."

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."

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. "

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."