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Physics Condensed Matter Materials Science

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Monday 2 February

Materials Science · Cross-listed from physics.geo-ph

Machine learning determines the Mg2SiO4 P-T phase diagram

Siyu Zhou, Daohong Liu, Chuanyu Zhang et al. (+3 more)

Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase boundaries remain debated, largely due to experimental limitations at extreme conditions and the high computational cost of first-principles free-energy calculations. Here, a machine-learning-potential driven workflow combining non-equilibrium thermodynamic integration (NETI) and two-phase coexistence simulations is employed to enable large-scale, long-timescale molecular dynamics sampling. Within this workflow, the melting curve of forsterite is evaluated and a complete P-T phase diagram is constructed. Relative to conventional ab initio approaches, this strategy reduces computational expense while retaining thermodynamic consistency in phase-stability assessment. The workflow is applicable to efficient evaluation of phase stability and thermodynamic properties in deep-Earth silicate systems.
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All authors Siyu Zhou, Daohong Liu, Chuanyu Zhang, Yu He, Xuben Wang, Xiaopan Zuo

Materials Science · Cross-listed from cs.CV

Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis

Salma Zahran, Zhou Ao, Zhengyang Zhang et al. (+3 more)

Semantic segmentation of microscopy images is a critical task for high-throughput materials characterisation, yet its automation is severely constrained by the prohibitive cost, subjectivity, and scarcity of expert-annotated data. While physics-based simulations offer a scalable alternative to manual labelling, models trained on such data historically fail to generalise due to a significant domain gap, lacking the complex textures, noise patterns, and imaging artefacts inherent to experimental data. This paper introduces a novel framework for labour-free segmentation that successfully bridges this simulation-to-reality gap. Our pipeline leverages phase-field simulations to generate an abundant source of microstructural morphologies with perfect, intrinsically-derived ground-truth masks. We then employ a Cycle-Consistent Generative Adversarial Network (CycleGAN) for unpaired image-to-image translation, transforming the clean simulations into a large-scale dataset of high-fidelity, realistic SEM images. A U-Net model, trained exclusively on this synthetic data, demonstrated remarkable generalisation when deployed on unseen experimental images, achieving a mean Boundary F1-Score of 0.90 and an Intersection over Union (IOU) of 0.88. Comprehensive validation using t-SNE feature-space projection and Shannon entropy analysis confirms that our synthetic images are statistically and featurally indistinguishable from the real data manifold. By completely decoupling model training from manual annotation, our generative framework transforms a data-scarce problem into one of data abundance, providing a robust and fully automated solution to accelerate materials discovery and analysis.
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All authors Salma Zahran, Zhou Ao, Zhengyang Zhang, Chen Chi, Chenchen Yuan, Yanming Wang

Materials Science

Ferromagnetic Ferroelectricity due to Orbital Ordering

I. V. Solovyev

Realization of ferromagnetic ferroelectricity, combining two ferroic orders in a single phase, is the longstanding problem of great practical importance. One of the difficulties is that ferromagnetism alone cannot break inversion symmetry $\mathcal{I}$. Therefore, such a phase cannon be obtained by purely magnetic means. Here, we show how it can be designed by making orbital degrees of freedom active. The idea can be traced back to a basic principle of interatomic exchange, which states that an alternation of occupied orbitals along a bond (i.e., antiferro orbital order) favors ferromagnetic coupling. Moreover, the antiferro orbital order breaks $\mathcal{I}$, so that the bond becomes not simply ferromagnetic but also ferroelectric. Then, we formulate main principles governing the realization of such a state in solids, namely: (i) The magnetic atoms should not be located in inversion centers, as in the honeycomb lattice; (ii) The orbitals should be flexible enough to adjust they shape and minimize the energy of exchange interactions; (iii) This flexibility can be achieved by intraatomic interactions, which are responsible for Hund's second rule and compete with the crystal field splitting; (iv) For octahedrally coordinated transition-metal compounds, the most promising candidates appear to be iodides with a $d^{2}$ configuration and relatively weak $d$-$p$ hybridization. Such a situation is realized in the van der Walls compound VI$_3$, which we expect to be ferromagnetic ferroelectric.
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Materials Science · Cross-listed from cond-mat.mes-hall

Steady-state skin effect in bosonic topological edge states under parametric driving

Nobuyuki Okuma

Non-Hermitian systems have attracted significant theoretical interest due to their extreme properties. However, realizations have mostly been limited to classical applications or artificial setups. In this study, we focus on the quantum nature inherent in bosonic Bogoliubov-de Gennes (BdG) systems, which from the perspective of spectral theory corresponds to non-Hermiticity. Based on this insight, we propose a steady-state skin effect in quantum condensed matter utilizing such BdG non-Hermiticity. Specifically, we introduce BdG quantum terms arising from parametric pumping to the edge states of an underlying bosonic Hermitian Chern insulator, thereby realizing non-Hermiticity without dissipation. This system design has the advantage of being largely independent of microscopic model details. Through analysis using non-equilibrium Green's functions, we find that under open boundary conditions, a steady state exhibiting the non-Hermitian skin effect is realized. The pronounced corner particle accumulation observed in this steady state shows quadrature anisotropy, which manifests the bosonic quantum nature. Our results bridge the gap between the fascinating mathematics of non-Hermitian matrices and practical quantum physical systems.
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Sunday 1 February

