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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Monday 27 October

Materials Science

Topological Control of Transition Metal Networks for Reversible High-Capacity Li-rich Cathodes

Changming Ke, Yudi Yang, Minjun Wang et al. (+2 more)

Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity. The atomic-scale mechanisms behind this degradation, and whether it can be made reversible, remain open questions. Here, using submicrosecond-scale molecular dynamics simulations with first-principles accuracy, we directly visualize the entire charge-discharge cycle of Li$_2$MnO$_3$, uncovering the full lifecycle of the O$_2$-filled nanovoids responsible for degradation and identifying the critical size limit for voids to remain fully repairable upon discharge. Our results reveal that the topology of the Mn cation network is the key factor governing void growth, coalescence, and reparability. Based on a structural topology-informed design principle, we computationally develop a novel Li$_2$MnO$_3$ structure featuring a Mn lattice with a Kagome-like pattern, demonstrating full electrochemical reversibility even under extreme 80% delithiation. Our work establishes a new paradigm for designing high-energy cathodes, shifting the focus from mitigating damage to engineering inherent stability through atomic-level topological control of transition metal network.
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All authors Changming Ke, Yudi Yang, Minjun Wang, Jianhui Wang, Shi Liu

Materials Science

LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale

Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang et al. (+5 more)

Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theory (DFT), and subsequently, to machine learning interatomic potentials (MLIPs). While universal MLIPs (u-MLIPs) offer broad transferability, their computational overhead limits large-scale applications. Task-specific MLIPs (ts-MLIPs) achieve superior efficiency but require prohibitively expensive DFT data generation for each material system. In this paper, we propose LightPFP, a data-efficient knowledge distillation framework. Instead of using costly DFT calculations, LightPFP generates a distilled ts-MLIP by leveraging u-MLIP to generate high-quality training data tailored for specific materials and utilizing a pre-trained light-weight MLIP to further enhance data efficiency. Across a broad spectrum of materials, including solid-state electrolytes, high-entropy alloys, and reactive ionic systems, LightPFP delivers three orders of magnitude faster model development than conventional DFT-based methods, while maintaining accuracy on par with first-principles predictions. Moreover, the distilled ts-MLIPs further sustain the computational efficiency essential for large-scale molecular dynamics, achieving 1-2 orders of magnitude faster inference than u-MLIPs. The framework further enables efficient precision transfer learning, where systematic errors from the u-MLIP can be corrected using as few as 10 high-accuracy DFT data points, as demonstrated for MgO melting point prediction. This u-MLIP-driven distillation approach enables rapid development of high-fidelity, efficient MLIPs for materials science applications.
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All authors Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang, Ryuhei Okuno, Yuta Tsuboi, So Takamoto, Junichi Ishida, Ju Li

Materials Science

Amplified Photocurrent in Heterojunctions comprising Nano-rippled Zinc Oxide and Perovskite-inspired Cs3Cu2I5

Si Hyeok Yang, Lim Kyung Oh, Na Young Lee et al. (+12 more)

Molecular zero-dimensional (0D) halide perovskite-inspired cesium copper iodide (Cs3Cu2I5) is a highly promising candidate for optoelectronic applications due to their low toxicity, high stability, and intense blue emission. However, their intrinsically poor electrical conductivity, stemming from isolated conductive copper iodide tetrahedra by cesium atoms, severely limits charge transport which poses a critical challenge for optoelectronic applications. In this study, we propose a novel strategy to overcome this limitation by utilizing precisely optimized zinc oxide nanoripple structures within a lateral Cs3Cu2I5 photodetector (PD) architecture featuring interdigitated electrodes (IDEs). The ZnO nanoripple was systematically tuned to improve the percolation paths, providing efficient routes for photogenerated carriers to migrate to the IDEs. Consequently, the optimized heterojunctions comprising Cs3Cu2I5 and ZnO exhibited superior photocurrent compared to the pristine Cs3Cu2I5 counterparts. This nanostructure-mediated charge transport engineering strategy for lateral structured PDs offers a new pathway for utilizing low-conductivity 0D materials for conventional optoelectronics, next-generation Internet of Things sensor networks, and plausibly biosensing applications.
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All authors Si Hyeok Yang, Lim Kyung Oh, Na Young Lee, Dong Ho Lee, Sang Min Choi, Bowon Oh, Yun Ji Park, Yunji Cho, Jaesel Ryu, Hongki Kim, Sang-Hyun Chin, Yeonjin Yi, Myungkwan Song, Han Seul Kim, Jin Woo Choi

Materials Science

Mind the Gap – Imaging Buried Interfaces in Twisted Oxide Moirés

Harikrishnan KP, Xin Wei, Chia-Hao Lee et al. (+10 more)

