Materials Science · Cross-listed from physics.ins-det
Multi-scale reconstruction of single-ion damage tracks in diamond via nitrogen-vacancy centers
Daniel G. Ang, Jiashen Tang, Maximilian Shen et al. (+9 more)
Understanding particle-induced damage tracks in solid-state materials underpins emerging applications in rare-event detection and
quantum defect engineering. Resolving these tracks requires multi-scale readout, from event localization at the millimeter scale to track-morphology reconstruction at the nanoscale. Nitrogen-vacancy (NV) centers in diamond provide such a platform, combining optical localization with
quantum sensing of track morphology. Here, we implant sub-MeV carbon ions into nitrogen-rich diamond and detect individual
recoil events via spatially localized NV formation. We develop a simulation framework that explains the observed NV yield and predicts that directional information is retained in the NV distribution after annealing. Machine learning further recovers much of the information lost to defect
diffusion and limited NV yield, improving head-tail classification to a level comparable to pre-annealed vacancy tracks. Measurements of NV
spin coherence indicate compatibility with nanoscale track reconstruction via NV strain mapping and
magnetic gradient-based techniques. These results identify promising pathways toward NV-diamond directional detectors for rare events, while the track-modeling framework has broader implications for paleodetection and
quantum material synthesis.
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All authors Daniel G. Ang, Jiashen Tang, Maximilian Shen, Michael Titze, Gavishta Liyanage, Jordan Chapman, Chinmay Bharathulwar, Andrew T. Gilpin, Tanguy Terlier, Edward S. Bielejec, Vsevolod Ivanov, Ronald L. Walsworth
Materials Science
Data analysis methods for powder x-ray diffraction intensity under laser-driven dynamic compression at Omega and NIF laser facilities
Marius Millot, Federica Coppari, Amy Lazicki et al. (+1 more)
Powder x-ray diffraction (PXRD) under
laser-driven dynamic compression is a powerful tool to investigate material response to extreme pressure, temperature and strain rates. Robust PXRD platforms have been developed at kJ and MJ
laser facilities worldwide including the Powder X-Ray Diffraction Image Plate (PXRDIP) at the Omega
Laser Facility at the Laboratory for
Laser Energetics (LLE) and the TARget Diffraction In Situ (TARDIS) at the National Ignition Facility (NIF). Here we present further developments of data analysis methods focused towards improving the fidelity of the PXRD intensity determination for these platforms. We illustrate these methods by discussing how they can be implemented in a data analysis package and applied to shock compression data on diamond near 1 TPa. We discuss using the XRD signal from the collimating pinhole or a layer of un-compressed material in the sample package as in-situ references for XRD intensity. We detail how to compare data collected with different x-ray sources and how to account for thermal damping of XRD signal when comparing XRD from a shock-compressed, hot material with the reference material at ambient.
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All authors Marius Millot, Federica Coppari, Amy Lazicki, Jon H. Eggert
Materials Science · Cross-listed from cond-mat.mes-hall
Helical Domain-Wall-Ring Networks Reshape Superconducting Correlations
Yi-Chun Hung and Arun Bansil
Extended domain-wall networks that emerge in moiré materials provide a distinct platform for quasi-one-dimensional electronic states. However, the interaction-driven orders in confined networks remain largely unexplored. Here, we discuss superconducting (SC) correlations in interacting helical domain-wall-ring networks in the closed topological domains formed within the moiré patterns of an underlying twisted
bilayer honeycomb
lattice. We first analyze the system within the framework of an infinite-size theory and show that inter-ring SC-pair tunneling is renormalization-group relevant and thus enhances SC correlations through inter-ring phase locking. To address finite-size effects resulting from the ring-network geometry, we present a self-consistent variational approach. Our analysis shows that even in the regime where the infinite-size theory predicts strongly coupled pair tunneling, the induced phase-locking scale remains strongly suppressed. In contrast, the SC scaling dimension continues to decrease with stronger inter-ring density-density interaction and a decreasing twist angle, while remaining insensitive to the pair-tunneling strength. This discrepancy demonstrates that ring networks do not simply approach their infinite-size counterparts but can exhibit qualitatively distinct collective behavior. Our study highlights how the interplay of confinement effects and ring-network geometry can reshape SC correlations.
