Issue 4, 2022

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

Abstract

The computational prediction of the structure and stability of hybrid organic–inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an important role in their rational design. However, the rich diversity of molecular configurations and the important role of long-range interactions in such systems make it difficult to use machine learning (ML) potentials to facilitate structure exploration that otherwise requires computationally expensive electronic structure calculations. 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 train models on sparse structure relaxation data from density functional theory and show the ability of the models to deliver highly efficient structure optimizations and semi-quantitative energy predictions of adsorption structures.

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

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Article information

Article type
Paper
Submitted
25 Febr. 2022
Accepted
03 Jūn. 2022
First published
06 Jūn. 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2022,1, 463-475

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 and R. J. Maurer, Digital Discovery, 2022, 1, 463 DOI: 10.1039/D2DD00016D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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