The ability to tune electronic structure in twisted stacks of two-dimensional (2D) materials has motivated the exploration of similar moiré physics with twisted oxide membranes. Due to the intrinsic three-dimensional nature of bonding in many oxides, achieving atomic-level coupling is significantly more challenging than with van der Waals materials. Although clean interfaces with atomic-level proximity have been demonstrated in ceramic bicrystals using high-temperature and high-pressure processing to facilitate atomic diffusion that flattens rough interfaces, such conditions are not readily accessible when bonding oxide membranes. This study shows how topographic mismatch due to surface roughness of the membranes can restrict atomic-scale proximity at the interface to isolated patches even after contaminants and amorphous interlayers are eliminated. In interfaces between 2D materials and oxide membranes the reduced ability of the 2D material to conform to the membrane's step-terrace topography also limits atomic-scale contact. When imaging stacked membranes in projection, we find conventional through-focal imaging to be relatively insensitive to the buried interface, whereas electron ptychography detects structural variations on the order of a nanometer. These findings highlight interface roughness as a key challenge for the field of oxide twistronics and emphasize the need for reliable characterization methods, both in cross-section and projection.
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All authors Harikrishnan KP, Xin Wei, Chia-Hao Lee, Dasol Yoon, Yonghun Lee, Kevin J. Crust, Yu-Tsun Shao, Ruijuan Xu, Jong-Hoon Kang, Ce Liang, Jiwoong Park, Harold Y. Hwang, David A. Muller

Materials Science · Cross-listed from cs.LG

LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation

Subhojyoti Khastagir, Kishalay Das, Pawan Goyal et al. (+3 more)

Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language models (LLMs) or equivariant denoising models, each with complementary strengths: LLMs excel at handling discrete atomic types but often struggle with continuous features such as atomic positions and lattice parameters, while denoising models are effective at modeling continuous variables but encounter difficulties in generating accurate atomic compositions. To bridge this gap, we propose CrysLLMGen, a hybrid framework that integrates an LLM with a diffusion model to leverage their complementary strengths for crystal material generation. During sampling, CrysLLMGen first employs a fine-tuned LLM to produce an intermediate representation of atom types, atomic coordinates, and lattice structure. While retaining the predicted atom types, it passes the atomic coordinates and lattice structure to a pre-trained equivariant diffusion model for refinement. Our framework outperforms state-of-the-art generative models across several benchmark tasks and datasets. Specifically, CrysLLMGen not only achieves a balanced performance in terms of structural and compositional validity but also generates more stable and novel materials compared to LLM-based and denoisingbased models Furthermore, CrysLLMGen exhibits strong conditional generation capabilities, effectively producing materials that satisfy user-defined constraints. Code is available at https://github.com/kdmsit/crysllmgen
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All authors Subhojyoti Khastagir, Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly

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

Mastering energy landscapes via liquid liquid phase separation to program active supramolecular coassembly from the nano to macro scale

Yuanhao Wu, Alexander van Teijlingen, Julie Watts et al. (+6 more)

The energy landscape dictates pathways and outcomes in supramolecular selfassembly, yet harnessing it from the nano to the macro scales remains a major challenge. Here, we demonstrate liquid liquid phase separation (LLPS) as a powerful tool to navigate and engineer the energy landscapes of coassembly systems comprising disordered proteins and peptides. We quantitatively map the energy barriers and transition states governing structural transitions, enabling predictive on off control of assembly and hierarchical order from nano to macro scales. By integrating supramolecular biofabrication, we achieve spatially organized architectures with life like non equilibrium behaviour. Crucially, assembly stability and scalable selfsorting are shown to depend on accessing minimum energy states, regardless of whether the co assembled structures are disordered or ordered. This work establishes energy landscape mediation via LLPS as a general framework for designing lifelike, hierarchically structured materials.
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All authors Yuanhao Wu, Alexander van Teijlingen, Julie Watts, Zhiquan Yu, Shanshan Su, Jose Carlos RodriguezCabello, Lihi Abramovich, Tell Tuttle, Alvaro Mata

Materials Science

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction

Pengfei Gao and Haidi Wang

Universal machine learning interatomic potentials have emerged as efficient tools for materials simulation, yet their reliability for elastic property prediction remains unclear. Here, we present a systematic benchmark of four uMLIPs – MatterSim, MACE, SevenNet, and CHGNet – against first-principles data for nearly 11\,000 elastically stable materials from the Materials Project database. The results show that SevenNet achieves the highest accuracy, MACE and MatterSim balance accuracy with efficiency, while CHGNet performs less effectively overall. To further improve predictive quality, we perform targeted fine-tuning on all four uMLIPs using strained configurations derived from 185 high-error materials. After fine-tuning, CHGNet shows the most substantial improvement in overall accuracy, with MatterSim and SevenNet also benefiting from the fine-tuning, whereas MACE shows limited robustness to this procedure. This work provides quantitative guidance for model selection and data refinement, advancing uMLIPs toward reliable applications in mechanical property prediction.
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Materials Science

