Materials Science
Solvation Restructuring Accelerates Early SEI Nucleation in Lithium Metal Batteries
Syed Mustafa Shah, Mohammed Lemaalem and Anh T. Ngo
The development of high-energy-density lithium metal batteries is limited by electrolyte instability and the poorly understood onset of solid-electrolyte interphase (SEI) formation. Here, we use AIMD-trained Deep Potential molecular dynamics to link electrolyte solvation structure to early SEI nucleation in \ce{LiTFSI}/DMC electrolytes. Increasing \ce{LiTFSI} concentration drives the electrolyte from solvent-separated ion pairs toward contact ion pairs and aggregates, with \SI{3.5}{M} showing the strongest anion coordination. This anion-rich environment promotes earlier interfacial Li–F/Li–O bond formation, faster consumption of intact \ce{TFSI-} and solvent, and a denser, LiF/Li$_2$O-rich nascent interphase, in contrast to the more organic-laden, phosphorus/fluorine-based interphase formed by a \SI{1}{M} \ce{LiPF6} reference electrolyte. These results show that bulk solvation architecture biases both the timing and chemistry of early SEI formation and suggest solvation control as a design handle for Li-metal electrolytes.
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Materials Science · Cross-listed from physics.chem-ph
Optimization of random phase approximation calculations for improved energies of molecules, solids, and surfaces
Neung-Kyung Yu, Johannes Voss and Andrew J. Medford
We present an optimized random phase approximation method (optRPA26) that significantly improves upon conventional RPA. The method employs an empirically constructed hybrid functional to generate DFT orbitals to evaluate the RPA correlation energy, which is then scaled by a constant. Comprehensive
benchmarks across molecules, bulk solids, and surface systems demonstrate that optRPA26 consistently achieves high accuracy, with mean absolute errors of 0.05 eV for W4-11 reaction energies, 0.07 eV for cohesive energies, 0.09 eV for metal
oxide formation energies, 0.11-0.12 eV for adsorption of small molecules on metals, and 0.06 eV for adsorption on
oxides. In addition, optRPA26 correctly captures phase stability in metal
oxides and
magnetic metals. The optRPA26 approach can be run using standard RPA implementations, highlighting its potential as a general-purpose reference method that can accurately capture covalent, ionic, and metallic, and van der Waals bonding in molecules, solids, and interfaces.
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Materials Science · Cross-listed from cond-mat.soft
Sculpting 2D Crystals via Membrane Contractions before and during Solidification
Hao Wan, Geunwoong Jeon, Gregory M. Grason et al. (+1 more)
When phospholipids crystallize within the otherwise fluid membranes of
giant unilamellar vesicles, the resulting molecularly-thin "2D" solids exhibit great variety in their morphology
evolution. For instance within membranes containing moderate amounts of the crystallizing component,
crystals grow with a fixed morphology depending on vesicle size. Conversely for membranes containing large amounts of the crystallizing species, we find small compact
crystals on vesicles of all sizes. However on large vesicles, growing
crystals sprout flower petals that lengthen progressively. These behaviors result from two combined mechanisms: First, like other 2D solids, the shear
rigidity of phospholipid
crystals renders them intolerant to morphologies with non-zero Gaussian
curvature. As a result and especially at elevated membrane tension, the cost of bending elasticity is reduced, at the expense of line energy, by the formation of flowers as opposed to compact
crystals. Second, the composition-dependent tension rise during cooling relaxes via water permeation of the membrane with a time constant scaling as $R^2$. The amount of
crystal formed for a small decrease in temperature determines this composition-dependent increase in stress from thermal contractions versus solidification. Surface Evolver computations motivated using the predicted tension
evolution to develop a processing space that maps to experimental observations for initial and growing
crystal morphology. Important variable groups are identified, including a scaled ratio of bending to line energy, a vesicle size-independent group for membrane contractions, and a time constant for stress relaxation. Though processing stresses ultimately relax, the
crystal morphology persists well beyond the processing window.