Materials Science · Cross-listed from cond-mat.soft

Mechanics of hierarchical twisted and coiled polymer artificial muscles: Decoupling force from kinematic limits

Ye Xiao, Zhao Luo, Falin Tian et al. (+3 more)

Thermally actuated twisted and coiled polymer (TCP) artificial muscles exhibit exceptional specific work capacities but are limited by an inherent competition between load-bearing capacity and actuation stroke. To address this limitation, we investigate a hierarchical helical structure designed to decouple force generation from kinematic limits. We propose a coupled thermo-mechanical model incorporating inter-filamentary contact mechanics and geometric nonlinearities to predict the assembly's equilibrium response. The results indicate that this hierarchical topology significantly amplifies isometric actuation stress compared to monofilament baselines, while maintaining a biological-like contraction stroke of approximately 22%. A critical topological threshold governed by the balance between cooperative load-sharing and geometric confinement is identified. Beyond an optimal bundle complexity, the geometric jamming dominates, as excessive inter-filamentary friction hinders actuation. Furthermore, we elucidate a stiffness-stroke synergy in homochiral configurations, where high helical angles amplify the thermal untwisting torque to overcome increased structural rigidity. Crucially, the volumetric energy density exhibits scale invariance regarding the hierarchical radius, implying that absolute force output can be linearly scaled through geometric upsizing without compromising efficiency. These findings provide a mechanics-based rationale for the structural programming, demonstrating that soft actuator performance limits are dictated by topological order rather than intrinsic material properties.
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All authors Ye Xiao, Zhao Luo, Falin Tian, Xinghao Hu, Dabiao Liu, Chun Li

Materials Science · Cross-listed from math-ph

General teleparallel geometric theory of defects

Muzaffer Adak, Ertan Kok and Mehmet Orhan

We revisit the geometric theory of defects. In the differential-geometric models of defects that have been adopted since the 1950s, dislocations have been associated with torsion, disclinations with the full curvature, and point defects with the first kind trace of non-metricity. The mainstream formulation exhibits several conceptual and technical shortcomings, most notably a hierarchy inconsistency, the non-exictence of a genuine metric formulation, and the potential emergence of Ostrogradsky-type instabilities. These issues have motivated us to develop a new framework, namely a generalized teleparallel geometric theory of defects. In our model, dislocations are identified with the trace of torsion, disclinations with the second kind trace of the non-metricity, and point defects with the first kind trace of the non-metricity. In addition, we retain the scalar part torsion as a free parameter for describing some possible unknown degrees of freedom in the theory of defects. The proposed geometric theory of defects is free from all of the aforementioned drawbacks and is therefore worthy of further investigation. To ensure the coherence and completeness of the discussion, we begin our analysis with elastic deformations, then summarize the existing metric-affine geometric theory of defects, and finally proceed to our original contribution, namely the new theory introduced here. We formulate the entire theory in Eulerian coordinates. Naturally, all results can be reformulated in Lagrangian coordinates as well. All analyses and formulae are expressed in the language of exterior algebra and are carried out in coordinate-independent orthonormal frames.
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Materials Science

Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties

Abril Azocar Guzman, Hoang-Thien Luu, Sarath Menon et al. (+3 more)

Mechanical and thermodynamic properties, including the influence of crystal defects, are critical for evaluating materials in engineering applications. Molecular dynamics simulations provide valuable insight into these mechanisms at the atomic scale. However, current practice often relies on fragmented scripts with inconsistent metadata and limited provenance, which hinders reproducibility, interoperability, and reuse. FAIR data principles and workflow-based approaches offer a path to address these limitations. We present reusable atomistic workflows that incorporate metadata annotation aligned with application ontologies, enabling automatic provenance capture and FAIR-compliant data outputs. The workflows cover key mechanical and thermodynamic quantities, including equation of state, elastic tensors, mechanical loading, thermal properties, defect formation energies, and nanoindentation. We demonstrate validation of structure-property relations such as the Hall-Petch effect and show that the workflows can be reused across different interatomic potentials and materials within a coherent semantic framework. The approach provides AI-ready simulation data, supports emerging agentic AI workflows, and establishes a generalizable blueprint for knowledge-based mechanical and thermodynamic simulations.
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All authors Abril Azocar Guzman, Hoang-Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld

Materials Science

Magnetic, transport and electronic properties of Ni$_2$FeAl Heusler alloy nanoparticles: Experimental and theoretical investigation

Priyanka Yadav, Mohd Zeeshan, Brajesh K. Mani et al. (+1 more)

