Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2207.00456

Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features

Anoop Kini·Amit Kumar Choudhary·Dominic Hohs·Andreas Jansche·Hermann Baumgartl·Ricardo Buettner·Timo Bernthaler·Dagmar Goll·Gerhard Schneider
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Fe14Nd2B-based permanent magnets are technologically sought-after for energy conversion due to their unparalleled high energy product (520 kJ/m3). For such 14:2:1 phases of different compositions, determining the magnetization from the measured magnetic moment is often bottlenecked by lack of mass density. We present a machine learning (ML) mass density model for 14:2:1 phases using chemical composition-based features (representing 33 elements) and optionally lattice parameter (LP) features. The datasets for training and testing contain 190 phases (177 compositionally different) with their literature reported densities and LP. With an ML model with merely compositional features, we achieved a low mean-absolute-error of 0.51% on an unseen test-dataset.

Explore related subjects

cond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anoop Kini, Amit Kumar Choudhary, Dominic Hohs, Andreas Jansche, Hermann Baumgartl, Ricardo Buettner, Timo Bernthaler, Dagmar Goll, Gerhard Schneider. 2022-07-01. Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features. https://doi.org/10.1016/j.cplett.2022.140231

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Structure and dynamics of the negative thermal expansion material Cd(CN)$_2$ under hydrostatic pressure

We use a combination of variable-temperature / variable-pressure neutron powder diffraction, variable-pressure inelastic neutron scattering, and quantum chemical calculations to interrogate the behaviour of the negative thermal expansion (NTE) material $^{114}$Cd(CN)$_2$ under hydrostatic pressure. We determine the equation of state of the ambient-pressure phase, and discover the so-called `warm hardening' effect whereby the material becomes elastically stiffer as it is heated. We also identify a number of high-pressure phases, and map out the phase behaviour of Cd(CN)$_2$ over the range $0\leq p\leq0.5$\,GPa, $100\leq T\leq300$\,K. As expected for an NTE material, the low-energy phonon frequencies are found to soften under pressure, and we determine an effective Gr{ü}neisen parameter for these modes. Finally, we show that the elastic behaviour of Cd(CN)$_2$ is sensitive to the local Cd coordination environment, which suggests an interplay between short- (phononic) and long-timescale (cyanide flips) fluctuations in Cd(CN)$_2$.

cond-mat.mtrl-sci↗
arXiv2026

Unconventional Magnetism, Sliding Ferroelectricity, and Magneto-Optical Kerr Effect in Multiferroic Bilayers

Antiferromagnetic (AFM) materials provide a platform to couple altermagnetic (AM) spin-splitting with the magneto-optical Kerr effect (MOKE), offering potential for next-generation quantum technologies. In this work, first-principles calculations, symmetry analysis, and kp modeling are employed to show that interlayer sliding in AFM multiferroic bilayers enables control of electronic, magnetic, and magneto-optical properties. This study reveals an intriguing dimension-driven AM crossover: the 2D paraelectric (PE) bilayer exhibits spin-degenerate bands protected by the [C2||Mc] spin-space symmetry, whereas the 3D counterpart manifests AM spin-splitting along kz \neq 0 paths. Furthermore, interlayer sliding breaks this Mc symmetry and stabilizes a ferroelectric (FE) state with compensated ferrimagnetism, where the Zeeman-like field is responsible for the nonrelativistic spin-splitting. In the FE phase, spin-orbit coupling (SOC) lifts accidental degeneracies and produces `alternating' spin-polarized bands through the interplay of Zeeman and Rashba effects. Crucially, spin polarization, ferrovalley polarization, and the Kerr angle can all be reversed by switching either sliding ferroelectricity or the Neel vector. Our findings reveal the rich coupling among electronic, magnetic, and optical orders in sliding multiferroics, illustrating new prospects for ultralow-power spintronic and optoelectronic devices.

cond-mat.mtrl-sci↗
arXiv2026

Lead-free piezoelectric perovskites for arterial-pulse e-skin: from configurational complexity to equivariant machine-learning potentials

Continuous, non-invasive monitoring of the arterial pulse is a clinical priority for cardiovascular disease, the leading cause of global mortality. Flexible piezoelectric electronic skins can transduce the 1-10 kPa pressure wave into a self-powered voltage, but the best-performing piezoceramics are lead-based, and their toxicity is incompatible with skin contact and with tightening RoHS/REACH regulation. Among lead-free alternatives, the BaTiO$3$-based solid solution BZT-BCT reaches $d{33} \approx 620$ pC/N near its tricritical morphotropic phase boundary, rivalling soft PZT while remaining biocompatible. Exploiting this in a wearable confronts a sensitivity-flexibility paradox and three computational walls: the combinatorial explosion of atomic configurations in a disordered solid solution, the band-gap error of affordable density-functional approximations, which corrupts leakage and insulation estimates, and the 0 K nature of standard calculations against a 310 K operating temperature. We review lead-free piezoelectrics, morphotropic-boundary physics and fabricated flexible devices, then argue that equivariant machine-learning interatomic potentials --- coupled to a tiered functional hierarchy and finite-temperature lattice dynamics --- can survey the full configurational ensemble at body temperature and close the gap to a clinically viable lead-free pulse sensor.

cond-mat.mtrl-sci↗