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Magnetic levitation by rotation is a simply yet astonishing phenomenon where a permanent magnet can be levitated by placing it in the vicinity of another permanent magnet that rotates sufficiently fast. The few previous works on this novel type of magnetic levitation all required magnets rotating on the order of 200 Hz. Here we investigate the influence of applying an on-axis, static magnetic field and show that this can lower the needed rotation frequency to below 50 Hz. We explain this by a detailed analysis of the force producing levitation, which is a superposition of a repelling force caused by the off-axis (rotating) magnetic field and an attractive force due to the on-axis field. We study this force and resulting levitation experimentally, analytically and numerically for three different rotor magnet configurations, showing that the levitation distance and frequency range can be accurately predicted from both the numerical and analytical models., Comment: 14 pages, 8 figures (including supplementary material)

We analytically solve Poisson's equation for the magnetic scalar potential generated by a magnetized prism and determine a closed-form solution for the magnetic scalar potential given only in terms of arctan and natural logarithmic functions. We furthermore show that this can be written as a demagnetization vector, containing all the geometric information of the prism, multiplied with the magnetization, analogous to the way demagnetization tensors are written. We validate the derived analytical expression for the magnetic scalar potential by comparing with a finite element simulation and show that these agree perfectly. We finally extend the concept of the demagnetization tensor, which contains the geometric information for the source generating the potential, to gravitational objects., Comment: 6 pages, 2 figures. arXiv admin note: text overlap with arXiv:2501.11044

Poisson's equation for the magnetic scalar potential generated by a magnetized cylindrical slice and a full cylinder is solved analytically and a closed-form solution for the magnetic scalar potential is determined. This is given in terms of elliptic integrals of the first, second and third kind. We show that the magnetic scalar potential can be written as a demagnetization vector, containing all the geometric information of generating cylinder, multiplied with the magnetization. We validate the derived analytical expression for the magnetic scalar potential by comparing with a finite element simulation and show that these agree perfectly for both the cylindrical slice and the full cylinder., Comment: 10 pages, 5 figures

Magnetometers based on the extraordinary magnetoresistance (EMR) effect are promising for applications which demand high sensitivity combined with room temperature operation but their application for magnetic field sensing requires further optimization. A key challenge is to obtain Ohmic metal/semiconductor contacts with low contact resistances in EMR devices comprising semiconductors with low carrier densities and high electron mobilities, yet, this topic remains scarcely investigated experimentally. By annealing high-mobility InSb in argon with systematically increasing temperatures, we experimentally demonstrate how the contact resistance to InSb films can be improved by two orders of magnitude by annealing to the micro-Ohm cm2 range without degrading the high mobility. We further show that lowering the contact resistance monotonously increases the room temperature magnetoresistance at 2 T from 700% to 65,000%. Lastly, we explore the origin of intrinsic magnetoresistance in high-mobility InSb thin films and suggest that it can best be explained by multiple band conduction., Comment: 13 pages + 2 pages of supplemental information. 5 Figues + 3 supplemental figures

Universal potentials open the door for DFT level calculations at a fraction of their cost. We find that for application to systems outside the scope of its training data, CHGNet\cite{deng2023chgnet} has the potential to succeed out of the box, but can also fail significantly in predicting the ground state configuration. We demonstrate that via fine-tuning or a $\Delta$-learning approach it is possible to augment the overall performance of universal potentials for specific cluster and surface systems. We utilize this to investigate and explain experimentally observed defects in the Ag(111)-O surface reconstruction and explain the mechanics behind its formation.

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of 2-dimensional pyroxene, 3-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles., Comment: 12 pages, 10 figures

Reliable uncertainty measures are required when using data based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian Process Regression type MLIP a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIPs. The uncertainty measure is coined \textit{"label noise"} ensemble uncertainty as it emerges from adding noise to the energy labels in the training data. We find that this method of calculating an ensemble uncertainty is as well calibrated as the one obtained from the closed-form expression for the posterior variance when the sparse GPR is treated as a projected process. Comparing the two methods, our proposed ensemble uncertainty is, however, faster to evaluate than the closed-form expression. Finally, we demonstrate that the proposed uncertainty measure acts better to support a Bayesian search for optimal structure of Au$_{20}$ clusters.