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All authors Hao Wan, Geunwoong Jeon, Gregory M. Grason, Maria M. Santore
Materials Science · Cross-listed from cond-mat.supr-con
Best practices for a proper evaluation and conversion of physical property equations in superconductors: the examples of WHH formulation, Bean model and other cases of interest
Chiara Tarantini, Evgeny F. Talantsev, Ilaria Pallecchi et al. (+3 more)
In recent years there have been growing concerns about the proper evaluation of physical properties of
superconductors, in particular for quantities extracted from
magnetic characterizations. Errors can and often do occur due to the following issues: i) several measurement instruments still use Gaussian & cgs-emu units instead of the preferable International System (SI) units; ii) there are decades of valuable publications where, however, equations were expressed in Gaussian & cgs-emu or other unit systems or where constants were normalized to unity, which requires proper understanding and unit conversion in order to correctly evaluate the measured physical quantities; iii) the conversion between unit systems sometimes appears challenging and may not be properly performed. In this paper we will describe how to properly convert physical quantities relevant for the evaluation of
magnetic and other properties focusing on the still most used unit systems, SI and Gaussian & cgs-emu. We will provide examples of how to properly verify and understand the physical formulae. We will include examples for the correct method to determine the critical current density Jc of a
superconductor from the measurement of its
magnetic hysteresis loop through the Bean model, and the correct conversion to SI of the equations for Hc2(T) according to the Werthamer-Helfand-Hohenberg (WHH) formulation. The goals of this paper are to make the readers aware of the unit conversion issue, to provide useful hands-on tools for proper conversion and to strongly encourage future exclusive use of the SI units and formulae.
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All authors Chiara Tarantini, Evgeny F. Talantsev, Ilaria Pallecchi, Jens Hänisch, Jeffery L. Tallon, David C. Larbalestier
Materials Science
From Literature to Lab: Closed-Loop Advancement of Perovskite Solar Cells via Domain Knowledge Guided LLM
Penglei Sun, Shuyan Chen, Xiang Liu et al. (+6 more)
Perovskite
solar cells (PSCs) have been considered as a next-generation disruptive photovoltaic technology, yet their advancement is constrained by the complexity of perovskite recipe with high-dimensional material and process design space. Despite the impressive general
reasoning of Large
Language Models (LLMs), they struggle with two limitations for application in PSCs: an inability to align general semantics with the perovskite domain knowledge, and an inefficiency in navigating high-dimensional perovskite material and recipe design spaces. To address these limitations, we introduce a domain-knowledge-guided framework PVK-LLM, a specialized model to serve as an expert to bridge general semantics with perovskite domain knowledge. By integrating this domain knowledge into a hierarchical
Bayesian Optimization workflow, our approach efficiently navigates the high-dimension design space on a
solar cell simulator platform. The domain knowledge resolves cold-start problems while dynamically adapting to simulator feedback. Moreover, in an individual wet-lab experiment aimed at maximizing power conversion efficiency (PCE), our framework autonomously proposes a novel synergistic four-component recipe comprising specialized organic passivation recipe (3MTPAI, PDAI2, EDAI2, and PipDI) which has not been reported in existing literature. This AI-designed recipe effectively achieves a champion PCE value of over 26.0 %, approaching world records achieved through extensive expert trial-and-error. Our approach can effectively enable LLM comprehend the domain knowledge, which can efficiently navigate in a high-dimensional, capable to accelerate the advancement in real-world perovskite as well as other material science development.
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All authors Penglei Sun, Shuyan Chen, Xiang Liu, Longhan Zhang, Huajie You, Chang Yan, Yongqi Zhang, Xiaowen Chu, Tong-yi Zhang
Materials Science · Cross-listed from cs.LG
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Ryan Liu, Eric Qu, Tobias Kreiman et al. (+2 more)
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the
quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-
equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient
benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from
equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained
Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors
algorithm and temperature-controlled
attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric for practitioners to assess MLIP utility and as an "in-the-loop" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP
benchmarks. The BSCT
dataset and evaluation are available on https://github.com/ryanliu30/bsct.git
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All authors Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan
Materials Science
Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys
Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai et al. (+5 more)
Refractory multi-principal element
alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanistic understanding of plastic deformation. Here, we perform atomistic simulations to determine local slip resistances (LSRs) of edge and screw dislocations on primary BCC slip planes in 12 equal-molar RMPEAs. Machine learning is employed to uncover relationships between LSR and underlying material properties, enabling systematic assessment of compositional effects on dislocation behavior. Based on these insights, we develop a thermally activated, dislocation-based model to predict macroscopic yield stress. We find that increasing the fraction of hexagonal close-packed elements above 50% significantly reduces unstable stacking fault energy, ideal shear strength, and screw LSR across all slip planes. Higher elastic anisotropy further lowers these quantities, while
lattice distortion modifies relative slip resistances between dislocation characters and slip systems. By combining an autoencoder with a random forest model, we identify elastic constants and
lattice distortion as the dominant factors controlling LSR. The resulting framework accurately predicts tensile yield stress in BCC RMPEAs and provides guidance for
alloy design.