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Materials Science · Cross-listed from cond-mat.mes-hall
Quantum Geometry Driven Finite-Momentum Exciton Fluctuations in Flat-Band Systems
Yi-Chun Hung, Xiaoting Zhou and Arun Bansil
Quantum geometry is instrumental in stabilizing exotic phenomena in systems ranging from topological
insulators to
superconductors. In dispersionless flat bands, where the kinetic energy is quenched, the
quantum metric emerges as the fundamental driver of macroscopic collective phenomena. Here, we theoretically demonstrate that
lattice-geometry-induced flat bands, such as those in kagome and Lieb
lattices, provide a fertile platform for realizing a purely
quantum-geometry-driven excitonic
insulator (EI) phase. By applying an out-of-plane Zeeman field to lift
spin degeneracy without
spin-orbit coupling, we establish a Ginzburg-Landau framework in which the electron-hole wavefunction-overlap directly maps the flat-band
quantum metric onto the macroscopic free energy. This mapping plays a key role in both the EI and the associated superfluid phases, with the coherence length and phase stiffness emerging directly from the
quantum metric. Our analysis reveals that under strong interactions, the
quantum metric induces a negative effective kinetic coefficient for the amplitude mode. Rather than destabilizing the uniform condensate, this softens the amplitude fluctuations at a finite momentum, giving rise to a finite-momentum superfluid density fluctuation (FMSDF) state. This state is observable as a periodically modulated magnitude of in-plane magnetization fluctuations. Our findings establish a rigorous link between flat-band
quantum geometry and dynamic collective excitonic states, with promising pathways for realization in covalent-organic frameworks (COFs).
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Materials Science
INCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models
Bin Shao, Jixiang Li, Xinyue Zhang et al. (+3 more)
Large
language models (LLMs) are increasingly being integrated into first-principles computational workflows, yet their ability to configure scientific calculations remains poorly understood. Here, we introduce INCARBench, a
benchmark for evaluating LLMs on input configuration for the Vienna Ab initio Simulation Package (VASP) through both configuration generation and repair tasks. Evaluating 19 model configurations reveals substantial capability differences among current frontier models. While several models achieve high semantic and policy accuracy, task-critical correctness remains substantially lower, demonstrating that parameter-level correctness does not necessarily imply scientifically valid configurations. Failure analysis shows that errors concentrate in physically coupled settings involving DFT+$U$, magnetism, and correlated materials, where multiple constraints must be satisfied simultaneously. Repair evaluation further reveals that correcting incorrect settings and preserving already-valid configurations are distinct capabilities, with configuration preservation remaining a major challenge. These findings establish scientific configuration as a measurable capability of large
language models and provide a foundation for developing more reliable AI systems for computational materials science.
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All authors Bin Shao, Jixiang Li, Xinyue Zhang, Baishun Yang, Zhiyang Liu, Weichao Wang
Materials Science
Atomic-scale theory of robust out-of-plane ferroelectricity in ultrathin films
Fengbo Yuan, Yujia Teng, Karin M. Rabe et al. (+1 more)
Ferroelectricity in ultrathin films, characterized by robust switchable out-of-plane polarization, is key to next-generation nanoelectronics. Although the macroscopic theory of ferroelectricity suggests that ferroelectricity is inevitably suppressed as the film thickness decreases, recent studies have demonstrated robust ultra thin-film ferroelectricity, for certain ferroelectric materials, specifically HfO$_2$-based
oxides and bismuth-based
oxides. In this work, we develop an atomic-scale theoretical framework for understanding ferroelectricity in this limiting regime. By considering the work function of the termination layers of the film, we find that robust ferroelectricity arises from “self-polarizing” and “switchable role of the termination layer” effects strongly correlated to the “characteristic structure.” This theory also provides further insights on the importance of top electrodes in stabilizing ferroelectricity for this class of materials in the ultrathin limit. This work aims to develop a comprehensive theoretical framework for thin-film ferroelectricity, providing fundamental insights that can guide the design of next-generation nanoscale devices.