We present a generative model that amortises computation for the field around e.g. gravitational or magnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources and field evaluation points, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit representation of the field around a source collection, our model instead performs as $\mathcal{O}(M + N)$, achieves accuracy of $\sim\!4\%-6\%$, and allows evaluation at arbitrary locations for arbitrary numbers of sources, greatly increasing the speed of e.g. physics simulations. We also examine a model relating to the physical properties of the output field and develop two-dimensional examples to demonstrate its application. The code for these models and experiments is available at https://github.com/cmt-dtu-energy/hypermagnetics., Comment: 13 pages, 4 figures

Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection. Our method leverages active learning techniques to query diverse and informative data samples from an unlabeled pool, enhancing the model's ability to detect underrepresented or novel objects. Specifically, we introduce the Vision-Language Embedding Diversity Querying (VisLED-Querying) algorithm, which operates in both open-world exploring and closed-world mining settings. In open-world exploring, VisLED-Querying selects data points most novel relative to existing data, while in closed-world mining, it mines novel instances of known classes. We evaluate our approach on the nuScenes dataset and demonstrate its efficiency compared to random sampling and entropy-querying methods. Our results show that VisLED-Querying consistently outperforms random sampling and offers competitive performance compared to entropy-querying despite the latter's model-optimality, highlighting the potential of VisLED for improving object detection in autonomous driving scenarios. We make our code publicly available at https://github.com/Bjork-crypto/VisLED-Querying

Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent results on RAW input compared to RGB and present a new public dataset consisting of RAW images and the corresponding converted RGB images. Classifying images directly from RAW is attractive, as it allows for skipping the conversion to RGB, lowering computation time significantly. Two CNN classifiers are used to classify the images in both formats, confirming that classification performance can indeed be preserved. We furthermore show that the total computation time from RAW image data to classification results for RAW images can be up to 8.46 times faster than RGB. These results contribute to the evidence found in related works, that using RAW images as direct input to computer vision algorithms looks very promising., Comment: https://www.kaggle.com/datasets/mathiasviborg/raw-instinct

Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a type of structure generation, which locally optimizes structures in complementary energy (CE) landscapes. These landscapes are formulated temporarily during the searches as machine learned potentials (MLPs) using local atomistic environments sampled from collected data. The CE landscapes are deliberately incomplete MLPs that rather than mimicking every aspect of the true PES are sought to become much smoother, having only few local minima. This means that local optimization in the CE landscapes may facilitate identification of new funnels in the true PES. We discuss how to construct the CE landscapes and we test their influence on global optimization of a reduced rutile SnO2(110)-(4x1) surface, and an olivine (Mg2SiO4)4 cluster for which we report a new global minimum energy structure., Comment: 13 pages, 9 figures

We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion alongside a force-field for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser-network is proposed to scale generation far beyond structure sizes represented in the training data. We showcase the generative model by investigating multiple surface systems and propose an atomistic structure model for a previously unknown silver-oxide domain-boundary of unprecedented size.

The curation of large-scale datasets is still costly and requires much time and resources. Data is often manually labeled, and the challenge of creating high-quality datasets remains. In this work, we fill the research gap using active learning for multi-modal 3D object detection. We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training. We explore various continuous training methods and integrate the most efficient method regarding computational demand and detection performance. Furthermore, we perform extensive experiments and ablation studies with BEVFusion and PV-RCNN on the nuScenes and TUM Traffic Intersection dataset. We show that we can achieve almost the same performance with PV-RCNN and the entropy-based query strategy when using only half of the training data (77.25 mAP compared to 83.50 mAP) of the TUM Traffic Intersection dataset. BEVFusion achieved an mAP of 64.31 when using half of the training data and 75.0 mAP when using the complete nuScenes dataset. We integrate our active learning framework into the proAnno labeling tool to enable AI-assisted data selection and labeling and minimize the labeling costs. Finally, we provide code, weights, and visualization results on our website: https://active3d-framework.github.io/active3d-framework., Comment: 2024 Proceedings of the IEEE Intelligent Vehicles Symposium 2024 (IV'24)

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments.

