材料科学
Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of…
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred,…
Nonvolatile optical manipulation of material properties on demand is a highly sought-after feature in the advancement of future optoelectronic applications. While the discovery of such metastable transition in various materials holds good…
Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account…
Co3Sn2S2 has been reported to be a Weyl semimetal with broken time-reversal symmetry with c axis ferromagnetism (FM) below a Curie temperature of 177 K. Despite the large interest in Co3Sn2S2, the magnetic structure is still under debate…
At extreme strain rates, where fast moving dislocations govern plastic deformation, anharmonic phonon scattering imparts a drag force on the dislocations. In this paper, we present calculations of the dislocation drag coefficients of…
The spin ordering in RuO2 remains a highly debated topic, owing to its elusive nature, with reports ranging from a nonmagnetic ground state to signatures of unconventional magnetic order. Here we provide the first unambiguous, and direct…
Nanometer-scale modulations can spontaneously emerge in complex materials when multiple degrees of freedom interact. Here we demonstrate that ferroelectric Sr$_{1-x}$Ca$_x$TiO$_3$ lies in close proximity to an incipient structurally…
While current technology has enabled their widespread use, further improvements are needed for stationary, portable, and mobile applications, for example by the development of novel cathode materials. Digitalization of battery development,…
InN nanowires were grown on Si<111> and Si<100> substrates by plasma-assisted molecular beam epitaxy using a thin AlN buffer layer at temperatures compatible with the thermal budget limitation imposed by Back-End-Of-Line processing.…
Gold nanoclusters possess multiple competing structural motifs with small energy differences, enabling structural coexistence and interconversion. Using a high-accuracy machine learned potential trained on some 20'000 density functional…
Designing metal-semiconductor junctions is essential for optimizing the performance of modern nanoelectronic devices. A widely used material is TiSi$_2$, which combines low electronic resistivity with good endurance. However, its multitude…
A physically un-clonable function (PUF) is a physical system that cannot be reproduced or predicted and therefore is a good basis to build security and anti-counterfeiting applications. The unclonability of PUFs typically stems from the…
The hydration of magnesium oxide (MgO) to magnesium hydroxide (Mg(OH)$_2$) is a fundamental solid-surface chemical reaction with significant implications for materials science. Yet its molecular-level mechanism from water adsorption to…
The influence of varying the number of bilayers (N) on the anomalous Hall effect (AHE) in sputtered Si/Fe multilayers has been investigated. Both the AHE and magnetisation data reveal the in-plane magnetic anisotropy in the samples. Large…
In this work, 3-atom clusters, Ru3 and Pt3, were deposited onto radio frequency RF-sputter deposited TiO2, treated with Ar+ ion sputtering. Ru3 was deposited by both solution submersion and chemical vapor deposition of Ru3(CO)12, while Pt3…
In this work we explore the performance and behavior of reasoning large language models to autonomously optimize atomic layer deposition (ALD) processes. In the ALD process optimization task, an agent built on top of a reasoning LLM has to…
Magnetoelectric multiferroics, materials with intrinsically coupled electric polarization and magnetic order, promise ultralow-power switching, nonvolatile memory, and energy-efficient signal transduction. Yet practical deployment demands…
We employ state-of-the-art first-principles calculations to investigate the shandite compounds Co3Sn2S2, Co3Sn2SeS, and Co3Sn2Se2, which host Weyl fermions and complex magnetic textures. Their magnetic structures are governed primarily by…
The exchange bias (EB) effect, arising from interfacial coupling between ferromagnetic (FM) and antiferromagnetic (AF) layers, induces a unidirectional magnetic anisotropy and underpins a wide range of spintronic functionalities. Extending…