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All authors Fengbo Yuan, Yujia Teng, Karin M. Rabe, Yubo Qi
Materials Science
High-quality single crystals of the kagome metals Ni$_3$In and Ni$_3$Sn grown from Pb flux
Fabian Garmroudi, Jennifer Coulter, Caitlin S.T. Kengle et al. (+4 more)
The
bilayer kagome metal Ni$_3$In has recently attracted
attention due to the presence of a flat band located near the
Fermi level, which has been associated with unconventional thermodynamic and electronic transport properties [Ye et al., Nat. Phys. 20, 610-614 (2024)]. However, further investigation of the intrinsic properties of this system has been hindered by the lack of large, high-quality single
crystals. Here, we report the successful growth of Ni$_3$(In, Sn) single
crystals using a Pb-flux technique. By optimizing the growth conditions, competing binary phases can be effectively suppressed, enabling the synthesis of single
crystals with dimensions reaching several millimeters. We compare the physical properties of our Pb-flux-grown
crystals to previously reported samples prepared by iodine-assisted chemical vapor transport and molecular beam epitaxy as well as to first-principles resistivity calculations. We find a significantly lower electrical resistivity in our
crystals, in excellent agreement with calculations of resistivity from electron-
phonon scattering, a sizeable non-saturating magnetoresistance, and a reduced Sommerfeld coefficient and
magnetic susceptibility compared to previous experimental findings, which are likely related to differences in the
Fermi level position. Our results establish Pb-flux growth as a reliable route for obtaining large single
crystals of the
bilayer kagome metals Ni$_3$(In, Sn) that are suitable for further thermodynamic and spectroscopic investigations of their intrinsic electronic and
magnetic properties.
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All authors Fabian Garmroudi, Jennifer Coulter, Caitlin S.T. Kengle, Joe D. Thompson, Eric D. Bauer, Sean M. Thomas, Priscila F.S. Rosa
Materials Science · Cross-listed from physics.optics
Hexagonal Boron Nitride Spin Defects for Quantum Photonics: Annealing-Free Generation by Krypton Ion Implantation
Ikshvaku Shyam, Raj Singh, Mangababu Akkanaboina et al. (+11 more)
Controlled, reproducible generation of luminescent defect centres in hBN remains a key challenge for scalable
quantum-photonic technologies. Here, we report Kr$^{+}$ ion implantation as a tunable, annealing-free, and chemically inert route to room-temperature near-infrared luminescent
spin defects in hBN, requiring no pre- or post-implantation annealing. SRIM Monte Carlo simulations were used to optimise the parameters for 40 keV Kr$^{+}$ irradiation of hBN flakes. The implanted samples exhibit a stable near-infrared photoluminescence (PL) band centred at $\sim$830 nm whose intensity increases with implantation fluence over $10^{11}$-$10^{15}$ions/cm$^{2}$. Temperature-dependent PL measurements (20-300 K) reveal a linewidth broadening well described by a $T^{3}$ dependence, consistent with acoustic-
phonon-mediated dephasing. Raman spectra show the characteristic $E_{2g}$ mode of pristine hBN at $\sim$1366 cm$^{-1}$ alongside an implantation-induced defect feature at $\sim$1295 cm$^{-1}$, confirming irradiation-induced
lattice disorder. Electron paramagnetic resonance (EPR) measurements reveal a paramagnetic centre with a $g$-factor of 2.003, and density functional theory (DFT) calculations indicate that a spatially separated $V_{\mathrm{N}}$-$C_{\mathrm{B}}$ donor-acceptor pair complex is a viable origin of the observed optical and
magnetic signatures. Overall, Kr$^{+}$ implantation offers an effective, annealing-free, and scalable platform for generating stable room-temperature luminescent defects, providing a promising route toward
quantum photonics.
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All authors Ikshvaku Shyam, Raj Singh, Mangababu Akkanaboina, A. M. Sonawane, Muthu Satheeshkumar, Amrita Majumder, Ekta, Janhavi Jayawant Khunte, Akash Khaire, Kenji Watanabe, Takashi Taniguchi, Gopalan Rajaraman, S. S. Dahiwale, Anshuman Kumar
Materials Science
Step-Edge Passivation and Quantitative Raman Mapping of Transfer Quality in Aligned Graphene Nanoribbons
Dominik Lüthi, Rimah Darawish, Klaus Müllen et al. (+2 more)
The transfer of aligned
graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair
graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing-like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative
benchmark for the systematic optimization of GNR transfer strategies.