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All authors Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai, Anshu Raj, Xiaohu Yao, Yanqing Su, Shuozhi Xu, Irene J. Beyerlein
Materials Science
Scalar machine learning of tensorial quantities – Born effective charges from monopole models
Bernhard Schmiedmayer, Angela Rittsteuer, Tobias Hilpert et al. (+1 more)
Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for learning tensorial quantities based on scalar descriptors. We apply this approach to the Born effective charge tensor, showing that scalar (monopole) kernel models can successfully capture its tensorial nature by exploiting the definition of the Born effective charge tensor as the derivative of the polarisation with respect to atomic displacements. We compare this method with tensorial (dipole) kernel models, as established in our previous work, in which the tensorial structure of the Born effective charge is encoded directly in the kernel and obtained via its derivative. Both approaches are then used for charge partitioning, enabling the separation of monopole and dipole contributions. Finally, we demonstrate the effectiveness of the framework by computing finite-temperature infrared spectra for a range of complex materials.
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All authors Bernhard Schmiedmayer, Angela Rittsteuer, Tobias Hilpert, Georg Kresse
Materials Science · Cross-listed from cond-mat.soft
Hydrodynamics substantially affects induced structure formation in magnetic fluids
Henning Reinken, Markus Heiber, Takeaki Araki et al. (+1 more)
Magnetorheological fluids consist of micrometer-sized
magnetic particles in a carrier
liquid. Sufficiently strong external
magnetic fields lead to the formation of string-like particle aggregates. We demonstrate that hydrodynamic interactions, that is, mutual couplings via induced flows, play a substantial role during the structuring process. They support the formation of slender chains instead of more compact clusters in the absence of mutual hydrodynamic interactions between the particles. This fundamental insight is substantial from an application perspective, due to the enormous technical importance and potential of structured magnetorheological materials.
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All authors Henning Reinken, Markus Heiber, Takeaki Araki, Andreas M. Menzel
Materials Science · Cross-listed from cs.LG
DMFlow: Disordered Materials Generation by Flow Matching
Liming Wu, Rui Jiao, Qi Li et al. (+4 more)
The design of materials with tailored properties is crucial for technological progress. However, most deep generative models focus exclusively on perfectly ordered
crystals, neglecting the important class of disordered materials. To address this gap, we introduce DMFlow, a generative framework specifically designed for disordered
crystals. Our approach introduces a unified representation for ordered, Substitutionally Disordered (SD), and Positionally Disordered (PD)
crystals, and employs a flow matching model to jointly generate all structural components. A key innovation is a Riemannian flow matching framework with spherical reparameterization, which ensures physically valid disorder weights on the probability simplex. The vector field is learned by a novel Graph Neural Network (GNN) that incorporates physical
symmetries and a specialized message-passing scheme. Finally, a two-stage discretization procedure converts the continuous weights into multi-hot atomic assignments. To support research in this area, we release a
benchmark containing SD, PD, and mixed structures curated from the Crystallography Open Database. Experiments on
Crystal Structure Prediction (CSP) and De Novo Generation (DNG) tasks demonstrate that DMFlow significantly outperforms state-of-the-art baselines adapted from ordered
crystal generation. We hope our work provides a foundation for the AI-driven discovery of disordered materials.
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All authors Liming Wu, Rui Jiao, Qi Li, Mingze Li, Songyou Li, Shifeng Jin, Wenbing Huang
Materials Science · Cross-listed from cond-mat.dis-nn
Scalable platform enabling reservoir computing with nanoporous oxide memristors for image recognition and time series prediction
Joshua Donald, Ben A. Johnson, Amir Mehrnejat et al. (+5 more)
Typical mammal
brains have some form of random connectivity between
neurons. Reservoir computing, a neural network approach, uses random weights within its processing layer along with built-in recurrent connections and short-term, fading
memory, and is shown to be time and training efficient in processing spatiotemporal signals. Here we prepared a niobium
oxide-based thin film memristor device with intrinsic structural in-homogeneity in the form of random nanopores and performed computational tasks of XOR operations, image recognition, and time series prediction and reconstruction. For the latter task we chose a complex three-dimensional chaotic Lorenz-63 time series. By applying three temporal voltage waveforms individually across the device and training the readout layer with electrical current signals from a three-output physical reservoir, we achieved satisfactory prediction and reconstruction accuracy in comparison to the case of no reservoir. This work highlights the potential for scalable, on-chip devices using all-
oxide reservoir systems, paving the way for energy-efficient neuromorphic electronics dealing with time signals.