The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here we apply an accurate and efficient protocol to collect training data for constructing a neural network based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics (MD) simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium (ISM) and in circumstellar environments., Comment: 11 pages, 8 figures, accpected by J. Chem. Phys

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability, including electrode broadcasting, multi-layer RF routing, and magneto-optical trap (MOT) loading, while maintaining, and in some cases exceeding, the gate fidelities of previous QCCD systems. The system is initially operated with 32 qubits, but future upgrades will allow for more. We benchmark the performance of primitive operations, including an average state preparation and measurement error of 1.6(1)$\times 10^{-3}$, an average single-qubit gate infidelity of $2.5(3)\times 10^{-5}$, and an average two-qubit gate infidelity of $1.84(5)\times 10^{-3}$. The system-level performance of the quantum processor is assessed with mirror benchmarking, linear cross-entropy benchmarking, a quantum volume measurement of $\mathrm{QV}=2^{16}$, and the creation of 32-qubit entanglement in a GHZ state. We also tested application benchmarks including Hamiltonian simulation, QAOA, error correction on a repetition code, and dynamics simulations using qubit reuse. We also discuss future upgrades to the new system aimed at adding more qubits and capabilities., Comment: 24 pages, 24 figures. Made some minor edits and added several more authors

A permanent magnet can be levitated simply by placing it in the vicinity of another permanent magnet that rotates in the order of 200 Hz. This surprising effect can be easily reproduced in the lab with off-the-shelf components. Here we investigate this novel type of magnetic levitation experimentally and clarify the underlying physics. Using a 19 mm diameter spherical NdFeB magnet as rotor magnet, we capture the detailed motion of levitating, spherical NdFeB magnets, denoted floater magnets. We find that as levitation occurs, the floater magnet frequency-locks with the rotor magnet, and, noticeably, that the magnetization of the floater is oriented close to the axis of rotation and towards the like pole of the rotor magnet. This is in contrast to what might be expected by the laws of magnetostatics as the floater is observed to align its magnetization essentially perpendicular to the magnetic field of the rotor. Moreover, we find that the size of the floater has a clear influence on the levitation: the smaller the floater, the higher the rotor speed necessary to achieve levitation, and the further away the levitation point shifts. We verify that magnetostatic interactions between the rotating magnets are responsible for creating the equilibrium position of the floater. Hence, this type of magnetic levitation does not rely on gravity as a balancing force to achieve an equilibrium position. Based on theoretical arguments and a numerical model, we show that a constant, vertical field and eddy-current enhanced damping is sufficient to produce levitation from rest. This enables a gyroscopically stabilised counter-intuitive steady-state moment orientation, and the resulting magnetostatically stable, mid-air equilibrium point. The numerical model display the same trends with respect to rotation speed and the floater magnet size as seen in the experiments., Comment: 15 pages, 6 figures, 10 videos + 8 pages supplementary material. Videos available at https://youtube.com/playlist?list=PLOfbFSFa_WoK4PgYQXhuNucS_WcIxXDET

Extraordinary magnetoresistance (EMR) is a geometric magnetoresistance effect occurring in hybrid devices consisting of a high-mobility material joined by a metal. The change in resistance can exceed 107% at room temperature when a magnetic field of 5 T is applied. Magnetic field sensors based on EMR hold the potential formeasuring weak magnetic fields with an unprecedented sensitivity, yet, to date this potential is largely unmet. In this work, we provide an extensive review of the current state-of-the-art in EMR sensors with a focus on the hybrid device geometries, the constituent material properties and applications of EMR. We present a direct comparison of the best devices in literature across magnetoresistance, sensitivity and noise equivalent field for different materials and geometric designs. The compilation of studies collected in this review illustrates the extremely rich possibilities for tuning the magnetoresistive behavior varying the device geometry and material properties. In addition, we aim to improve the understanding of the EMR effect and its interplay with geometry and material properties. Finally, we discuss recent trends in the field and future perspectives for EMR.

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases a reliable structural motif is needed as starting configuration for structure refinements. Here, we present an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model that combines density functional theory (DFT) calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape. Due to the nature of this landscape, even metastable configurations can be determined.