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All authors Dominik Lüthi, Rimah Darawish, Klaus Müllen, Roman Fasel, Gabriela Borin Barin
Materials Science
Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models
Jonas Hänseroth, Aaron Flötotto and Christian Dreßler
Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling when reactive events are involved remains unsettled. We compare five universal MLIPs across seven chemically diverse systems and find that strong performance on standard
benchmarks does not guarantee accurate predictions of target observables. In particular, zero-shot models do not reliably reproduce reactive, transport, or high-barrier processes, exemplified here in particular by the sulfur-vacancy jump in MoS$_2$. We therefore propose a practical alternative: universal MLIPs are used to generate long molecular dynamics trajectories, the resulting configurations are sub-sampled and relabeled with DFT, and material-specific MLIPs are subsequently trained or fine-tuned on the resulting first-principles
datasets. This workflow converts universal models into efficient configuration-space generators while retaining ab initio reference labels for training. Across the tested systems, $2{,}000$ DFT-recalculated structures are often sufficient to obtain accurate fine-tuned or trained-from-scratch models. For the most challenging case, iterative self-training progressively refines the sampled configuration space and recovers the DFT MoS$_2$ potential energy profile with only $600$ first-principles calculations in total. The resulting workflow enables the generation of $1$ ns ab initio-quality trajectories - including training data generation and model creation - within three days.
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Materials Science
Hierarchical Unsupervised Clustering of Nanobeam Electron Diffraction Data Using Sparse Bragg-Vector Representations
Ian MacLaren, Ala Al-Afeef, Trevor Almeida et al. (+5 more)
If disk detection is applied to nanobeam electron diffraction
datasets, then the results are effectively a list of vectors describing the position of every diffraction peak in real and reciprocal space. This is the natural territory for the application of clustering
algorithms, and they are shown to be highly effective at decomposing such
datasets and automating imaging and analysis. In particular, this is found to work particularly well in a hierarchical clustering approach, where a first level is concerned with finding clusters of similar diffraction vectors and a second level with grouping those diffraction vector clusters that originate from the same real space area (i.e.
crystal or crystallographic variant). Examples are shown in both polycrystalline and single
crystal (with precipitates) systems. Additionally, automated separation of amorphous or deeply nanocrystalline components is also found to be possible in an optional third level of clustering, allowing composite images of both amorphous and crystalline components in partially crystallised samples to be easily and automatically generated. These advances promise to increase throughput in atomic structure analysis with nanobeam diffraction, and also make finding minor components much easier. They can also serve as a preliminary step towards more detailed crystallographic or
crystal size/shape distribution analysis.
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All authors Ian MacLaren, Ala Al-Afeef, Trevor Almeida, Rantej Bali, Shriyar Tariq, Emily Wackan, Luke Daly, Joshua F. Einsle
Materials Science · Cross-listed from cs.LG
Substitution-Based Analysis of Structural Novelty for Generative Models of Materials
Masahiro Negishi and Aron Walsh
There has been rapid progress in generative artificial intelligence (AI) models for inorganic
crystal design, which can efficiently generate large numbers of candidate compounds after being trained on databases of known
crystals. However, it remains unclear whether they genuinely expand the accessible materials search space beyond conventional strategies such as elemental substitution within known structure types. We address this question by developing a workflow to assess whether AI-generated
crystals are duplicates of training structures, reproducible by elemental substitution, or unmatched by either criterion. Applying this workflow to representative generative models reveals that 81-92% of chemically valid and metastable generated
crystals are either training duplicates or substitution-derived structures. This tendency is particularly strong in high-
symmetry crystal systems, even though many possible structural prototypes remain unexplored. Further analysis of the underlying structural fingerprints shows that low-
symmetry structures beyond duplication or substitution can be interpreted as interpolation in training-data-rich regions, while high-
symmetry duplicates appear to result from memorisation in training-sparse regions. Our findings highlight a limitation in the current generation of models that exhibit a bias towards known structural prototypes in the high
symmetry regions, but enable wider exploration of the low-
symmetry structural space.