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All authors Joshua Donald, Ben A. Johnson, Amir Mehrnejat, Alex Gabbitas, Arthur G. T. Coveney, Alexander G. Balanov, Sergey Savel'ev, Pavel Borisov
Materials Science
Cross-sectional helium irradiation reveals interface-controlled bubble evolution in Cr/CrAlSiN multilayer coatings on zirconium alloys
Renda Wang, Xue Bai, Xueliang Pei et al. (+9 more)
The irradiation stability of Cr based protective coatings on zirconium
alloys is critical for the development of accident-tolerant fuel claddings. However, conventional surface irradiation often produces shallow, nonuniform damage, obscuring interfacial behavior. In this study, we perform cross-sectional He irradiation to directly examine the interfacial response and He bubble
evolution across Cr monolayer and Cr and CrAlSiN multilayer coatings on Zr substrates. Irradiation was carried out at 500 C and 750 C to doses of 2 and 3 dpa, enabling a direct comparison of temperature-dependent microstructural
evolution. In the Cr monolayer, He implantation produced a homogeneous distribution of nanoscale bubbles throughout the damaged region and large cavities at the Cr and Zr interface, indicating severe Kirkendall-type voiding and interfacial decohesion at elevated temperature. In contrast, the Cr/CrAlSiN multilayer exhibited a periodically modulated bubble distribution, with bubble fragmentation and transformation into nanoscale platelets at CrAlSiN interfaces. A N-enriched Zr(N) interlayer formed spontaneously at the CrAlSiN and Zr interface, effectively suppressing bubble accumulation and interdiffusion. The nanochannel interfaces acted as He sinks and
diffusion barriers, enhancing interfacial bonding and mitigating swelling. This work demonstrates that cross-sectional ion irradiation is a powerful approach for probing interfacial stability in multilayer systems, offering new insights into He-defect interactions and radiation tolerance engineering at buried interfaces. The findings highlight the potential of Cr and CrAlSiN multilayers as advanced coating architectures for high-temperature
nuclear environments.
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All authors Renda Wang, Xue Bai, Xueliang Pei, Sijie Liu, Chunfan Liu, Ping Yu, Bingsheng Li, Nabil Daghbouj, Tomas Polcar, Fanping Meng, Fangfang Ge, Qing Huang
Materials Science
Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes
Qingmei Hu, Daniel Hedman, Ya Feng et al. (+17 more)
We investigate iron-cobalt (Fe-Co)
alloys as a representative high-performance catalyst system for SWCNT growth in a systematic manner by combining chemical vapor deposition (CVD) experiments with chirality-resolved spectroscopic analysis, as well as molecular dynamics (MD) simulations based on density functional theory-derived machine learning force fields while varying the Fe-Co ratio. Using zeolite-based SWCNTs prepared by alcohol CVD, absorption and photoluminescence spectroscopy, together with two-dimensional excitation-emission fitting was employed to quantify chirality-specific growth efficiency. Two distinct growth regimes were identified. At a relatively low temperature of 600 C, pure Co exhibits the highest catalytic activity, promoting efficient growth of small-diameter (0.7-0.9 nm) SWCNTs. In contrast, at 850 C, the Fe0.75Co0.25
alloy shows a pronounced enhancement in growth efficiency compared with pure Fe, pure Co, and other Fe-Co compositions, also yielding larger diameter tubes (0.9-1.1nm). Similar growth behavior was observed on SiO2 substrates, enabling detailed transmission electron microscopy analysis of catalyst nanoparticles. Electron microscopy and energy-dispersive X-ray spectroscopy reveal that high SWCNT yields correlate with the formation of small, uniform Fe-Co nanoparticles with Co-enriched surfaces, in excellent agreement with MD simulations. Lastly, MD results are summarized in a composition-diameter phase diagram that rationalizes the experimentally observed growth trends. The exceptional performance of the Fe0.75Co0.25 catalyst at high temperature is attributed to the stabilization of small and uniform catalyst clusters, providing mechanistic insight into the synergistic roles of
alloy composition and temperature in SWCNT growth.