Nonlinear optical quantum theory of demagnetization in L1$_0$ FePt and FePd

G. P. Zhang, Y. H. Bai and Thomas F. George

It is now well established that a laser pulse can demagnetize a ferromagnet. However, for a long time, it has not had an analytic theory because it falls into neither nonlinear optics (NLO) nor magnetism. Here we attempt to fill this gap by developing a nonlinear optical theory centered on the spin moment, instead of the more popular susceptibility. We first employ group theory to pin down the lowest order of the nonzero spin moment in a centrosymmetric system to be the second order, where the second-order density matrix contains four terms of sum frequency generation (SFG) and four terms of difference frequency generation (DFG). By tracing over the product of the density matrix and the spin matrix, we are now able to compute the light-induced spin moment. We apply our theory to FePt and FePd, two most popular magnetic recording materials with identical crystal and electronic structures. We find that the theory can clearly distinguish the difference between those two similar systems. Specifically, we show that FePt has a stronger light-induced spin moment than FePd, in agreement with our real-time ultrafast demagnetization simulation and the experimental results. Among all the possible NLO processes, DFGs produce the largest spin moment change, a manifestation of optical rectification. Our research lays a solid theoretical foundation for femtomagnetism, so the light-induced spin moment reduction can now be computed and compared among different systems, without time-consuming real-time calculations, representing a significant step forward.
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Materials Science

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Omar Allam, Brook Wander, SungYeon Kim et al. (+16 more)

Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis.
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All authors Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Rodrigo Wang, Shivang Agarwal, Alan Rask, Alexandre Fleury, Chuhong Wang, Andrew Wildman, Thomas Mustard, Kevin Ryczko, Paul Abruzzo, AJ Nish, Aayush R. Singh

Materials Science

Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?

Peichen Zhong and Kristin A. Persson

Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/\ce{Li2O}/\ce{Li2CO3} nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (\ce{LiPO2F2}), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature $\sigma \approx 0.18$ mS cm$^{-1}$ and $E_\mathrm{a} \approx 0.40$ eV. This enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li–P–O–F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces.
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Materials Science · Cross-listed from physics.med-ph

Radiation enhanced diffusion in cartilages as a physical mechanism underlying radiation treatments of osteoarthritis and related disorders

Diana Shvydka and Victor Karpov

Degradation of joint cartilages can result in osteoarthritis (OA) affecting about 10% of the US population and responsible for significant hospitalization costs. While observations show that low dose radiation treatments (LDRT) bring improvements for a majority of OA patients, the underlying mechanism is not sufficiently understood. Here, we show how the radiation enhanced diffusion (RED) can boost the molecular transport in cartilages promoting cartilage self-healing rendering a mechanism for the observed positive LDRT effects on OA. Along with quantitative estimates for RED, we predict a related phenomenon of the electric charge build up that allows LDRT schedules promoting desirable types of molecular transports dominated by either positive or negative molecular species. Our analyses call upon further experimental verifications and clinical trials with curative rather than palliative intent. In addition to OA applications, our developed approaches can be useful for sports medicine dealing with damage or degeneration of the articular cartilages.
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Sunday 26 October

Materials Science · Cross-listed from physics.ins-det

Accurate Reporting of Ion Time-of-Flight during HiPIMS with Gated Front-End Mass Spectrometry

Nathan Rodkey, Jyotish Patidar, Kerstin Thorwarth et al. (+1 more)

The quality of high-power impulse magnetron sputtering (HiPIMS) deposited films can often improve through the effective use of metal-ion acceleration, requiring precise measurements of time-of-flight (ToF). These measurements are commonly done using time- and energy-resolved mass spectrometry but require careful consideration of the transit time of ions inside. The transit time is typically calculated by considering the travel length in various parts of the spectrometer (e.g. from orifice to detector), but errors associated with these estimations can lead to nonphysical values in a HiPIMS process (e.g. negative ToFs). Here we report a practical approach to determine ion ToF experimentally, using a bipolar HiPIMS power supply to synchronize a gating pulse to the front-end of a HIDEN Analytical EQP-300 mass spectrometer, placed at the working distance. The ToF is measured by applying a +70 V bias to repel ions, and a 5 us gating pulse of 0 V to accept them. To prevent interference with the HiPIMS plasma, a grounded shield is placed in front of the mass-spec head with a variable slit-opening (0.5-3 mm). The effectiveness of the shielding is verified by Langmuir probe measurements, noting negligible shifts in plasma potential for a DC sputter discharge. The gate is then synchronized to a HiPIMS pulse and data collected at 5 us intervals by adjusting the pulse delay. Measurements of the time-of-flights of Ar+, Al+, Sc+, Y+, and W+ ions are presented; Al+ and Ar+ ions were also compared to ToF calculated using mass spectrometry flight tube equations.
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All authors Nathan Rodkey, Jyotish Patidar, Kerstin Thorwarth, Sebastian Siol

Materials Science

Novel A2CrH6 (A = Ca, Sr, Ba) hydrides explored by first-principles calculations for hydrogen storage applications

Zakaria El Fatouaki, El Mustapha Hrida, Abderahhim Jabar et al. (+2 more)