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Materials Science
Toward in-situ/operando X-ray absorption spectroscopy and electrochemical characterization of solid oxide fuel cells
Renato A. N. de Oliveira, Rafael Galiza Yoshimura, Liliana Mogni et al. (+15 more)
The focus of the present work is the development of specialized experimental instrumentation compatible with synchrotron characterization for in-situ and operando symmetric intermediate temperature solid
oxide fuel
cells (IT-SOFC) studies at maximum temperatures of 800 C , exposed to reducing and oxidizing atmospheres, using fluorescence X-ray absorption spectroscopy (XAS) measurements in combination with electrochemical impedance spectroscopy (EIS) in the multipurpose Quati beamline at CNPEM/SIRIUS synchrotron facility [1]. Symmetric IT-SOFC are gaining importance due to their structural simplicity, as they allow for the use of identical materials on both sides of the fuel
cell electrolyte; the anode, and the cathode [ 2,3 ]. The symmetric configuration opens new opportunities for fundamental research of electrode materials and improves the versatility of SOFC electrochemical devices [2,3].
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All authors Renato A. N. de Oliveira, Rafael Galiza Yoshimura, Liliana Mogni, Maurico Arce, Diego G. Lamas, Lucia Toscani, Maria Belén Arcentales Vera, Esteban Elvis Asto Ramos, Magna Monteiro Schaerer, Celeste Z.A. Aquino, Marcia Carvalho de Abreu Fantini, Taofeeq Oladayo Bello, Tereza da Silva Martins, Danilo Waismann Losito, James Moraes de Almeida, Valeria Spolon Marangoni, Leopoldo Suescunand, Santiago Figueroa
Materials Science
Unconventional topological Hall response and anisotropic magnetotransport properties of a helical magnet EuAuAs
Anyesh Saraswati, Koyendrila Debnath, Shubhankar Roy et al. (+3 more)
Topological magnets with nontrivial
spin texture have attracted considerable interest because they display a rich spectrum of emergent
quantum phenomena. Here, we present a combined experimental and theoretical investigation of the
magnetic and magnetotransport properties of EuAuAs, an antiferromagnet with Néel temperature ($T_\mathrm{N}$) $\sim$ 6 K. The temperature and
magnetic field dependence of electrical resistivity and magnetization demonstrate that the charge transport in EuAuAs is strongly influenced by the
spin configuration of local Eu moments. Below $T_\mathrm{N}$, both longitudinal magnetoresistance (LMR) and transverse magnetoresistance (TMR) are positive at low fields but large and negative at high fields. With increasing temperature, TMR becomes positive above 60 K, whereas LMR remains negative up to 100 K. The low-field positive LMR and TMR originate from weak antilocalization (WAL). The WAL contribution in TMR is well captured by the Hikami-Larkin-Nagaoka model, whereas the LMR data are described by a generalized Altshuler-Aronov framework. Moreover, we observe a
giant topological
Hall effect arising from the scalar
spin chirality, which is further supported by the helical
magnetic structure obtained from the ab-initio calculations. The observed anisotropy in longitudinal resistivity and magnetoresistance underscores the very nature of the
Fermi surface of the EuAuAs, as confirmed by first-principles calculations. These results establish EuAuAs as a unique platform for exploring the interplay between electronic structure and noncoplanar
spin texture in a centrosymmetric helical magnet.
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All authors Anyesh Saraswati, Koyendrila Debnath, Shubhankar Roy, Barun Ghosh, Nitesh Kumar, Prabhat Mandal
Materials Science
Reduced-Order Modelling of Defect Transport using Surrogate Kinetics: Application to U$_x$Pu$_ {1-x}$N
Peter Hatton
Defect transport in chemically disordered materials is a difficult phenomenon to model since migration energetics depend strongly on the local chemical environment, producing a distribution of transition barriers that cannot be exhaustively enumerated. Here, we develop a reduced-order approach to modelling defect
diffusion in compositionally complex materials using environment-dependent surrogate kinetics. Migration energetics for actinide vacancies, nitride vacancies, and actinide-nitride divacancies in U$_x$Pu$_{1-x}$N are generated using the Hop-Decorate workflow across thousands of chemical configurations. These data are distilled into compact surrogate functions that predict migration barriers and energy differences from simple descriptors based on local coordination counts. The surrogate models reproduce the atomistic
dataset with low error and enable efficient evaluation of migration rates during
lattice kinetic Monte Carlo simulations. Long-time diffusivities computed across temperature and composition reveal strongly non-linear behaviour arising from two dominant mechanisms: species-controlled migration on the actinide sublattice and environment-dependent trapping on the nitride sublattice. Although demonstrated for U$_x$Pu$_{1-x}$N the framework provides a general and computationally efficient approach for modelling defect transport in chemically disordered materials and for integrating atomistic kinetics into higher-scale simulations.
