Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces

J. Westermayr, S. Chaudhuri, A. Jeindl, O. T. Hofmann, R. J. Maurer, Digital Discovery DOI:10.1039/D2DD00016D (2022)

"We present an ML approach that enables fast, yet accurate, structure optimizations by combining two different types of deep neural networks trained on high-level electronic structure data. The first model is a short-ranged interatomic ML potential trained on local energies and forces, while the second is an ML model of effective atomic volumes derived from atoms-in-molecules partitioning. The latter can be used to connect short-range potentials to well-established density-dependent long-range dispersion correction methods. For two systems, specifically gold nanoclusters on diamond (110) surfaces and organic π-conjugated molecules on silver (111) surfaces, we show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures."

Roadmap on Machine Learning in Electronic Structure

Kulik et al., IOP Electronic Structure DOI: 10.1088/2516-1075/ac572f (2022)

"A perspective roadmap that covers the present role and future perspective of machine learning in materials property prediction, the construction of accurate force fields, the solution of the many-body problem, and big data challenges."

Coexistence of carbonyl and ether groups on oxygen-terminated (110)-oriented diamond surfaces

Shayanthan Chaudhuri, Samuel J. Hall, Benedikt P. Klein, Marc Walker, Andrew J. Logsdail, Julie V. Macpherson, Reinhard J. Maurer, Communications Materials 3, 6 (2022)

"Here, we determine the oxygenation state of the (110) surface using a combination of density functional theory calculations and X-ray photoelectron spectroscopy experiments. We report the fabrication of the highest-quality (100)-oriented diamond crystal surface to date. We further propose a mechanism for the formation of the hybrid carbonyl-ether phase and rationalize its high stability. "

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.