We present a comprehensive investigation of structural, magnetic and transport properties of Ni$_2$FeAl Heusler alloy nanoparticles (NPs) synthesized via template-less chemical route. The NPs exhibit high saturation magnetization of 3.02 $\mu_ {\rm B}$/f.u. at 5~K, large magnetic anisotropy of 0.238 MJ/m$^3$, and a Curie temperature of 874~K. Magnetocaloric analysis reveals a magnetic entropy change of 3.1 J.kg$^{-1}$K$^{-1}$ at 70 kOe. Low-temperature transport measurements show a weak resistivity upturn, following a $-T^{1/2}$ dependence, indicative of disorder-enhanced electron-electron interactions. First-principles calculations based on density functional theory yield a magneto-crystalline anisotropy energy of 0.987 MJ/m$^3$, consistent with experiment and demonstrate pronounced surface and finite-size effects through comparison of bulk and nanocluster geometries. The combination of high Curie temperature, sizable perpendicular magnetic anisotropy, and moderate spin polarization and magnetic entropy change make the Ni$_2$FeAl as promising candidate for various applications.
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All authors Priyanka Yadav, Mohd Zeeshan, Brajesh K. Mani, Rajendra S. Dhaka

Materials Science

AI Meets Plasticity: A Comprehensive Survey

Hadi Bakhshan, Sima Farshbaf, Junior Ramirez Machado et al. (+2 more)

Artificial intelligence (AI) is rapidly emerging as a new paradigm of scientific discovery, namely data-driven science, across nearly all scientific disciplines. In materials science and engineering, AI has already begun to exert a transformative influence, making it both timely and necessary to examine its interaction with materials plasticity. In this study, we present a holistic survey of the convergence between AI and plasticity, highlighting state-of-the-art AI methodologies employed to discover, construct surrogate models for, and emulate the plastic behavior of materials. From a materials science perspective, we examine cause-and-effect relationships governing plastic deformation, including microstructural characterization and macroscopic responses described through plasticity constitutive models. From the perspective of AI methodology, we review a broad spectrum of applied approaches, ranging from frequentist techniques such as classical machine learning (ML), deep learning (DL), and physics-informed models to probabilistic frameworks that incorporate uncertainty quantification and generative AI methods. These data-driven approaches are discussed in the context of materials characterization and plasticity-related applications. The primary objective of this survey is to develop a comprehensive and well-organized taxonomy grounded in AI methodologies, with particular emphasis on distinguishing critical aspects of these techniques, including model architectures, data requirements, and predictive performance within the specific domain of materials plasticity. By doing so, this work aims to provide a clear road map for researchers and practitioners in the materials community, while offering deeper physical insight and intuition into the role of AI in advancing materials plasticity and characterization, an area of growing importance in the emerging AI-driven era.
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All authors Hadi Bakhshan, Sima Farshbaf, Junior Ramirez Machado, Fernando Rastellini Canela, Josep Maria Carbonell

Materials Science

A Generalizable Machine Learning Framework for Half-Heusler Thermoelectrics

Shoeb Athar, Adrien Mecibah and Philippe Jund

Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suffer from limited generalizability. Here we present a robust ML framework for predicting the thermoelectric figure of merit (zT) in half-Heusler (hH) compounds, emphasizing prediction of previously unseen compositions. A principal component analysis (PCA)-based train/test splitting strategy is introduced to generate representative partitions of chemical space. Bayesian hyperparameter optimization is combined with correlation-based k-best feature selection for Random Forest, XGBoost, and Neural Network models, together with the interpretable SISSO symbolic-regression method. Relative to the full set of 118 input features, the optimized feature subsets reduce the test RMSE by 16-27%. SHAP and SISSO analyses identify temperature, A-site dopant concentration, and A-site heat of vaporization as the most important descriptors of zT. The framework is validated through prediction of previously unexplored compositions, recovery of literature-reported optimal zT values for established hH systems, and first-principles calculations for the new compounds CeCoBi and CeIrBi, which exhibit favorable electronic transport properties. The framework is further applied to high-throughput screening of the doped hH chemical space, covering about 2.46 million elemental substitution combinations across 631 thermodynamically screened parent matrices under stability constraints. This screening yields a concise set of promising candidates for future study. Overall, the work establishes a generalizable workflow for reliable data-driven thermoelectric materials discovery.
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Materials Science · Cross-listed from cond-mat.mes-hall

Anisotropic electron gas in a hyperbolic van der Waals material

Nicola Melchioni, Andrea Mancini and Antonio Ambrosio

Electron gases in low dimensional materials exhibit unconventional transport and optical phenomena due to reduced phase space, enhanced interactions, and strong sensitivity to lattice symmetry. While commonly realized in quantum confined systems and engineered heterostructures, such states are rare in naturally occurring materials. Hyperbolic materials provide a compelling alternative, as extreme lattice anisotropy can host unconventional electronic states and novel electron-phonon interactions. Here, we investigate the angle resolved polarized Raman (ARPR) response of MoOCl2, the first naturally occurring hyperbolic material whose hyperbolicity originates from a highly anisotropic electron gas. We observe pronounced polarization dependent Fano line shapes, revealing coherent coupling between phonons and an anisotropic electronic continuum. We characterize the directional response of this continuum, incorporating it into effective Raman tensors that quantitatively reproduce the ARPR measurements and capture the distinct Raman fingerprint of MoOCl2. Excitation energy and thickness dependent ARPR measurements further demonstrate a tunable quasi 1D electronic continuum with weak interlayer coupling, establishing MoOCl2 as a model system for Raman studies of electron-phonon coupling in hyperbolic materials
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Saturday 31 January