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Materials Science · Cross-listed from physics.app-ph
Physics-governed executable modelling of triboelectric nanogenerators
Hongfa Zhao, Baiqiao Wang, Tiancong Zhao et al. (+7 more)
Predictive modelling of triboelectric nanogenerators (TENGs) remains fragmented across analytical theories, finite-geometry solvers and disconnected simulation workflows. These disparate approaches must be unified into an executable framework to advance quantitative TENG research.Here we introduce a charge-defined modelling framework and implement it as TENG-CLAW, a physics-governed platform for traceable TENG simulation. The framework establishes a self-consistent electrostatic hierarchy in which triboelectric charges, pre-charging charges and compensating electrode charges serve as defining state variables.This hierarchy connects the infinite plate analytical limit for near-uniform fields with finite-geometry numerical formulations required for edge-dominated devices. Built on this basis, TENG-CLAW converts user-defined research requests into physically admissible simulation tasks, so that generated outputs are tied to explicit charge states, boundary conditions, solver routes and reusable artifacts across spatial, temporal, field-level, comparative and reporting workflows. This work establishes a rigorous computational basis for interpreting TENG mechanisms and provides reproducible research infrastructure for simulation and physics-guided device design.
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All authors Hongfa Zhao, Baiqiao Wang, Tiancong Zhao, Chun Jin, Hanlin Zhou, Mingrui Shu, Minyi Xu, Liwei Lin, Wenbo Ding, Zhong Lin Wang
Materials Science · Cross-listed from physics.acc-ph
Powder Spreading and Layer Deposition in Metal Powder Bed Fusion
Antonello Astarita
Powder spreading and layer deposition are fundamental stages of Powder Bed Fusion (PBF) technologies and play a critical role in determining process stability and final component quality. This chapter examines the mechanisms governing powder-bed formation, highlighting the interactions between powder characteristics, process parameters, and machine architecture. Particular
attention is devoted to the influence of particle size distribution, morphology, cohesion, flowability, layer thickness, recoater velocity, and environmental conditions on powder-bed quality. The resulting powder-bed is discussed as a process state variable whose characteristics, including packing density, surface coverage, effective layer thickness, and spatial homogeneity, directly affect energy absorption, melt-pool stability, defect formation, and mechanical performance. The chapter also reviews the application of the Discrete Element Method (DEM) for modelling powder spreading phenomena and quantifying powder-bed quality metrics. Finally, the role of powder reuse, lifecycle management, and future developments involving process monitoring, digital twins, and data-driven optimization strategies is discussed, emphasizing the growing importance of powder engineering in advanced metal additive manufacturing.
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Materials Science · Cross-listed from physics.optics
Large-Area Patternable Solar-Powered Bistable Organic Crystalline Film for Nonlinear Optical Communication
Ankur Khapre, Avulu Vinod Kumar, Haseeba Nasreen Punathil et al. (+5 more)
Reversible control of
crystal symmetry offers a powerful route to programmable optical functionality. However, achieving solid-state bistability between centrosymmetric and non-centrosymmetric crystalline phases remains a formidable challenge; examples of materials that enable stable switching of second-order nonlinear optical (NLO) responses are exceptionally rare. Here we report a
solar-powered,
symmetry-bistable organic material based on the photoisomerizable molecule (E/Z)-2-(4-(4-bromophenyl)thiazol-2-yl)-3-(4- (dimethylamino)phenyl)acrylonitrile (E/Z-BTDPA). The crystallizable E- and Z-isomers adopt distinct molecular packing arrangements that reversibly toggle between these states, controlling second-order NLO activity. The E-form exhibits strong second-harmonic generation (SHG), whereas the Z-form is SHG-inactive and displays twophoton luminescence. This bistable behavior is retained in flexible thin films, where sunlight-driven photoisomerization enables reversible photoswitching of the second-order electric susceptibility (ḩi} 2), large-area optical patterning, and real-time NLO communication via waveform generation and text-string transcription at telecommunication wavelengths. This sustainable strategy bypasses rigid inorganic architectures, establishing photoinduced
symmetry bistability as a scalable paradigm for all-optical computing and advanced communication networks.