A theoretical study of a number of properties of A2CrH6 (where A = Ba, Sr, and Ca) hydride perovskites with the Cambridge Serial Total Energy Package (CASTEP). These include structural, hydrogen storage, mechanical, phonon, thermodynamic, electronic, and optical properties. The lattice constants of the compounds studied are in the range from 7.220 Å to 8.082 Å, and they exhibit stable cubic crystal structures. Negative formation energies, elastic constants, phonon dispersion and AIMD simulations testify to their thermodynamic, mechanical, dynamic and thermal stability, respectively. For the perovskite hydrides Ba2CrH6, Sr2CrH6 and Ca2CrH6, the corresponding specific hydrogen storage capacities are 1.82 wt.%, 2.69 wt.%, and 4.37 wt.%, respectively. Among these compounds, Sr2CrH6 exhibits the lowest applicable hydrogen desorption temperature, at 463.7 K. The electronic bands show remarkable spin activity, demonstrating that the change of A2+ cation (where A = Ca, Sr, and Ba) immediately influences the spin polarization and electronic behavior of hydride perovskites. On the basis of the elastic moduli studied, the mechanical behavior determines that Ca2CrH6 is the strongest material. The present results highlight the potential of A2CrH6 (A = Ca, Sr, and Ba) perovskite hydrides, in particular Ca2CrH6, for applications in advanced energy systems and hydrogen storage, as well as for electrical and optoelectronic devices.
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All authors Zakaria El Fatouaki, El Mustapha Hrida, Abderahhim Jabar, Abdellah Tahiri, Mohamed Idiri

Materials Science

Switching between Skyrmions and Yoshimori Spin Spirals via Li Absorption in Janus Magnets

Xinyuan Jiang, Jian Wu and Weiyi Pan

Chiral magnetic textures have attracted considerable attention owing to their topological properties and potential applications in spintronic devices. Here, we employ first-principles calculations together with atomic spin dynamics simulations to explore the switching between skyrmions and Yoshimori-type spin spirals induced by Li adsorption in Janus two-dimensional (2D) CrTeSe. We show that selective Li adsorption on either the Se- or Te-terminated surface stabilizes distinct magnetic phases: Li adsorption on the Se side favors a Yoshimori-type spin spiral, whereas adsorption on the Te side stabilizes the skyrmionic state. This contrast originates from site-dependent modifications of exchange interactions, magnetic anisotropy (MA), and the Dzyaloshinskii-Moriya interaction (DMI). In addition, the response of magnetic textures to out-of-plane magnetic fields differs strongly between the two systems. These results demonstrate that surface adsorption provides an effective strategy for reversible control of chiral magnetic states in 2D magnets, while also offering fundamental insights into the competing interactions that govern the stability of skyrmions and Yoshimori spin spirals. Our findings highlight the potential of Janus 2D materials as a versatile platform for engineering tunable spintronic devices.
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Materials Science

Normal Dirac Semimetal Phase and Zeeman-Induced Topological Fermi Arc in PtSr5

Inkyou Lee, Churlhi Lyi and Youngkuk Kim

Pt-Sr binary intermetallics encompass a broad range of stoichiometries and crystal structures, stabilized by complex bonding and multivalent chemistry. The Sr-rich end member, PtSr5, is recently identified via artificial-intelligence-guided materials design as a body-centered tetragonal compound (I4/m). Using first-principles calculations, we show that PtSr5 hosts a Dirac semimetal phase with trivial Z2 topology, classified as a normal Dirac semimetal. A symmetry-indicator analysis based on parity eigenvalues at the eight time-reversal-invariant momenta confirms that all Z2 invariants-evaluated on time-reversal-invariant two-dimensional subspaces of momentum space with a direct band gap-are trivial, thereby establishing the topologically trivial nature of the Dirac semimetal phase. Nonetheless, our calculations reveal that applying an external Zeeman magnetic field along the z-axis drives the system into a Weyl semimetal phase, as corroborated by characteristic changes in the computed surface states. This work demonstrates the tunability of topological phases in PtSr5 via external perturbations and highlights the effectiveness of AI-based materials exploration in discovering new quantum materials.
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Materials Science

Bidirectional Photoinduced Carrier Transfer in Fluorinated Quasi-2D Perovskites Governing Enhanced Photocurrent Generation

Soumya Halder, Koushik Gayen, Nagendra S. Kamath et al. (+1 more)