We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches., Comment: 12 pages, 11 figures

Fragmentation is an important decay mechanism for polycyclic aromatic hydrocarbons (PAHs) under harsh interstellar conditions and represents a possible formation pathway for small molecules such as H2, C2H2, C2H4. Our aim is to investigate the dissociation mechanism of superhydrogenated PAHs that undergo energetic processing and the formation pathway of small hydrocarbons. We obtain, experimentally, the mass distribution of protonated tetrahydropyrene (C16H15 , py+5H+) and protonated hexahydropyrene (C16H17+, py+7H+) upon collision induced dissociation (CID). The IR spectra of their main fragments are recorded by infrared multiple-photon dissociation (IRMPD). Extended tight-binding (GFN2-xTB) based molecular dynamics simulations are performed in order to provide the missing structure information in experiment and identify fragmentation pathways. The pathways for fragmentation are further investigated at a hybrid-density functional theory (DFT) and dispersion corrected level. A strong signal for loss of 28 mass units of py+7H+ is observed both in the CID experiment and the MD simulation, while py+5H+ shows negligible signal for the product corresponding to a mass loss of 28. The 28 mass loss from py+7H+ is assigned to the loss of ethylene (C2H4) and a good fit between the calculated and experimental IR spectrum of the resulting fragment species is obtained. Further DFT calculations show favorable kinetic pathways for loss of C2H4 from hydrogenated PAH configurations involving three consecutive CH2 molecular entities. This joint experimental and theoretical investigation proposes a chemical pathway of ethylene formation from fragmentation of superhydrogenated PAHs. This pathway is sensitive to hydrogenated edges (e.g. the degree of hydrogenation and the hydrogenated positions). The inclusion of this pathway in astrochemical models may improve the estimated abundance of ethylene., Comment: 11 pages, 6 figures

Dimerization of polycyclic aromatic hydrocarbons (PAHs) is an important, yet poorly understood, step in the on-surface synthesis of graphene (nanoribbon), soot formation, and growth of carbonaceous dust grains in the interstellar medium (ISM). The on-surface synthesis of graphene and the growth of carbonaceous dust grains in the ISM require the chemical dimerization in which chemical bonds are formed between PAH monomers. An accurate and cheap method of exploring structure rearrangements is needed to reveal the mechanism of chemical dimerization on surfaces. This work has investigated the chemical dimerization of two dehydrogenated PAHs (coronene and pentacene) on graphene via an evolutionary algorithm augmented by machine learning surrogate potentials and a set of customized structure operators. Different dimer structures on surfaces have been successfully located by our structure search methods. Their binding energies are within the experimental errors of temperature programmed desorption measurements. The mechanism of coronene dimer formation on graphene is further studied and discussed., Comment: 8 pages, 6 figures

Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and position of all involved atoms. Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, and bulk phases of amorphous and solid materials represents a difficult high dimensional global optimization problem. The rise of machine learning techniques in materials science has, however, led to many compelling developments that may speed up such structure searches. The complexity of the new methods have established the necessity for an efficient way of experimenting with and assembling them into global optimization algorithms. In this paper we introduce the Atomistic Global Optimization X (AGOX) framework and code, as a customizable approach to building efficient global optimization algorithms. A modular way of expressing global optimization algorithms is described and modern programming practices are used to enable that modularity in the freely available AGOX python package. Two examples of global optimization problems are analyzed: One that is computationally inexpensive which is used to showcase that AGOX enables the expression of multiple global optimization algorithms. As the other example, AGOX is used for solving a complex atomistic optimization problem for a metal-nitride nano-cluster embedded in a graphene sheet as described at the density functional theory (DFT) level., Comment: 18 pages, 19 figures

Plenty of scientific and real-world applications are built on magnetic fields and their characteristics. To retrieve the valuable magnetic field information in high resolution, extensive field measurements are required, which are either time-consuming to conduct or even not feasible due to physical constraints. To alleviate this problem, we predict magnetic field values at a random point in space from a few point measurements by using a generative adversarial network (GAN) structure. The deep learning (DL) architecture consists of two neural networks: a generator, which predicts missing field values of a given magnetic field, and a critic, which is trained to calculate the statistical distance between real and generated magnetic field distributions. By minimizing this statistical distance, a reconstruction loss as well as physical losses, our trained generator has learned to predict the missing field values with a median reconstruction test error of 5.14%, when a single coherent region of field points is missing, and 5.86%, when only a few point measurements in space are available and the field measurements around are predicted. We verify the results on an experimentally validated field., Comment: 10 pages, 12 figures