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All authors Ankur Khapre, Avulu Vinod Kumar, Haseeba Nasreen Punathil, Biswajit Kumar Barman, Bedanta Kumar Deka, Shilpa Mangalassery, Sri Ram G Naraharishetty, Rajadurai Chandrasekar
Materials Science · Cross-listed from physics.chem-ph
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration
Ruili Li, Rui Qi, Shuoqi Zhang et al. (+5 more)
Catalyst discovery for ammonia synthesis is inherently a reaction-network challenge because catalytic performance is governed not by a single adsorbed intermediate, but by a surface's orchestrated compatibility with multiple distinct intermediates across competing dissociative and associative pathways. However, navigating ultra-large chemical spaces under such multi-intermediate constraints remains a formidable bottleneck for conventional screening workflows. Here, we report a reaction-network-level catalyst discovery framework driven by ten-million-scale generative exploration. By coupling adsorbate-specific generative
Transformers with high-throughput machine learning potentials, we systematically map the structure-property landscapes of four critical intermediates (N*, NH*, NNH*, and HNNH*). Scale-dependent overlap analysis shows that the full four-intermediate compatibility space remains strongly under-sampled at conventional 105-106 generative scales, emerging exclusively under ten-million-scale exploration. By generating approximately 15 million configurations per adsorbate, followed by structural compression and machine-learning-potential predictions, we identified 279 highly potential target materials. This sparse compatibility space successfully recovers traditional Fe- and Ru-based motifs while uncovering previously unexplored catalyst families. Representative DFT calculations validate pathway-dependent mechanisms: Fe-V emerges as a dissociative-pathway lead by significantly lowering the initial N2 dissociation barrier, whereas Al-Pd-Zr efficiently stabilizes associative intermediates as an associative-pathway lead. These findings establish multi-intermediate reaction-network compatibility as a robust criterion for discovering advanced catalysts from multi-million generative chemical spaces.
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All authors Ruili Li, Rui Qi, Shuoqi Zhang, Qingli Tang, Qingqing Mao, Ritankar Das, Beien Zhu, Yi Gao
Materials Science
Coherent seeding and control of dynamical ferroelectricity by phonon anharmonicity
Junhan Huang, Yongkang Ju, Xinbo Wang et al. (+12 more)
Optical control of
quantum materials has progressed along two separate directions: creating non-
equilibrium states inaccessible at
equilibrium, and coherently controlling ultrafast dynamics with multi-pulse protocols. Ferroelectricity is especially attractive in this context because its order parameter, macroscopic polarization, directly links inversion-
symmetry breaking to functional response. Yet light-induced ferroelectricity has so far been confined to
quantum paraelectrics near the ferroelectric instability, where critical fluctuations obscure the formation of a homogeneous ferroelectric state and complicate its deterministic coherent control. Unifying these capabilities – preparing a
symmetry-broken state and then coherently steering its functionality – remains a central challenge. Here we show that intense terahertz excitation of a soft
phonon mode induces a ferroelectric state in centrosymmetric PbTe, a thermoelectric material with strong
lattice anharmonicity but no ferroelectric transition at finite temperature. The light-induced
symmetry-broken state can be realized up to about 100 K, without relying on local dipolar fluctuations. Experiment and theory together reveal that terahertz-driven anharmonic coupling between degenerate transverse optical
phonons underlies this ferroelectric induction. Furthermore, we demonstrate coherent amplification and suppression of the induced polarization via a double-pulse-excitation protocol. These results establish terahertz-driven anharmonic mode coupling as a general strategy for controlling mode-mediated functionalities in
quantum materials, opening a route to ultrafast information processing.
