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Author: van den Brink, Jeroen × Start date: 2021 × DDC: 600 ×

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    Intermixing-Driven Surface and Bulk Ferromagnetism in the Quantum Anomalous Hall Candidate MnBi6Te10
    (Weinheim : Wiley-VCH, 2023) Tcakaev, Abdul‐Vakhab; Rubrecht, Bastian; Facio, Jorge I.; Zabolotnyy, Volodymyr B.; Corredor, Laura T.; Folkers, Laura C.; Kochetkova, Ekaterina; Peixoto, Thiago R. F.; Kagerer, Philipp; Heinze, Simon; Bentmann, Hendrik; Green, Robert J.; Gargiani, Pierluigi; Valvidares, Manuel; Weschke, Eugen; Haverkort, Maurits W.; Reinert, Friedrich; van den Brink, Jeroen; Büchner, Bernd; Wolter, Anja U. B.; Isaeva, Anna; Hinkov, Vladimir
    The recent realizations of the quantum anomalous Hall effect (QAHE) in MnBi2Te4 and MnBi4Te7 benchmark the (MnBi2Te4)(Bi2Te3)n family as a promising hotbed for further QAHE improvements. The family owes its potential to its ferromagnetically (FM) ordered MnBi2Te4 septuple layers (SLs). However, the QAHE realization is complicated in MnBi2Te4 and MnBi4Te7 due to the substantial antiferromagnetic (AFM) coupling between the SLs. An FM state, advantageous for the QAHE, can be stabilized by interlacing the SLs with an increasing number n of Bi2Te3 quintuple layers (QLs). However, the mechanisms driving the FM state and the number of necessary QLs are not understood, and the surface magnetism remains obscure. Here, robust FM properties in MnBi6Te10 (n = 2) with Tc ≈ 12 K are demonstrated and their origin is established in the Mn/Bi intermixing phenomenon by a combined experimental and theoretical study. The measurements reveal a magnetically intact surface with a large magnetic moment, and with FM properties similar to the bulk. This investigation thus consolidates the MnBi6Te10 system as perspective for the QAHE at elevated temperatures.
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    Machine learning for additive manufacturing: Predicting materials characteristics and their uncertainty
    (Amsterdam [u.a.] : Elsevier Science, 2023) Chernyavsky, Dmitry; Kononenko, Denys Y.; Han, Jun Hee; Kim, Hwi Jun; van den Brink, Jeroen; Kosiba, Konrad
    Additive manufacturing (AM) is known for versatile fabrication of complex parts, while also allowing the synthesis of materials with desired microstructures and resulting properties. These benefits come at a cost: process control to manufacture parts within given specifications is very challenging due to the relevance of a large number of processing parameters. Efficient predictive machine learning (ML) models trained on small datasets, can minimize this cost. They also allow to assess the quality of the dataset inclusive of uncertainty. This is important in order for additively manufactured parts to meet property specifications not only on average, but also within a given variance or uncertainty. Here, we demonstrate this strategy by developing a heteroscedastic Gaussian process (HGP) model, from a dataset based on laser powder bed fusion of a glass-forming alloy at varying processing parameters. Using amorphicity as the microstructural descriptor, we train the model on our Zr52.5Cu17.9Ni14.6Al10Ti5 (at.%) alloy dataset. The HGP model not only accurately predicts the mean value of amorphicity, but also provides the respective uncertainty. The quantification of the aleatoric and epistemic uncertainty contributions allows to assess intrinsic inaccuracies of the dataset, as well as identify underlying physical phenomena. This HGP model approach enables to systematically improve ML-driven AM processes.