Materials Science

Disproportionate influence of site disorder on the evolution of magnetic phases in anti-Heusler alloy Al$_2$MnFe

Soumya Bhowmik, Santanu Pakhira, Ashis Kundu et al. (+3 more)

Anti-Heusler alloys, being a new addition to the Heusler alloys family, exhibit atomic disorders, and almost all of them are reported as a re-entrant spin-glass system. Although such spin-glass feature is generally attributed to the inherent atomic disorder, a comprehensive and extensive investigation on the individual roles of different types of disorders in magnetic interactions remains lacking for any of the reported anti-Heusler systems. As an illustrative case, we have carried out an in-depth experimental as well as theoretical investigation of structural, magnetic, and transport properties of a polycrystalline anti-Heusler alloy, Al$_2$MnFe. While the major atomic disorder is found to be among Fe and Mn atoms, which are randomly distributed among the two octahedral sites, 4$a$ and 4$b$ (B2-type disorder), a relatively small fraction ($\sim$12%) of Mn atoms also replace Al atoms at the tetrahedral 8$c$ site. Magnetically, the system undergoes two transitions: a paramagnetic to a ferromagnetic transition at $T_{\rm C}\sim$113~K, followed by a spin-glass phase transition below $T_{\rm f}\sim$20~K. Here, the magnetic moment is primarily confined to Mn atoms. Very interestingly, our theoretical analysis reveals that the ferromagnetic spin arrangement remains rather robust in spite of the 50% disorder of moment-carrying Mn atoms between the two octahedral sites, but a much smaller ($\sim$12%) cross-distribution of Mn atoms between octahedral and tetrahedral sites are sufficient to impose a reentrant spin-glass state at low temperature. Our analysis brings forth the importance of understanding the role of individual types of swap-disorder on magnetic properties in the anti-Heusler family of materials.
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All authors Soumya Bhowmik, Santanu Pakhira, Ashis Kundu, V. Raghavendra Reddy, Mukul Kabir, Chandan Mazumdar

Materials Science

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks

Brandon Schoener, Yuting Hu, Pasit Wanlapha et al. (+6 more)

Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles techniques. To address this, recent research has created experimental databases from information extracted from scientific literature. However, most existing experimental databases do not provide full atomic coordinate information, which prevents them from supporting advanced ML architectures such as Graph Neural Networks (GNNs). In this work, we propose to bridge this gap through an alignment process between experimental databases and Crystallographic Information Files (CIF) from the Inorganic Crystal Structure Database (ICSD). Our approach enables the creation of a database that can fully leverage state-of-the-art model architectures for material property prediction. It also opens the door to utilizing transfer learning to improve prediction accuracy. To validate our approach, we align NEMAD with the ICSD and compare models trained on the resulting database to those trained on NEMAD originally. We demonstrate significant improvements in both Mean Absolute Error (MAE) and Correct Classification Rate (CCR) in predicting the ordering temperatures and magnetic ground states of magnetic materials, respectively.
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All authors Brandon Schoener, Yuting Hu, Pasit Wanlapha, Akshay Rengarajan, Ian Moog, Michael Wang, Peihong Zhang, Jinjun Xiong, Hao Zeng

Materials Science

Momentum- and frequency-resolved collective electronic excitations in solids: insights from spectroscopy and first-principles calculations

Dario A. Leon and Kristian Berland

Collective electronic excitations, including plasmons, excitons, and intra- and interband transitions, play a central role in determining the dynamic screening, optical response, and energy transport properties of materials. Recent advances in momentum- and frequency-resolved spectroscopies, such as electron energy-loss spectroscopy (EELS) and inelastic x-ray scattering (IXS), together with progress in first-principles many-body perturbation theory (MBPT) calculations, now allow collective excitations to be mapped with considerable precision across the Brillouin zone. This topical review surveys current developments in the representation and interpretation of both experimental and theoretical dielectric-response spectra. Particular emphasis is placed on recent ways of representing spectral band structures (SBS) of the direct and inverse dielectric functions, such as analytical approaches based on multipole-Padé approximants in momentum and frequency (MPA($\q$)), which provide a combined band-like description of the dispersion of the main collective excitations. We discuss how features observed in metals, semiconductors, and low dimensional systems reflect the interplay between electronic structure, screening strength, and local-field effects, and how post-processing procedures can improve the quantitative comparison between experiment and theory. Finally, we provide perspectives on open challenges and potential developments in quantitative dielectric-function analyses.
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Materials Science

Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems

Shun Muroga, Hideaki Nakajima, Taiyo Shimizu et al. (+2 more)

Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a general strategy for data-driven, explainable characterization of complex materials.
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All authors Shun Muroga, Hideaki Nakajima, Taiyo Shimizu, Kazufumi Kobashi, Kenji Hata

Materials Science

Synergistic effect of the electronic band delocalization and bond anharmonicity on the thermoelectric performance of Cs2TeX6(X=Cl, Br, I)

Heena, Vineet Kumar Pandey, Saanvi Marethiya et al. (+3 more)

We investigate the structural, mechanical, and thermoelectric properties of lead-free double halide perovskites Cs2TeX6 (X = Cl, Br, I) using first-principles calculations and semiclassical Boltzmann transport theory. The HSE06 band gap is incorporated using the scissor correction method along with PBE calculated electronic band structures including spin orbit coupling to accurately predict transport properties. The band gap values are 3.27, 2.50, and 1.55 eV for Cs2TeX6 (X = Cl, Br, I), respectively. The coexistence of heavy and light bands in the Cs2TeI6 band structure helps mitigate the trade-off between the Seebeck coefficient and electrical conductivity. Among these systems, Cs2TeI6 exhibits superior performance with a ZT of 1.97 at 800 K and an electronic concentration of 3.35 x 10^19 cm^-3. Such a high ZT at relatively low carrier concentration arises from high electrical conductivity combined with low lattice thermal conductivity. The lattice thermal conductivity of Cs2TeI6 is found to be 0.41 W m^-1 K^-1 at room temperature. This low lattice thermal conductivity is attributed to weak Te-I bonding and non-uniform out-of-phase displacement of Cs atoms. The presence of local TeX6 units together with weak bonds strongly resists heat conduction, leading to significant suppression of lattice thermal conductivity. In particular, transverse acoustic phonons and optical phonons play a key role in limiting lattice thermal conductivity. These results identify Cs2TeI6 as a promising candidate for high performance thermoelectric applications.
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All authors Heena, Vineet Kumar Pandey, Saanvi Marethiya, Ambesh Dixit, Ajay Singh Verma, K.C. Bhamu

Materials Science

Upward band gap bowing and negative mixing enthalpy in multi-component cubic halide perovskite alloys

Xiuwen Zhang, Fernando P. Sabino, Jia-Xin Xiong et al. (+1 more)

Physical properties intermediate between constituents of alloys can be achieved as downward convex positive bowing, upward concave negative bowing, or zero bowing. Such bowing effects are essential for band gap engineering in semiconductor alloys. Upward band gap bowing effects are rather rare, hindering the exploration on half of the available physical property space of alloys. Part of the this being a rare event is related to the need to stabilize an alloy with low mixing enthalpy, so it does not phase separate. In this paper we find via density functional theory that one can satisfy the simultaneous conditions of negative mixing enthalpy and upward band gap bowing in four-component ABX3 halide perovskite alloys in the cubic perovskite structure. Such perovskite alloys have the B-site occupied by a mixture of group IVB and IIB elements that have the IVB-s and IIB-s states in the valence bands and conduction bands, respectively, leaving the delocalized s states strongly repel each other. This s-s repulsion leads to the upward band gap bowing and negative mixing enthalpies simultaneously. Remarkably, we identify a perovskite alloy that has a band gap much larger than all its components. Analogous trends of upward band gap bowing and negative mixing enthalpy also appear in the corresponding three-component and two-component ABX3 halide perovskite alloys. These observations of upward band gap bowing and negative mixing enthalpy will significantly accelerate the design of stable upward band gap bowing alloys in a broad range of material families.
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All authors Xiuwen Zhang, Fernando P. Sabino, Jia-Xin Xiong, Alex Zunger

Materials Science

In situ Gas-Cell Electron Microscopy Reveals Pressure-Selected Restructuring Pathways in AuRu Ammonia Catalysts

Amy S. McKeown-Green, Parivash Moradifar, Zisheng Zhang et al. (+7 more)

Bimetallic catalysts provide new routes toward sustainable ammonia synthesis, but the nanoscale structural dynamics under reaction-relevant conditions remain poorly understood. Here, we combine in situ gas-cell and multimodal electron microscopy to determine how temperature, gas pressure, and chemistry select among distinct restructuring pathways in AuRu nanocrystal catalysts. Initially, the AuRu nanocrystals form polycrystalline face-centered cubic (FCC) alloys with Au/Ru intermixing. Elevated temperature ($\geq 350~^\circ$C) induces intraparticle phase segregation into distinct Au-rich (FCC) and Ru-rich hexagonal close-packed (HCP) domains that exhibit localized plasmonic modes. Atmospheric-pressure 3:1 H$_2$:N$_2$ gas unlocks a distinct restructuring regime absent at lower pressures, characterized by pronounced faceting and nanovoid formation. Systematic gas variation identifies hydrogen as the dominant driver. Density functional theory-trained machine-learning interatomic potentials and grand-canonical Monte Carlo simulations reveal that H-Ru interactions enhance the Au/Ru diffusivity mismatch, promoting vacancy accumulation and nanovoid formation. Together, these results show that, rather than simply accelerating the thermally driven phase segregation observed at lower pressures, atmospheric-pressure H$_2$:N$_2$ gas redirects restructuring toward faceting and nanovoid formation through a gas-mediated Kirkendall-type mechanism.
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All authors Amy S. McKeown-Green, Parivash Moradifar, Zisheng Zhang, Cedric Lim, Andrew Barnum, Lin Yuan, Robert Sinclair, Frank Abild-Pedersen, Colin Ophus, Jennifer A. Dionne

