arXiv subjects

David Lamprecht

Publications and source records attributed to David Lamprecht.

11 recordsLinked to original sources

arXiv2026

Competition between vacancy creation and filling in defect-engineering of hBN

Hexagonal boron nitride (hBN) has recently become the focus of intense research as a material that can host quantum emitters. It is known that such emission is related to point defects, but in order to conclusively correlate specific defects to their spectra, having control over the defect creation mechanism is required. Here, we prepare freestanding, monolayer hBN samples and irradiate them with ultra-low-energy (150 eV) Ar+ ions. The samples are characterized before and after irradiation via scanning transmission electron microscopy to assess the defect density and distribution. Contrary to what analytical potential molecular dynamics simulations have predicted, we predominantly observe boron single vacancies after ion irradiation, followed by double vacancies at half the count. Moreover, we also observe that vacancy filling with Si and C impurity atoms plays a more significant role in the created defects than previously assumed, potentially posing a problem for selective creation of quantum emitters in hBN.

cond-mat.mtrl-sci↗

arXiv2026

SemRepo: A Knowledge Graph for Research Software and Its Scholarly Ecosystem

We present SemRepo, an RDF knowledge graph comprising over 81 million triples describing nearly 200,000 GitHub repositories associated with scientific research. SemRepo captures repository-level metadata, such as contributors, issues, and programming languages, and interlinks this information with external scholarly knowledge graphs. In particular, repository authors are linked to their profiles in SemOpenAlex, repositories are connected to scholarly publications in LPWC, and research artifacts, such as datasets and experiments, are linked via MLSea-KG. This integration enables queries that span publications and their scholarly artifacts, which are typically fragmented across separate platforms. SemRepo supports analyses that are difficult to perform with existing resources in isolation, including provenance reconstruction across repositories and publications, as well as the systematic identification of risks to research reproducibility and software sustainability. By unifying research software with its scholarly context in a single graph, SemRepo provides an important infrastructure for large-scale analysis of software within the broader scientific research ecosystem.

cs.DL↗

arXiv2025

Single photon emitters in hBN: Limitations of atomic resolution imaging and potential sources of error

There is a growing interest in identifying the origin of single-photon emission in hexagonal boron nitride (hBN), with proposed candidates including boron and nitrogen vacancies as well as carbon substitutional dopants. Because photon emission intensity often increases with sample thickness, hBN flakes used in these studies commonly exceed 30 atomic layers. To identify potential emitters at the atomic scale, annular dark-field scanning transmission electron microscopy (ADF-STEM) is frequently employed. However, due to the intrinsic AA' stacking of hBN with vertically alternating boron and nitrogen atoms, this approach is complicated even in few-layer systems. Here, we demonstrate using STEM image simulations and experiments that, even under idealized conditions, the intensity differences between boron- and nitrogen-dominated columns and carbon substitutions become indistinguishable at thicknesses beyond 17 atomic layers (ca. 6 nm). While vacancy-type defects can remain detectable at somewhat larger thicknesses, also their detection becomes unreliable at thicknesses typically used in photonic studies. We further show that common residual aberrations, particularly threefold astigmatism, can lead to artificial contrast differences between columns, which may result in misidentification of atomic defects. We systematically study the effects of non-radially symmetric aberrations on multilayer hBN and demonstrate that even small residual threefold astigmatism can significantly distort the STEM contrast, leading to misleading interpretations.

cond-mat.mtrl-sci↗

arXiv2025

Atomically clean free-standing two-dimensional materials through heating in ultra-high vacuum

Surface contamination not only influences but in some cases even dominates the measured properties of two-dimensional materials. Although different cleaning methods are often used for contamination removal, commonly used spectroscopic cleanliness assessment methods can leave the level of achieved cleanliness ambiguous. Despite two decades of research on 2D materials, the true cleanliness of the used samples is often left open to interpretation. In this work, freestanding monolayer graphene and hexagonal boron nitride are annealed at different temperatures in a custom-built ultra-high vacuum heating chamber, connected to a scanning transmission electron microscope via a vacuum transfer line, enabling atomically resolved cleanliness characterization as a function of annealing temperature, while eliminating the introduction of airborne contamination during sample transport. While annealing at 200 {\deg}C already reduces contamination significantly, it is not until 400 {\deg}C or higher, where over 90% of the free-standing monolayer areas are atomically clean. At this point, further contamination removal is mainly limited by defects in the material and metal contamination introduced during the sample transfer or growth. The achieved large, atomically clean areas can then be used for further nanoscale engineering steps or device processing, facilitating interaction with the material rather than contamination.

