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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anisotropic magnetism of polymorphic ErAl 3

ErAl 3 can form in either a trigonal (α) or cubic (β) polymorph and this paper investigates the physical properties of these polymorphs through characterizations of single crystals grown in an aluminum flux. Here, we demonstrate that polymorph selection can be achieved based on the nominal composition of the crystal growth. Magnetic measurements confirm that both β-ErAl 3 and α-ErAl 3 order antiferromagnetically at low temperatures. β-ErAl 3 undergoes antiferromagnetic ordering at a Néel temperature T N = 5.1 K, and the transition is suppressed continually with applied field. α-ErAl 3 displays more complex behavior, with successive magnetic transitions at T N = 5.7 K and T 2 = 4.6 K for zero field, where heat capacity and dilatometry measurements evidence that these transitions are second and first order, respectively. Under magnetic field, strong anisotropy is revealed in α-ErAl 3 , with several steplike metamagnetic transitions observed below T 2 for H ∥ c. These transitions produce sequential magnetization plateaus near one-half of the apparent saturation magnetization. The electrical resistivity of α-ErAl 3 is strongly coupled to its magnetism. At T = 2 K, we observe a positive magnetoresistance reaching 60%, with distinct anomalies at the metamagnetic transitions. The results are summarized in H-T phase diagrams that demonstrate complex magnetic behavior for α-ErAl 3 , suggesting an important role of competing interactions in this metallic system that possesses characteristics of Ising physics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on ErAl by Materials Project

ErAl crystallizes in the orthorhombic Pbcm space group. The structure is three-dimensional. there are two inequivalent Er sites. In the first Er site, Er is bonded in a 2-coordinate geometry to eight Al atoms. There are a spread of Er–Al bond distances ranging from 3.06–3.37 Å. In the second Er site, Er is bonded in a 6-coordinate geometry to eight Al atoms. There are a spread of Er–Al bond distances ranging from 3.03–3.54 Å. There are two inequivalent Al sites. In the first Al site, Al is bonded to eight Er and four Al atoms to form a mixture of distorted edge, corner, and face-sharing AlEr8Al4 cuboctahedra. There are two shorter (2.73 Å) and two longer (2.79 Å) Al–Al bond lengths. In the second Al site, Al is bonded in a 10-coordinate geometry to eight Er and two equivalent Al atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

Deep nonparametric estimation of operators between infinite dimensional spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