Materials Science · Cross-listed from cs.LG

Open Materials Generation with Inference-Time Reinforcement Learning

Philipp Hoellmer and Stefano Martiniani

Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a policy-gradient RL framework that operates directly on the learned velocity fields and eliminates the need for the explicit computation of the score. OMatG-IRL leverages stochastic perturbations of the underlying generation dynamics preserving the baseline performance of the pretrained generative model while enabling exploration and policy-gradient estimation at inference time. Using OMatG-IRL, we present the first application of RL to crystal structure prediction (CSP). Our method enables effective reinforcement of an energy-based objective while preserving diversity through composition conditioning, and it achieves performance competitive with score-based RL approaches. Finally, we show that OMatG-IRL can learn time-dependent velocity-annealing schedules, enabling accurate CSP with order-of-magnitude improvements in sampling efficiency and, correspondingly, reduction in generation time. The OMatG-IRL code is included in a new release of the Open Materials Generation (OMatG) framework available at https://github.com/FERMat-ML/OMatG.
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Materials Science

Continuously tunable dipolar exciton geometry for controlling bosonic quantum phase transitions

Zhenyu Sun, Haoteng Sun, Xiaohang Jia et al. (+12 more)

The geometry and binding energy of excitons, set by electron-hole wavefunction distributions, are fundamental factors that underpin their many-body interactions and determine optoelectronic properties of semiconductors. However, in typical solid-state systems, these quantities are fixed by material composition and structure. Here we introduce a polarizable interlayer exciton hosted in a two-dimensional tetralayer heterostructure whose dipole length, in-plane radius, and binding energy can be continuously programmed in situ over a wide range, enabling direct control over the nature of excitonic many-body phase transitions. An out-of-plane electric field redistributes layer-hybridized electron-hole wavefunctions, realizing in situ control of exciton geometry through a strong quadratic Stark response. This tunability further regulates the nature of interaction-driven Mott transition, transforming it from gradual to abrupt. Our results establish exciton geometry as a continuously tunable materials parameter, opening routes to exciton-based quantum phase-transition simulators and guiding the design of emergent optoelectronic functionalities from programmable excitonic materials.
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All authors Zhenyu Sun, Haoteng Sun, Xiaohang Jia, An Li, Naiyuan J. Zhang, Ken Seungmin Hong, Joseph DePinho, Conor Y. Long, Kenji Watanabe, Takashi Taniguchi, Ou Chen, Jue Wang, Jia Li, Brenda Rubenstein, Yusong Bai

Friday 30 January

Materials Science

Interatomic potentials for platinum

R. K. Koju, Y. Li and Y. Mishin

We present two new interatomic potentials for platinum (Pt) in angular-dependent potential (ADP) and modified Tersoff (MT) formats. Both potentials have been trained on a reference database of first-principles calculations without using experimental data. The properties of Pt predicted by the ADP and MT potentials agree better with DFT calculations and experimental data than the potentials available in the literature. Future applications of the MT model to mixed-bonding metal-covalent systems are discussed.
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Materials Science · Cross-listed from quant-ph

A room-temperature cavity-magnonic source of correlated microwave pairs

Qiuyuan Wang, Aravind Karthigeyan, Chung-Tao Chou et al. (+1 more)

Correlated microwave photon sources are key enablers for technologies in quantum-limited sensing, signal amplification and communication, but the reliance on millikelvin operating temperature limits their scalability for broader applications. Here, at room temperature, we demonstrate strong correlated microwave signals emitted from a hybrid magnon-photon platform. Different from traditional parametrically induced magnons with degenerate frequencies, we achieve non-degenerate excitations by coupling magnon modes simultaneously with two cavity photon modes. Through the magnon-photon interactions in the corresponding linear and nonlinear regimes, one input microwave photon splits into a pair of magnon polaritons that possess distinct frequencies but maintain strong inter-mode correlations. The nonlinear magnon polariton dynamics empowered by this new parametric platform brings both verified true randomness and robust multi-channel correlations, from which we construct a microwave communication experiment for noise resilient signal transmission with added security. This work establishes cavity magnonics as a versatile and compact platform for generating correlated multi-mode microwave signals, opening new avenues for applications in classical and quantum domains.
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All authors Qiuyuan Wang, Aravind Karthigeyan, Chung-Tao Chou, Luqiao Liu

Materials Science

Defects, Corrugation and Temperature Govern Rarefied-Air Drag on Graphene Coatings

Samuel Cajahuaringa, Davide Bidoggia, Maria Peressi et al. (+1 more)