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All authors Junhan Huang, Yongkang Ju, Xinbo Wang, Li Yue, Hao Wang, Qiaomei Liu, Tianchen Hu, Yuchen Cui, Liyu Shi, Shangfei Wu, Sijie Zhang, Dong Wu, Peizhe Tang, Tao Dong, Nan-Lin Wang
Materials Science
Interfacial melting as a thermodynamic indicator of solid-state synthesizability
Zihan Zhang, Mengyi Chen, Qianxiao Li et al. (+1 more)
Computational materials discovery commonly ranks candidate materials by their thermodynamic stability on the formation energy convex hull, yet many predicted-stable phases resist synthesis. We propose that solid-state synthesizability through interfacial-melt-mediated routes requires an additional thermodynamic condition: the interfacial melt at the target composition must itself remain locally stable against spinodal decomposition. We examine this in the classical Fe–B system, where thermodynamically stable FeB$_4$ has been reported under high-pressure synthesis but not in low-pressure synthesis attempts. Using melt–quench molecular dynamics driven by a fine-tuned machine-learning interatomic potential, we find that, at ambient pressure, the B-rich interfacial melt near the FeB$_4$ composition develops a concave free-energy landscape, signaling a demixing instability that is corroborated by the concentration–concentration structure factor and correlated with low-energy icosahedral and pentagonal-pyramidal boron motifs. In contrast to FeB$_4$, metastable Fe$_3$B and Fe$_{23}$B$_6$ remain synthesizable because their corresponding melts are stable. Applied pressure introduces a convex $PV$ contribution that strongly suppresses this instability, reducing the
curvature at the FeB$_4$ composition to within the uncertainty of our fit at 1800~K, consistent with the experimental synthesis boundary. Comparison with CrB$_4$ further shows that weaker melt instability correlates with easier experimental synthesis. Interfacial-melt stability, which atomistic simulations can assess via the low-$k$ concentration–concentration structure factor, is thus proposed as a practical thermodynamic screening descriptor of synthesizability for AI-assisted materials discovery.
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All authors Zihan Zhang, Mengyi Chen, Qianxiao Li, Peichen Zhong
Materials Science
Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products
Chen Shen, Yang Su, Maciej P. Polak et al. (+5 more)
Silicon carbide is a leading candidate material for advanced
nuclear energy systems, but irradiation-induced defects and transmutation products can severely degrade its thermal conductivity. In fusion environments, Mg is predicted to be a major solid transmutant in SiC, yet it is not well understood how different Mg-related defects affect
phonon transport. Here, we develop a machine-learning interatomic potential, MLIP4SiC-Mg, for 3C-SiC containing intrinsic point defects, Mg-related defects, and Mg-defect complexes. The potential is trained on a large DFT
dataset and reproduces DFT energies, forces, equation-of-state behavior,
phonon dispersions, and
lattice thermal conductivities with near-DFT accuracy. Combined with Green-Kubo molecular dynamics, force-error correction, and a resistance-based treatment for dilute defective systems, MLIP4SiC-Mg enables quantitative thermal-conductivity calculations in large defective supercells. The corrected thermal conductivity of pristine 3C-SiC is 421 W/(mK) at 300 K, in good agreement with available experimental data. All defects considered strongly reduce thermal conductivity, but their scattering strengths are highly configuration dependent. V_C and Mg_TC act as strong
phonon scatterers, whereas isolated Mg_Si is comparatively weak. Residual thermal resistivity analysis shows that defect-induced thermal resistance is not strictly linear with concentration and should be treated as an effective temperature- and concentration-dependent scattering metric. Mg_Si-V_C clustering enhances scattering relative to isolated Mg_Si, but reduces the total excess resistance relative to spatially separated Mg_Si and V_C defects. These results clarify the configuration-dependent role of Mg transmutation in irradiation-degraded SiC and provide an atomistic framework for quantifying defect-controlled heat transport in
nuclear ceramics.
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All authors Chen Shen, Yang Su, Maciej P. Polak, Rafi Ullah, Nuohao Liu, Mary Alice Cusentino, Dane Morgan, Izabela Szlufarska