Quasi-two-dimensional (quasi-2D) metal halide perovskites exhibit rich phase heterogeneity that profoundly influences light-matter interactions and charge transport. However, the fundamental mechanisms governing carrier transfer across distinct phases remain poorly understood. Here, we demonstrate effective electron-hole separation in fluorinated multilayered quasi-2D perovskite films nominally prepared for three layers, using femtosecond transient absorption spectroscopy. The films are revealed to comprise a heterogeneous phase distribution (with 1, 2, 3 layers and bulk) naturally stacked along the growth direction. Our ultraviolet photoelectron spectroscopy (UPS) measurements, show the type-two band alignment between the small-n (layer number) phases and the bulk. This alignment drives charge separation via both direct and sequential carrier transfer mechanisms, whereby electrons preferentially migrate into the bulk domains while holes accumulate in the small-n layers, extending even to single layer phase-a process only rarely observed in previous studies. The nearly symmetric transfer times of electrons and holes yield an efficient and balanced spatial separation of carriers. Global target analysis employing a carrier transfer model quantitatively reproduces the spectral evolution, providing a rigorous validation of the mechanism. Nonetheless, we found photocurrent enhancement in the diode devices of this quasi-2D perovskite as a consequence of the efficient transfer of photocarriers in the opposite directions. This work delivers a comprehensive picture of interphase charge transfer in fluorinated quasi-2D perovskites and highlights strategies to engineer directional separation pathways for high-performance photovoltaic, optoelectronic, and quantum devices.
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All authors Soumya Halder, Koushik Gayen, Nagendra S. Kamath, Suman Kalyan Pal

Materials Science

Magnetoelectric effect in multiferroic metals via a direct spin-charge interaction

Zefei Han, Haojin Wang and Yuanchang Li

Much is known about the magnetoelectric effect of multiferroic insulators, yet little is understood about multiferroic metals. In this work, we propose a stacking engineering strategy based on monolayer magnets to construct multiferroic metals, with validation through first-principles calculations on six experimentally synthesized materials. Such multiferroic metals exhibit predominantly linear magnetoelectric response, originating from direct spin-charge interactions as a result of external field-modulated Fermi energy. This fundamentally differs from spin-charge-lattice or spin-orbit coupling mechanisms in multiferroic insulators, offering application advantages in the field of high-speed response. We derive a universal formula for understanding the magnetoelectric coupling in these multiferroic metals. Our work provides insights for exploring magnetoelectric coupling mechanisms and designing functional materials with strong magnetoelectric coupling.
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Materials Science · Cross-listed from physics.comp-ph

Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon

Yvelin Giret, Filippo Federici Canova, Al-Moatasem El-Sayed et al. (+6 more)

Carbon nanotube (CNT) films are widely considered as prospective building blocks for advanced electronic and nanostructured materials. In particular, electrical transport in high-density CNT films results from a complex interplay between network morphology and CNT connectivity, which remains challenging to characterize quantitatively. To identify the structural parameters that govern the electrical current in CNT films, we employed coarse-grained molecular dynamics to construct dense mesoscale CNT film models that include CNTs with different chiralities and lengths. The effects of CNT geometrical features on the film morphologies were quantified by devising a set of structural descriptors and analyzing their mutual correlations. The impact of varying the concentration of amorphous carbon (aC) inclusions on the film structure was assessed. Finally, we employed a nodal analysis framework to compute the electrical current across the networks and correlate the charge transport characteristics to the underlying structural descriptors. The current is found to be enhanced in films that exhibit high curvature and buckling, low bundling, and strong connectivity. We discuss how the presence of aC inclusions modifies these morphological and current characteristics. This work provides a mesoscale modeling framework for modeling structure-transport relationships in dense CNT films and highlights the role of morphological descriptors in guiding the interpretation of electrical transport in complex nanostructured networks.
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All authors Yvelin Giret, Filippo Federici Canova, Al-Moatasem El-Sayed, Thomas R. Durrant, Rahul Sen, Harry Luan, Gennadi Bersuker, Alexander L. Shluger, David Z. Gao

Materials Science

Paradoxical Topological Soliton Lattice in Anisotropic Frustrated Chiral Magnets

Sayan Banik, Nikolai S. Kiselev and Ashis K. Nandy

Two-dimensional chiral magnets are known to host a variety of skyrmions, characterized by an integer topological charge. However, these systems typically favor uniform lattices as a thermodynamically stable phase composed of either skyrmions (Q = -1) or antiskyrmions (Q = 1). In isotropic chiral magnets, skyrmion-antiskyrmion coexistence is typically transient due to mutual annihilation, making the observation of a stable, long-range ordered lattice a significant challenge. Here, we address this challenge by demonstrating a skyrmion-antiskyrmion lattice as a magnetic field-induced topological ground state in chiral magnets with competing anisotropic interactions, specifically Dzyaloshinskii-Moriya and frustrated exchange interactions. This unique lattice exhibits a net-zero global topological charge due to the balanced populations of skyrmions and antiskyrmions. Furthermore, density functional theory and spin-lattice simulations identify 2Fe/InSb(110) as an ideal candidate material for realizing this phase. This finding reveals new possibilities for manipulating magnetic solitons and establishes anisotropic frustrated chiral magnets as a promising material class for future spintronic applications.
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Materials Science · Cross-listed from cs.LG

LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery

Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai et al. (+1 more)

Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials discovery (LLEMA), a unified framework that couples the scientific knowledge embedded in large language models with chemistry-informed evolutionary rules and memory-based refinement. At each iteration, an LLM proposes crystallographically specified candidates under explicit property constraints; a surrogate-augmented oracle estimates physicochemical properties; and a multi-objective scorer updates success/failure memories to guide subsequent generations. Evaluated on 14 realistic tasks that span electronics, energy, coatings, optics, and aerospace, LLEMA discovers candidates that are chemically plausible, thermodynamically stable, and property-aligned, achieving higher hit rates and improved Pareto front quality relative to generative and LLM-only baselines. Ablation studies confirm the importance of rule-guided generation, memory-based refinement, and surrogate prediction. By enforcing synthesizability and multi-objective trade-offs, LLEMA provides a principled approach to accelerating practical materials discovery. Project website: https://scientific-discovery.github.io/llema-project/
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All authors Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai, Chandan K. Reddy

Saturday 25 October

Materials Science · Cross-listed from q-bio.BM

Molecular Dynamics Simulations of Membrane Selectivity of Star Peptides Across Different Bacterial and Mammalian Bilipids

Amal Jayawardena, Andrew Hung, Greg Qiao et al. (+2 more)

Structurally nanoengineered antimicrobial peptide polymers (SNAPPs) are emerging as promising selective agents against bacterial membranes. In this study, we used all atom molecular dynamics simulation techniques to investigate the interaction of a promising cationic SNAPP architecture (Alt-SNAPP with 8 arms made of alternating lysine and valine residues) with modelled Gram-negative, Gram-positive, mammalian, and red blood cell membranes. Alt-SNAPP exhibited rapid and stable binding to bacterial membranes, driven by electrostatic interactions with anionic lipids such as phosphatidylglycerol (PG) and cardiolipin (CL), and supported by membrane fluidity. In contrast, mammalian and red blood cell membranes, enriched in zwitterionic lipids and cholesterol, resisted peptide association entirely. Analyses of center of mass distance, partial density, hydrogen bonding, and interaction energy confirmed that SNAPP remains fully excluded from host like membranes while forming stable, multivalent interactions with bacterial bilayers. These findings provide mechanistic insight into membrane selectivity of SNAPP and offer a molecular framework for designing next generation antimicrobial polymers with minimal off target toxicity.
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All authors Amal Jayawardena, Andrew Hung, Greg Qiao, Neil OBrien-Simpson, Elnaz Hajizadeh

Materials Science · Cross-listed from physics.atm-clus

Non-equilibrium Molecular Dynamics Study of Surface Wettability Effects on Pool Boiling of Water over Nanoscale Aluminum Substrate

Farhad Sotoudeh and Jafar Ghazanfarian

Non-equilibrium molecular dynamics (NEMD) simulations were used to study pool boiling of water films on an ultra-thin planar aluminum substrate as well as the effect of surface wettability. The simulation geometry is a 10 nm-thick water film on an FCC aluminum substrate heated from 300 K to 900 K. The first peak acceleration onset time of the film, as the measure of the nucleation start, has been observed. The average heating rates of the near-wall water were 0.064, 0.048, and 0.035 K/ps for hydrophilic, neutral, and hydrophobic surfaces, respectively. Boiling curves shows that the critical heat flux (CHF) equals 5216, 3979, and 2525 MW/m^2 at wall temperatures of 466, 502, and 561 K, respectively. The minimum heat flux (MHF, Leidenfrost point) is equal to 2157, 2463, and 2366 MW/m^2 at wall temperatures of 767, 784, and 746 K, respectively. Interfacial HTC remains higher for longer times under the hydrophilic condition, whereas Kapitza resistance is low initially but then increases sharply after transition to film boiling with the highest values for the hydrophobic surface. In general, the results demonstrate that engineering aluminum wettability towards intense hydrophilicity diminishes the explosive boiling point, increases CHF, and enhances nanoscale thermal management performance.
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Materials Science · Cross-listed from physics.chem-ph

Computing Solvation Shell Dynamics and Energetics in Electron Transfer Reactions via Molecular Dynamics Simulations

Zhenyu Wang, Mira Todorova, Christoph Freysoldt et al. (+1 more)

Marcus theory is fundamental to describing electron transfer reactions and quantifying their rates, effectively representing the energy surface associated with an electron transfer from the reactant to the product ionic state via parabolas within a reaction coordinate diagram. Here, we present an intuitive and computationally efficient generalised reaction coordinate amenable to molecular dynamics simulations. By utilising the nuclear charge of the ion, we are able to quantify in a targeted approach changes in the ion's solvation shell, thereby efficiently obtaining the free energy profile associated with the electron transfer, the transition state geometry and the evolution of the water network.
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All authors Zhenyu Wang, Mira Todorova, Christoph Freysoldt, Jörg Neugebauer

Materials Science

Reinforcement learning-guided optimization of critical current in high-temperature superconductors

Mouyang Cheng, Qiwei Wan, Bowen Yu et al. (+3 more)

