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

SUBMITTER: Westermayr J 

PROVIDER: S-EPMC9358753 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature