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Nataliia Manko

Publications and source records attributed to Nataliia Manko.

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Symmetry-Guided Computational Screening of Two-Dimensional Altermagnets with ab initio Hubbard Corrections

Altermagnets combine compensated antiferromagnetic order with momentum-dependent spin splitting, offering a promising platform for spintronic applications without macroscopic magnetization or stray magnetic fields. Although a wide range of three-dimensional (3D) materials have been identified as altermagnets, two-dimensional (2D) altermagnets remain comparatively limited. In this work, we perform a high-throughput computational search for altermagnetism across 2710 materials in the Materials Cloud 2D Crystals (MC2D) database. Our approach combines symmetry-based screening with first-principles density functional theory calculations, including self-consistent Hubbard-$U$ corrections, to reliably capture magnetic ground states. Through a systematic exploration of magnetic configurations and their energetic stability, we identify 42 materials exhibiting altermagnetic ground states for at least one value of $U$, of which 24 remain robust upon determination of the Hubbard-$U$ parameters from first principles--including 4 materials previously reported in the literature and 20 newly predicted candidates. These comprise promising monolayers such as metallic Fe$_2$Si$_2$SbO$_9$, and insulating CoBrO, with spin splittings about 294 meV and 330 meV, respectively. Our results significantly expand the pool of potential 2D altermagnet candidates with favorable exfoliation energetics and provide valuable guidance for experimental efforts. In addition, this work establishes a high-throughput computational framework for reproducible discovery and characterization of altermagnetic materials.

cond-mat.mtrl-sci

AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

Crafting neural-network interatomic potentials (NNIPs) remains a complex task, demanding specialized expertise in both machine learning and electronic-structure calculations. Here, we introduce AiiDA-TrainsPot, an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs by orchestrating density-functional-theory calculations, data augmentation strategies, and classical molecular dynamics. Our active-learning strategy leverages on-the-fly calibration of committee disagreement against ab initio reference errors to ensure reliable uncertainty estimates. We use electronic-structure descriptors and dimensionality reduction to analyze the efficiency of this calibrated criterion, and show that it minimizes both false positives and false negatives when deciding what to compute from first principles. AiiDA-TrainsPot has a modular design that supports multiple NNIP backends, enabling both the training of NNIPs from scratch and the fine-tuning of foundation models. We demonstrate its capabilities through automated training campaigns targeting pristine and defective carbon allotropes, including amorphous carbon, as well as structural phase transitions in monolayer $\mathrm{W_xMo_{1-x}Te_2}$ alloys.

physics.comp-ph