High-temperature superconductors are essential for next-generation energy and quantum technologies, yet their performance is often limited by the critical current density ($J_c$), which is strongly influenced by microstructural defects. Optimizing $J_c$ through defect engineering is challenging due to the complex interplay of defect type, density, and spatial correlation. Here we present an integrated workflow that combines reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously identify optimal defect configurations that maximize $J_c$. In our framework, TDGL simulations generate current-voltage characteristics to evaluate $J_c$, which serves as the reward signal that guides the RL agent to iteratively refine defect configurations. We find that the agent discovers optimal defect densities and correlations in two-dimensional thin-film geometries, enhancing vortex pinning and $J_c$ relative to the pristine thin-film, approaching 60% of theoretical depairing limit with up to 15-fold enhancement compared to random initialization. This RL-driven approach provides a scalable strategy for defect engineering, with broad implications for advancing HTS applications in fusion magnets, particle accelerators, and other high-field technologies.
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All authors Mouyang Cheng, Qiwei Wan, Bowen Yu, Eunbi Rha, Michael J Landry, Mingda Li

Materials Science

Enhanced magnetic and optical properties of oxygen deficient TiO$_{2-\delta}$ nanoparticles synthesized by environment-friendly green route using whole plant extract of Phyllanthus niruri

Latika Mishra, Vinod Kumar Dwivedi, Vishal Kumar Chakradhary et al. (+2 more)

We report magnetic, optical and oxidation states of oxygen deficient TiO$_{2-\delta}$ nanoparticles (NPs) synthesized by environment-friendly green route using Phyllanthus niruri (PN) whole plant extract instead of leaf extract. Rietveld refinement of room temperature XRD pattern confirms the formation of pure phase anatase TiO$_2$ crystals in a tetragonal structure with space group I41/amd. TEM and SEM microstructure shows agglomerated spherical shape NPs exhibiting average particle size $\sim$ 35~nm. FTIR result confirms the presence of biomolecules and functional group attached to the surface of TiO$_2$ NPs. The core level XPS of O-1s and Ti-2p confirms the presence of oxygen vacancies that leads to the mixed oxidation states of Ti (Ti$^{4+}$ and Ti$^{3+}$). UV-vis result shows a strong absorption peak ($\sim$ 250~nm) along with reduced optical band gap energy E$_g$ $\sim$ 2.75~eV, possibly arises due to the surface plasmon resonance (SPR) caused by lower band gap energy emerging from oxygen vacancies. Magnetization as a function of applied magnetic field shows ferromagnetic nature at room temperature [M$_S$ $\sim$ 0.029~emu/g and H$_C$ $\sim$ 0.0143~T]. The observed ferromagnetic behaviour can be understood by virtual hopping of electrons from Ti$^{3+}$(3d$^1$) to Ti$^{4+}$(3d$^0$)-sites, however, vice versa is prohibited.
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All authors Latika Mishra, Vinod Kumar Dwivedi, Vishal Kumar Chakradhary, Akila G. Prabhudessai, Shamshun Nehar

Materials Science · Cross-listed from physics.optics

Beam shaping techniques for pulsed laser ablation in liquids: Unlocking tunable control of nanoparticle synthesis in liquids

S. Molina-Prados, N. M. Bulgakova, A. V. Bulgakov et al. (+3 more)

Nanoparticle synthesis via pulsed laser ablation in liquids has gained prominence as a versatile and environmentally friendly approach for producing ligand-free colloids with controlled composition, size, and morphology. While pulsed laser ablation in liquids offers unparalleled advantages in terms of nanoparticle purity and material versatility, enhancing the size control and productivity require modifications of the standard pulsed laser ablation in liquid technique such as the incorporation of beam shaping techniques. Recent developments in spatial and temporal beam shaping have demonstrated their potential to revolutionize pulsed laser ablation in liquids by enabling more precise energy deposition and modified nanoparticle production dynamics. This review highlights the critical role of beam shaping, encompassing spatial shaping of the beam to influence laser-material interaction, and temporal modification to optimize pulse duration and energy delivery. The current advancements in beam shaping techniques, their impact on the nanoparticle characteristics, and their broader implications for scaling pulsed laser ablation in liquids to meet industrial demands are highlighted, offering a comprehensive perspective on the future of this dynamic field.
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All authors S. Molina-Prados, N. M. Bulgakova, A. V. Bulgakov, J. Lancis, G. Mínguez Vega, C. Doñate-Buendia