cond-mat.mtrl-sci↗

arXiv2025

Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems

Graph Neural Networks (GNNs) have substantially advanced the field of recommender systems. However, despite the creation of more than a thousand knowledge graphs (KGs) under the W3C standard RDF, their rich semantic information has not yet been fully leveraged in GNN-based recommender systems. To address this gap, we propose a comprehensive integration of RDF KGs with GNNs that utilizes both the topological information from RDF object properties and the content information from RDF datatype properties. Our main focus is an in-depth evaluation of various GNNs, analyzing how different semantic feature initializations and types of graph structure heterogeneity influence their performance in recommendation tasks. Through experiments across multiple recommendation scenarios involving multi-million-node RDF graphs, we demonstrate that harnessing the semantic richness of RDF KGs significantly improves recommender systems and lays the groundwork for GNN-based recommender systems for the Linked Open Data cloud. The code and data are available on our GitHub repository: https://github.com/davidlamprecht/rdf-gnn-recommendation

cs.IR↗

arXiv2025

Uncovering the atomic structure of substitutional platinum dopants in MoS$_2$ with single-sideband ptychography

We substitute individual Pt atoms into monolayer MoS$_2$ and study the resulting atomic structures with single-sideband (SSB) ptychography supported by ab initio simulations. We demonstrate that while high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) imaging provides excellent Z-contrast, distinguishing some defect types such as single and double sulfur vacancies remains challenging due to their low relative contrast difference. However, SSB with its nearly linear Z-contrast and high phase sensitivity enables reliable identification of these defect configurations as well as various Pt dopant structures at significantly lower electron doses. Our findings uncover the precise atomic placement and highlight the potential of SSB ptychography for detailed structural analysis of dopant-modified 2D materials while minimizing beam-induced damage, offering new pathways for understanding and engineering atomic-scale features in 2D systems.

cond-mat.mtrl-sci↗

arXiv2025

Hexatic Phase in Covalent Two-Dimensional Silver Iodide

According to the Kosterlitz-Thouless-Halperin-Nelson-Young (KTHNY) theory, the transition from a solid to liquid in two dimensions proceeds through an orientationally ordered liquid-like hexatic phase. However, alternative mixed melting scenarios, in which melting proceeds through the hexatic phase with both continuous and discontinuous transitions, have also been observed in some two-dimensional systems. In this study, we imaged silver iodide embedded in multilayer graphene using time- and temperature-resolved in situ atomic-resolution scanning transmission electron microscopy and nanobeam electron diffraction. We observed the hexatic phase and provide evidence supporting a mixed melting scenario.

cond-mat.mtrl-sci↗

arXiv2024

Electron-beam-induced adatom-vacancy-complexes in mono- and bilayer phosphorene

Phosphorene, a puckered two-dimensional allotrope of phosphorus, has sparked considerable interest in recent years due to its potential especially for optoelectronic applications with its layer-number-dependant direct band gap and strongly bound excitons. However, detailed experimental characterization of its intrinsic defects as well as its defect creation characteristics under electron irradiation are scarce. Here, we report on the creation and stability of a variety of defect configurations under 60 kV electron irradiation in mono- and bilayer phosphorene including the first experimental reports of stable adatom-vacancy-complexes. Displacement cross section measurements in bilayer phosphorene yield a value of 7.7 +- 1.4 barn with an estimated lifetime of adatom-vacancy-complexes of 19.9 +- 0.7 s, while some are stable for up to 68 s under continuous electron irradiation. Surprisingly, ab initio-based simulations indicate that the complexes should readily recombine, even in structures strained by up to 3 %. The presented results will help to improve the understanding of the wide variety of defects in phosphorene, their creation, and their stability, which may enable new pathways for defect engineered phosphorene devices.

cond-mat.mes-hall↗

arXiv2024

AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning

In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features -- i.e., features based on RDF datatype properties -- and topology-based features -- i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.

cs.LG↗

arXiv2023

Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph

In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. Compared to its non-RDF-based counterpart Papers With Code, LPWC not only translates the latest advancements in machine learning into RDF format, but also enables novel ways for scientific impact quantification and scholarly key content recommendation. LPWC is openly accessible at https://linkedpaperswithcode.com and is licensed under CC-BY-SA 4.0. As a knowledge graph in the Linked Open Data cloud, we offer LPWC in multiple formats, from RDF dump files to a SPARQL endpoint for direct web queries, as well as a data source with resolvable URIs and links to the data sources SemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graph embeddings, enabling LPWC to be readily applied in machine learning applications.

cs.DL↗

arXiv2023

SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing.

cs.DL↗