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All authors Qingmei Hu, Daniel Hedman, Ya Feng, Wanyu Dai, Daisuke Asa, Aina Fito-Parera, Yixi Yao, Yongjia Zheng, Kaoru Hisama, Gunjan Auti, Hirofumi Daiguji, Shohei Chiashi, Dmitry Levshov, Wim Wenseleers, Keigo Otsuka, Yan Li, Christophe Bichara, Sofie Cambre, Rong Xiang, Shigeo Maruyama
Materials Science
Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi et al. (+2 more)
The design of sustainable materials requires access to materials performance and sustainability data from literature corpus in an organized, structured and automated manner. Natural
language processing approaches, particularly large
language models (LLMs), have been explored for materials data extraction from the literature, yet often suffer from limited accuracy or narrow scope. In this work, an LLM-based pipeline is developed to accurately extract
alloy-related information from both textual descriptions and tabular data across the literature on high-
entropy (or multicomponent)
alloys (HEA). Specifically two databases with 37,711 and 148,069 entries respectively are retrieved; one from the literature text, consisting of
alloy composition, processing conditions, characterization methods, and reported properties, and other from the literature tables, consisting of property names, values, and units. The pipeline enhances materials-domain sensitivity through
prompt engineering and
retrieval-augmented generation and achieves F1-scores of 0.83 for textual extraction and 0.88 for tabular extraction, surpassing or matching existing approaches. Application of the pipeline to over 10,000 articles yields the largest publicly available multicomponent
alloy database and reveals compositional and processing-property trends. The database is further employed for sustainability-aware materials selection in three application domains, i.e., lightweighting, soft
magnetic, and corrosion-resistant, identifying multicomponent
alloy candidates with more sustainable production while maintaining or exceeding
benchmark performance. The pipeline developed can be easily generalized to other class of materials, and assist in development of comprehensive, accurate and usable databases for sustainable materials design.
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All authors Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra
Materials Science
Nonreciprocal topological kink-wave propagation in mechanical metamaterials
Brahim Lemkalli, Qingxiang Ji, Jingyi Zhang et al. (+3 more)
Nonlinear mechanical metamaterials can exhibit emergent transport phenomena that mimic topological protection without relying on linear band
topology. Here, we realize a bifurcation-induced nonreciprocal
lattice that supports robust propagation of elastic kink waves. Each unit is a prestrained, hinged-beam circulator that develops angular momentum bias during snap-through transitions between buckling states, producing an effective breaking of time reversal
symmetry. Coupling such units into a hexagonal array yields a mechanically
chiral network where localized
soliton-like excitations propagate unidirectionally along interfaces and edges, immune to sharp bends. We demonstrate non-dispersive kink transport governed by a SineGordon type field whose effective bias encodes mechanical chirality. This framework bridges bifurcation dynamics and nonreciprocal transport, establishing a nonlinear route toward topological like mechanical functionality without
magnetic or gyroscopic bias.
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All authors Brahim Lemkalli, Qingxiang Ji, Jingyi Zhang, Richard Craster, Johan Christensen, Muamer Kadic
Materials Science
Fermi surface geometry and momentum dependent electron-phonon coupling drive the charge density wave in quasi-1D ZrTe$3$
Josu Diego and Matteo Calandra
ZrTe$_3$ is a prototypical quasi-one-dimensional compound undergoing a charge density wave transition via a very sharp Kohn anomaly in
phonon momentum space. While
Fermi surface geometry has long been considered the primary driver of the instability, a full understanding of the
lattice dynamics and electron-
phonon role has remained elusive. Our first principles calculations in the high-
symmetry phase show that the
Fermi surface is correctly reproduced only when the Hubbard interaction on the Te $5p$ orbitals is included, which in turn is essential for the appearance of a soft harmonic
phonon mode at the CDW wavevector. Analyzing the mode and momentum dependence of the electron-
phonon coupling, we find that its variations with
phonon momentum dominate over electronic effects. These results identify unambiguously the CDW origin in ZrTe$_3$ as a cooperative effect of
Fermi surface geometry and momentum-dependent electron-
phonon coupling, with the latter playing the leading role. We further determine the atomic structure in the low-
symmetry CDW phase, revealing a nonchiral modulation. The mechanisms revealed in our work are directly relevant to other quasi-1D systems, including trichalcogenides and compounds hosting Peierls-like chains.
