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."

First-principles calculations of hybrid inorganic-organic interfaces: From state-of-the-art to best practice

Oliver T. Hofmann, Egbert Zojer, Lukas Hörmann, Andreas Jeindl, and R. J. Maurer, Phys. Chem. Chem. Phys. 23, 8132-8180 (2021)

"In this review, we discuss how to choose appropriate atomistic representations for the simulation of hybrid inorganic-organic interfaces. We provide tips and tricks on how to efficiently converge the self-consistent field cycle and to obtain accurate geometries. We particularly focus on potentially unexpected pitfalls and the errors they incur. As a summary, we provide a list of best practice rules for interface simulations that should especially serve as a useful starting point for less experienced users and newcomers to the field."

Structure and Stability of Molecular Crystals with Many Body Dispersion Inclusive Density Functional Tight Binding

Majid Mortazavi, Jan Gerit Brandenburg, Reinhard J. Maurer, Alexandre Tkatchenko, J. Phys. Chem. Lett. 9, 399-405 (2018)

"We show the ability of many-body-dispersion-inclusive tight-binding methodology to accurately predict the structure of polymorphic organic molecular crystals."

Global structure search for molecules on surfaces: Efficient sampling with curvilinear coordinates

Konstantin Krautgasser, Chiara Panosetti, Dennis Palagin, Karsten Reuter, Reinhard J. Maurer, J. Chem. Phys. 145, 084117 (2016)

We extend our curvilinear coordinate global optimization method to efficiently sample adsorbate structures on surfaces.

Global materials structure search with chemically-motivated coordinates

C. Panosetti, K. Krautgasser, D. Palagin, K. Reuter, R. J. Maurer, Nano Lett., 15, 8044-8048 (2015)

We define general purpose coordinates which facilitate computational structure search in cluster and material science.