In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas-surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the $\alpha$-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction strengthens up to 900 K. While structural defects can increase TMAC via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains its performance at experimentally relevant defect densities.
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All authors Samuel Cajahuaringa, Davide Bidoggia, Maria Peressi, Antimo Marrazzo

Materials Science

A geometric basis for materials families in inorganic solids

Justin Tahmassebpur, Sarvesh Chaudhari, Cristóbal Méndez et al. (+6 more)

The thermodynamic stability of inorganic solids spans a vast compositional space, yet materials scientists have long organized their intuition around a manageable number of materials families. Here we show that this organization has a precise geometric basis. The formation-energy convex hull of all inorganic compounds from the Materials Project, spanning 92-dimensional elemental composition space, is captured to near DFT accuracy by a polyhedron with only seven facets. Each facet corresponds to a family of materials sharing similar chemical potentials. This low-dimensional structure is not merely an economical description of energies: without retraining or structural input, the same framework reproduces trends in DFT-calculated defect energies and elemental spatial correlations in high-entropy nanoparticles. These results reveal that a small number of material families, corresponding to geometric features of composition-energy space, govern bulk stability, defect energetics, and elemental mixing, and provide a unified, interpretable framework for rapid screening across diverse materials systems.
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All authors Justin Tahmassebpur, Sarvesh Chaudhari, Cristóbal Méndez, Rushil Choudhary, Sudipta Kundu, Raymond E. Schaak, Héctor Abruña, Peter Frazier, Tomás Arias

Materials Science

Gradient-expansion of the inhomogeneous electron-gas revisited

Mario Benites, Angel Rosado and Efstratios Manousakis

In the present work, we revisit the problem of the inhomogeneous electron gas under the influence of a weak external potential, which allows us to calculate the gradient corrections to the density functional within linear response, an approach known as the gradient expansion approximation. To obtain the exchange ($b_x$) and correlation ($b_{c}$) contributions to the coefficient $b_{xc}$, i.e., to the prefactor of the $q^2$ term of the proper-polarization function, we revisited all the previous calculations and expose misconceptions which led to incorrect conclusions. We used various ways to apply a necessary regularization to the singular Coulomb interaction potential. We found that the separate exchange ($b_x$) and correlation ($b_c$) contributions to the coefficient $b_{xc}$ have regularization-scheme dependent values even though the regulator is set to zero at the end of the calculation. This implies that it is impossible to define such a separation meaningfully. On the contrary, we found that when the regulator is set to zero at the end of the calculation, the combination $b_{xc}$ is regularization-scheme independent and, thus, has a unique value. We conclude that it is incorrect to separate those two terms when constructing a generalized-gradient-approximation (GGA) contribution to the density functional. This appears to be a common approach in most popular GGA functionals, where various constraints are applied to each contribution separately.
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Materials Science

QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

Fengxu Yang and Jack D. Evans

The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain constrained by rigid, carefully crafted domain-specific tool-calling paradigms and narrowly scoped agents. In this work, we introduce QUASAR, a universal autonomous system for atomistic simulation designed to facilitate production-grade scientific discovery. QUASAR autonomously orchestrates complex multi-scale workflows across diverse methods, including density functional theory, machine learning potentials, molecular dynamics, and Monte Carlo simulations. The system incorporates robust mechanisms for adaptive planning, context-efficient memory management, and hybrid knowledge retrieval to navigate real-world research scenarios without human intervention. We benchmark QUASAR against a series of three-tiered tasks, progressing from routine tasks to frontier research challenges such as photocatalyst screening and novel material assessment. These results suggest that QUASAR can function as a general atomistic reasoning system rather than a task-specific automation framework. They also provide initial evidence supporting the potential deployment of agentic AI as a component of computational chemistry research workflows, while identifying areas requiring further development.
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Materials Science · Cross-listed from cond-mat.mes-hall

In-situ Straining of Epitaxial Freestanding Ferroic Films by a MEMS Device

Simone Finizio, Tim A. Butcher, Maria Cocconcelli et al. (+10 more)

Mechanical strain can be used to control physical properties in materials. The experimental investigation of strain-induced effects at the nanoscale is of importance not only for its fundamental aspects, but also for the development of device applications. Transmission X-ray microscopy is a particularly well-suited technique for nanoscale imaging of magnetic materials, but its compatibility with in-situ mechanical straining of samples is limited. In this work, we present a setup for applying tailored in-situ mechanical strains to freestanding thin films by means of a micro electromechanical system (MEMS) actuator. We then present a proof-of-concept experiment in which a freestanding 80 nm thick (001) BiFeO3 multiferroic thin film is strained with the MEMS device, allowing us to control the coupled ferroelectric/spin cycloidal configuration.
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All authors Simone Finizio, Tim A. Butcher, Maria Cocconcelli, Elisabeth Müller, Lauren J. Riddiford, Jeffrey A. Brock, Chia-Chun Wei, Li-Shu Wang, Jan-Chi Yang, Shih-Wen Huang, Federico Maspero, Riccardo Bertacco, Jörg Raabe