Materials Science

Suppression of Thin-Film Thermal Conductivity due to Surface Roughness

Michimasa Morita and Junichiro Shiomi

Understanding thermal transport in silicon nanostructures is crucial for effective thermal management in semiconductor devices. In such nanostructures, boundary scattering can significantly reduce thermal conductivity. Diffusive boundary scattering explains the experimentally observed thickness dependence of thermal conductivity in thin films with thicknesses of tens of nanometers; however, introducing surface roughness further reduces the thermal conductivity, which falls far below the theoretical lower limit. In this study, we calculated the thermal conductivity and phonon transport properties of rough thin films with thicknesses of up to 25 nm using anharmonic lattice dynamics and investigated the mechanisms underlying the suppression of thermal conductivity arising from surface roughness. We found that in ultrathin films with rough surfaces, thermal conductivity was suppressed by a reduction in group velocity caused by hybridization with surface-localized modes, as well as a reduction in relaxation time due to the modulation of the anharmonic interatomic force constants of surface atoms. The reduction in group velocity significantly suppressed thermal conductivity across a wide range of thicknesses and surface-roughness values. In contrast, the reduction in relaxation time exhibited strong thickness dependence. Thus, this relaxation-time reduction should be considered in ultrathin films with roughness of approximately 0.1 nm and thicknesses below 5 nm. These thermal-conductivity suppression mechanisms due to surface roughness were not considered in the boundary-scattering model, resulting in an overestimation of the thermal conductivity of the roughened thin films by up to approximately 100%.
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Materials Science

Electric-Field-Tunable Luttinger compensated antiferromagnetism in double CrCl2 chains

Deping Guo, Weihan Zhang, Canbo Zong et al. (+2 more)

Luttinger compensated antiferromagnets (LcAFMs), combining spin polarization with vanishing net magnetization, offering distinct advantages for next-generation spintronic applications. Using first-principles calculations, we demonstrate that conventional antiferromagnetic CrCl2 double chains can be transformed into one-dimensional LcAFMs under an external electric field, exhibiting pronounced isotropic spin splitting. The magnitude of the splitting, as well as the band gap, can be effectively tuned by both in-plane and out-of-plane fields, thereby providing greater controllability than in two-dimensional counterparts. To further enhance the tunability, we design a nearly lattice-matched CrCl2/MoTe2 heterostructure and uncover that interfacial charge transfer generates a built-in electric field, inducing spin splitting comparable to that driven by external fields. These results establish interfacial engineering as a highly efficient route to realize and manipulate LcAFM states in low-dimensional magnets, expanding the design principles for spintronic functionalities at the nanoscale.
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All authors Deping Guo, Weihan Zhang, Canbo Zong, Cong Wang, Wei Ji

Materials Science

Machine Learning Enables Optimization of Diamond for Quantum Applications

Dane W. deQuilettes, Eden Price, Linh M. Pham et al. (+11 more)

Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged nitrogen vacancy (NV$^-$) and silicon vacancy centers (SiV$^-$), are emerging as quantum platforms poised for transition to commercial devices. A key enabler stems from the semiconductor-like platform that can be tailored at the time of growth. The large growth parameter space makes it challenging to use intuition to optimize growth conditions for quantum performance. In this paper, we use supervised machine learning to train regression models using different synthesis parameters in over 100 quantum diamond samples. We train models to optimize NV$^-$ defects in diamond for high sensitivity magnetometry. Importantly, we utilize a magnetic-field sensitivity figure of merit (FOM) for NV magnetometry and use Bayesian optimization to identify critical growth parameters that lead to a 300% improvement over an average sample and a 55% improvement over the previous champion sample. Furthermore, using Shapley importance rankings, we gain new physical insights into the most impactful growth and post-processing parameters, namely electron irradiation dose, diamond seed depth relative to the plasma, seed miscut angle, and reactor nitrogen concentration. As various quantum devices can have significantly different material requirements, advanced growth techniques such as plasma-enhanced chemical vapor deposition (PE-CVD) can provide the ability to tailor material development specifically for quantum applications.
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All authors Dane W. deQuilettes, Eden Price, Linh M. Pham, Arthur Kurlej, Swaroop Vattam, Alexander Melville, Tom Osadchy, Boning Li, Guoqing Wang, Collin N. Muniz, Paola Cappellaro, Jennifer M. Schloss, Justin L. Mallek, Danielle A. Braje

Friday 24 October

Materials Science

Dynamics and formation of antiferromagnetic textures in MnBi$_2$Te$_4$ single crystal

M. G. Kim, S. Boney, L. Burgard et al. (+2 more)

We report coherent X-ray imaging of antiferromagnetic (AFM) domains and domain walls in MnBi$_2$Te$_4$, an intrinsic AFM topological insulator. This technique enables direct visualization of domain morphology without reconstruction algorithms, allowing us to resolve antiphase domain walls as distinct dark lines arising from the A-type AFM structure. The wall width is determined to be 550(30) nm, in good agreement with earlier magnetic force microscopy results. The temperature dependence of the AFM order parameter extracted from our images closely follows previous neutron scattering data. Remarkably, however, we find a pronounced hysteresis in the evolution of domains and domain walls: upon cooling, dynamic reorganizations occur within a narrow $\sim$1 K interval below $T_N$, whereas upon warming, the domain configuration remains largely unchanged until AFM order disappears. These findings reveal a complex energy landscape in MnBi$_2$Te$_4$, governed by the interplay of exchange, anisotropy, and domain-wall energies, and underscore the critical role of AFM domain-wall dynamics in shaping its physical properties.
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All authors M. G. Kim, S. Boney, L. Burgard, L. Rutowski, C. Mazzoli