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Materials Science
Triple Junctions as Dislocation-Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation
Tobias Brink, Saba Saood, Peter Schweizer et al. (+2 more)
Grain boundary networks and their
evolution are strongly influenced by triple junctions. The defect nature of these line defects significantly affects the properties of the network, but they have not been fully characterized to date. Here, we use scanning transmission electron microscopy combined with atomistic computer simulations to investigate a triple junction at the atomic scale in an Al thin film with {111} texture. Using sampling methods, we were able to construct the same junction structure as in the experiment within a computer model. We present a technique to calculate the Burgers vector of the triple junction. This allows us to connect the junction's dislocation character to the microscopic degrees of freedom of the joining grain boundaries. The junction line energy in the computer model can then be calculated using an embedded atom method potential. It follows the same laws as a bulk dislocation. Finally, we discovered a range of possible triple junctions for the observed grain boundaries, which vary in the magnitude of their Burgers vector. Interestingly, the experimentally observed junction is not the one with the smallest possible Burgers vector and energy. This suggests that the kinetics of transforming the junction line are likely too slow to be driven by the small energy contribution of the triple junction.
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All authors Tobias Brink, Saba Saood, Peter Schweizer, Jörg Neugebauer, Gerhard Dehm
Materials Science
Strain tunable anomalous Hall and Nernst conductivities in compensated ferrimagnetic Mn$_3$Al
Guihyun Han, Minkyu Park and S. H. Rhim
The tunability of anomalous
Hall and Nernst conductivities is investigated in the compensated ferrimagnet Mn$_3$Al under isotropic strain ($\eta$) and chemical potential variation using first-principles calculations. At a chemical potential of $\mu = -0.3$ eV, three distinct topological features – Weyl points, nodal lines, and gapped nodal lines – are simultaneously realized along high-
symmetry directions of the Brillouin zone in the framework of
magnetic space group. The anomalous
Hall conductivity (AHC) is found to be predominantly governed by the Berry
curvature in the $k_y k_z$ plane and can be enhanced significantly under tensile strain, reaching $-1200$ $(\Omega~\mathrm{cm})^{-1}$. On the other hand, the anomalous Nernst conductivity (ANC) shows a sign change near the
Fermi level and whose magnitude increases at $\mu = -0.3$ eV with quasi-quadratic strain dependence. Regardless of strain, the underlying bands and
Fermi surface structures remain robust, while the distribution and magnitude of Berry
curvature evolve substantially. These results underscore the potential of Mn$_3$Al, a compensated ferrimagnet, as a platform for Berry
curvature engineering via strain and doping.
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Materials Science
Microscopic Origin of Polarization-Controlled Magnetization Switching in FePt/BaTiO$_3$
Qurat-ul-ain, Thi H. Ho, Soon Cheol Hong et al. (+2 more)
Electric-field driven magnetization switching in FePt/BaTiO$_3$ (001) is demonstrated through first-principles calculations. The
magnetic easy axis of FePt layer undergoes a transition from in-plane to perpendicular direction upon ferroelectric polarization reversal, a process sensitively controlled by epitaxial strain with threshold strain strain($\eta$) $\eta\approx\%$. At this phenomena, a large interfacial magnetoelectric coupling ($\alpha_I = 3.6 \times 10^{-10}$ G$\cdot$cm$^2$/V) is responsible, stemming from the orbital reconstruction. In particular, the redistribution of Pt-$d$ orbital occupancy alters
spin-orbit coupling, thereby tuning the competition between
magnetic anisotropy ($K_i$) and magnetoelastic energy ($b_1$). Our work clarifies the fundamental physics of strain-engineered magnetoelectricity and suggests a concrete pathway for designing ultra-low-power voltage-controlled
magnetic memory.