Materials Science · Cross-listed from astro-ph.EP

Direct simulations of H-He mixtures at planetary interior conditions: demixing, insulator-metal transition and miscibility boundaries

Valentin V. Karasiev, S. X. Hu, Joshua P. Hinz et al. (+4 more)

Accurate knowledge of the electrical and thermal conductivities and structural properties of hydrogen-helium mixtures under thermodynamic conditions within and beyond the immiscibility range is very important to predict the thermal evolution and internal structure of gas giant planets like Jupiter and Saturn. Here, we propose a novel method to determine the immiscibility boundary accurately without the need for free energy calculations, while providing consistent insights into structural and transport properties of mixtures. We show with direct large-scale ab initio simulations that the insulator-metal transition (IMT) of the hydrogen subsystem is strongly affected by an admixture with a small fraction of helium and occurs at temperatures significantly higher than those of pure hydrogen. At pressures below 150 GPa, the IMT boundary is not related anymore to the H2 subsystem dissociation, the system remains insulating even after the full dissociation of H2 molecules and its transition to an H-He mixture. The offset of the IMT in the H-He mixture relative to the dissociation region in the hydrogen subsystem and the significant reduction of static electrical and thermal conductivity by a factor between two and a few thousand relative to pure hydrogen found in mixtures have consequences for Jupiter and Saturn's thermal evolution, internal structure, and dynamo action, affecting a large fraction of the interior of both planets.
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All authors Valentin V. Karasiev, S. X. Hu, Joshua P. Hinz, R. M. N. Goshadze, Shuai Zhang, Armin Bergermann, Ronald Redmer

Materials Science

Rotating Magnetocaloric Effect in Sintered La(Fe,Mn,Si)$_{13}$H$_z$ Plates

Rafael Almeida, Tomás Ventura, Ricardo Moura Costa Pinto et al. (+6 more)

La-Fe-Si-based alloys are among the most application-ready magnetocaloric materials for room-temperature magnetic refrigeration. Powder metallurgy methods have been previously demonstrated to successfully produce structures with sub-mm features for magnetic refrigerators in a scalable method. In this work, we explore the rotating magnetocaloric effect (RMCE) present in a 0.27 mm thin plate of sintered and hydrogenated La(Fe,Mn,Si)$_{13}$. The high aspect ratio ($\sim$50) of the thin plate leads to an anisotropic magnetocaloric effect (MCE), dependent on the relative orientation of the external magnetic field, and an RMCE when the external field is rotated. We find a maximum rotating adiabatic temperature change ($\Delta T_{ad}^{rot}$) of 1.17 K with the rotation of a 1 T magnetic field and 1.12 K when rotating a 0.6 T magnetic field, a reduction of only 4% for a 40% reduction in applied field strength. Magnetostatic computations revealed a considerable rotating isothermal entropy change ($\Delta S_{iso}^{rot}$), comparable to the conventional MCE of Gd for similar fields, reaching 3.97 J K$^{-1}$ kg$^{-1}$ for 1 T and 3.68 J K$^{-1}$ kg$^{-1}$ for 0.6 T (7% reduction), highlighting La-Fe-Mn-Si alloys as high potential candidates for a magnetic refrigerator based on the RMCE utilizing relatively low external magnetic field amplitudes, such as 0.6 T.
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All authors Rafael Almeida, Tomás Ventura, Ricardo Moura Costa Pinto, João Oliveira Silva, Konrad Loewe, Rodrigo Kiefe, João Sequeira Amaral, João Pedro Araújo, João Horta Belo

Materials Science · Cross-listed from cond-mat.supr-con

Theory of Little-Parks oscillations by vortices in two-dimensional superconductors

Ying-Ming Xie and Naoto Nagaosa

The Little-Parks (LP) effect is a quantum phenomenon in which the superconducting transition temperature of a superconducting cylinder (or ring) oscillates periodically as a function of the magnetic flux threading the loop. Recently, multiple experiments have observed half-quantum flux shifts in measurements of LP oscillations, where the oscillations are globally shifted by half a flux quantum compared to conventional cases, a behavior referred to as a $\pi$-ring. Such observations are commonly linked to unconventional pairing symmetries. In this work, we demonstrate that half-quantum flux shifts can arise in two-dimensional (2D) superconducting rings without invoking unconventional pairing symmetry, provided that vortices near the Berezinskii-Kosterlitz-Thouless (BKT) transition are taken into account. Specifically, based on the vortex-charge duality theory near the BKT transition, we map the problem onto a Coulomb gas model, in which the magnetic flux is represented as a pair of opposite boundary charges (or vortices) at the two edges. The screening of these boundary charges by thermally excited vortex-antivortex pairs is investigated through explicit Monte Carlo simulations. Importantly, we demonstrate that the oscillation of the free-vortex density as a function of magnetic flux can exhibit an anomalous half-quantum flux shift. Our work thus predicts the LP oscillations induced by vortices in 2D superconducting rings near the BKT transition, which provides a new mechanism for generating $\pi$-rings.
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