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All authors Qurat-ul-ain, Thi H. Ho, Soon Cheol Hong, Dorj Odkhuu, S. H. Rhim
Materials Science
Potential-Induced Dynamic Coordination of Nonmetal Atoms Directly Bound to Metal Centers in Graphene-Embedded Single-Atom Catalysts and Its Implications
Jiahang Li, Suhang Li, Chong Yan et al. (+3 more)
Electrode-potential-induced dynamic coordination is an essential factor governing the performance of
graphene-embedded single-atom catalysts (SACs). While previous studies have primarily centered on structural dynamics at the metal site, the response of its coordinated nonmetal atoms remains largely unexplored. Here, using Ni SACs with mixed nitrogen/carbon coordination (NiN4-xCx) as representatives, we investigate potential-driven hydrogenation of metal-center-coordinated nonmetallic atoms through constant-potential density functional theory and ab initio molecular dynamics. We find that the C sites directly bound to Ni undergo potential-driven hydrogenation, whereas hydrogenation at N sites is thermodynamically unfavorable. Taking NiNC3 as a representative system, we demonstrate that these hydrogenation processes proceed with accessible kinetic barriers and obey the Brønsted-Evans-Polanyi relation. The resulting dynamic coordination reshapes the Ni 3d and dz2 orbitals, modulates the stability of the active center, and weakens molecular adsorption through combined electronic and steric effects. These findings reveal that electrode potential and solvent not only regulate the metal center but also dynamically reconfigure its coordination environment, offering novel mechanistic insights into potential-induced coordination dynamics and guiding the rational design of coordination-engineered SACs.
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All authors Jiahang Li, Suhang Li, Chong Yan, Qinzhuang Liu, Jiajun Yu, Dongwei Ma
Materials Science
Electronic States, Spin-Orbit Coupling and Magnetism in Germanium 60{\deg} Dislocations
Veronica Regazzoni, Fabrizio Rovaris, Anna Marzegalli et al. (+2 more)
Defects in
semiconductors have recently attracted renewed interest owing to their potential in novel
quantum applications. Here we investigate the electronic and
magnetic properties induced by 60{\deg} dislocations in Ge. Using large-scale DFT calculations, we determine the band structure for both the shuffle and glide sets in their lowest-energy configurations. The band structure for the shuffle set reveals defect-induced dispersive bands localized within the band gap near the $\Gamma$ point, whereas for the glide set, we observe strong overlap with the conduction band. Defect-induced band splitting evident away from $\Gamma$ reveals Rashba-Dresselhaus
spin-orbit coupling, an effect previously reported only for screw dislocations. Remarkably, we find evidence that specific dislocation arrangements can stabilize
antiferromagnetic ordering with sizable local
magnetic moments and considerable exchange splitting between opposite
spin states. These results uncover rich physics in Ge dislocations through the combination of
spin-orbit coupling and
magnetic ordering, potentially enabling novel defect-based functionalities in Ge devices.
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All authors Veronica Regazzoni, Fabrizio Rovaris, Anna Marzegalli, Francesco Montalenti, Emilio Scalise
Materials Science
Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
Ansu Mathew, Ahmer A. B. Baloch, Alamin Yakasai et al. (+3 more)
Prediction of
crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science, particularly for perovskite materials which are known for their diverse applications in photovoltaics, optoelectronics, and catalysis. In this study, we present a machine learning (ML)-driven framework that leverages advanced models, including Time Series Forest (TSF), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term
Memory (LSTM), Gated Recurrent Unit (GRU), and a simple feedforward neural network (NN), to classify
crystal systems, point groups, and space groups from XRD data of perovskite materials. To address class imbalance and enhance model robustness, we integrated feature augmentation strategies such as Synthetic Minority Over-sampling Technique (SMOTE), class weighting, jittering, and spectrum shifting, along with efficient data preprocessing pipelines. The TSF model with SMOTE augmentation achieved strong performance for
crystal system prediction, with a Matthews correlation coefficient (MCC) of 0.9, an F1 score of 0.92, and an accuracy of 97.76%. For point and space group prediction, balanced accuracies above 95% were obtained. The model demonstrated high performance for
symmetry-distinct classes, including cubic
crystal systems, point groups 3m and m-3m, and space groups Pnma and Pnnn. This work highlights the potential of ML for XRD-based structural characterization and accelerated discovery of perovskite materials
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All authors Ansu Mathew, Ahmer A. B. Baloch, Alamin Yakasai, Hemant Mittal, Vivian Alberts, Jayakumar V. Karunamurthy
Materials Science
ZnCdO:Eu Epitaxially Grown Alloys for Self-Powered Ultrafast Broadband Photodetection
Igor Perlikowski, Eunika Zielony, Aleksandra Wierzbicka et al. (+3 more)
Photodetectors (PDs) are essential in imaging, communication, and sensing technologies. However, their reliance on external power makes them energy-consuming. This creates a strong need for self-powered PDs as a sustainable alternative. ZnO is a promising
semiconductor material due to its pyroelectric properties, stemming from non-centrosymmetric wurtzite
crystal structure, enabling the pyro-phototronic effect that enhances response speed. Properties of ZnO can be tailored via alloying and doping. Thus, this work explores thin layers of ZnCdO:Eu random
alloys grown by molecular beam epitaxy (MBE) on silicon substrates, with varying Cd content. The study shows that doping with Eu notably affects growth kinetics, promoting strong [0001] orientation preference. Moreover, photoluminescence measurements confirm the successful incorporation of Eu3+ ions into the structure. Electrical measurements show that the introduction of Cd eliminates the problem of Schottky barrier formation on the ZnO/Si interface. The n-ZnCdO:Eu/p-Si junctions exhibit rectifying behavior and generate photocurrent across 380-1150 nm wavelength range without external electrical bias. Utilizing the pyro-phototronic effect, these devices achieved ultrafast response times: rise time below 10 us and decay time below 5 us for 405 nm and 650 nm illumination - placing them among the fastest self-powered
oxide-based detectors that do not rely on additional performance-enhancing layers.
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All authors Igor Perlikowski, Eunika Zielony, Aleksandra Wierzbicka, Anastasiia Lysak, Rafal Jakiela, Ewa Przezdziecka
Materials Science
Revealing the interfacial kinetic mechanisms in high-entropy doped Na$_3$V$_2$(PO$_4$)$_3$ through electrochemical investigation and distribution of relaxation times
Manish Kr. Singh and Rajendra S. Dhaka
We designed a high-
entropy doped NASICON cathode, Na$_3$V$_{1.9}$(CrMoAlZrNi)$_{0.1}$(PO$_4$)$_3$ and investigate its electrochemical performance for sodium-ion batteries (SIBs) to understand the
diffusion mechanism including distribution of relaxation times analysis of interfacial kinetics. This trace doping induces high-
entropy mixing at the vanadium site, tuning the
lattice and enhancing specific capacity, activating V$^{4+}$/V$^{5+}$ redox couple 3.95~V. Interestingly, it delivers a reversible capacity of 119~mAh~g$^{-1}$ at 0.1~C, and demonstrate excellent stability of 68% after 1000 cycles at 10~C. The calculated
diffusion coefficient values are found within the range of \(10^{-11}\)–\(10^{-13}~\mathrm{cm^2\,s^{-1}}\). The systematic investigation of temperature and voltage-dependent impedance data using the distribution of relaxation times provides deeper insights into the underlying charge-transfer and transport processes. The full
cells with hard carbon delivers 326~Wh~kg$^{-1}$ (with respect to cathode mass) at $\approx$3.2~V and retained $\sim$79% capacity after 100 cycles at 2~C. Our study opens new avenues for developing high-
entropy doped cathodes for enhanced structural stability, extended redox activity, and optimized electrochemical kinetics for practical implementation of SIBs.
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Materials Science
Flexocurrent-induced magnetization: Strain gradient-induced magnetization in time-reversal symmetric systems
Shinnosuke Koyama, Takashi Koretsune and Kazumasa Hattori
Symmetry constraints determine which physical responses are allowed in a given system. Magnetization induced by strain fields, such as in piezomagnetic and flexomagnetic effects, has typically been considered in materials that break time-reversal
symmetry. Here, we propose that nonuniform strain can induce magnetization even in nonmagnetic metals and
semiconductors that preserve time-reversal
symmetry. This mechanism differs from the conventional flexomagnetic effect: the strain gradient acts as a driving force on the electrons, generating magnetization in a manner closely analogous to current-induced magnetization. Treating the strain field as an external field, we derive a general expression for the magnetization induced by a strain gradient and demonstrate that this response is
symmetry-allowed even in time-reversal symmetric systems. We apply our formulation to nonmagnetic systems that lack spatial inversion
symmetry while preserving time-reversal
symmetry, using a decorated square
lattice, monolayer MoS$_2$, and monolayer Janus MoSSe as representative examples. We find a finite magnetization response to strain gradients, which is consistent with
symmetry arguments, supporting the validity of our theoretical framework. These results offer a pathway for controlling magnetization in nonmagnetic materials using strain fields.
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