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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Saturday 8 November

Materials Science

Impact of electron-phonon interaction on the electronic structure of interfaces between organic molecules and a MoS$_2$ monolayer

Ignacio Gonzalez Oliva, Sebastian Tillack, Fabio Caruso et al. (+2 more)

By means of first-principles calculations, we investigate the role of electron-phonon interaction in the electronic structure of hybrid interfaces, formed by MoS$_2$ and monolayers of the organic molecules pyrene and pyridine, respectively. Quasiparticle energies are initially obtained within the $G_0W_0$ approximation and subsequently used to evaluate the electron-phonon self-energy and momentum-resolved spectral functions to assess the temperature renormalization of the band structure. We find that the band-gap renormalization by zero-point vibrations of both hybrid systems is comparable to that of pristine MoS$_2$, with a value of approximately 80 meV. Pronounced features of molecular origin emerge in the spectral function of the valence region, which we attribute to satellites arising from out-of-plane vibrational modes of the organic monolayers. For pyrene, this satellite exhibits a predominantly molecular character, while for pyridine, it has a hybrid nature, originating from the coupling of molecular vibrations to the MoS$_2$ valence band.
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All authors Ignacio Gonzalez Oliva, Sebastian Tillack, Fabio Caruso, Pasquale Pavone, Claudia Draxl

Friday 7 November

Materials Science · Cross-listed from cs.LG

Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models

Eliane Younes, Elie Hachem and Marc Bernacki

Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization.
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Materials Science · Cross-listed from physics.chem-ph

Omics-scale polymer computational database transferable to real-world artificial intelligence applications

Ryo Yoshida, Yoshihiro Hayashi, Hidemine Furuya et al. (+103 more)

Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science.
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All authors Ryo Yoshida, Yoshihiro Hayashi, Hidemine Furuya, Ryohei Hosoya, Kazuyoshi Kaneko, Hiroki Sugisawa, Yu Kaneko, Aiko Takahashi, Yoh Noguchi, Shun Nanjo, Keiko Shinoda, Tomu Hamakawa, Mitsuru Ohno, Takuya Kitamura, Misaki Yonekawa, Stephen Wu, Masato Ohnishi, Chang Liu, Teruki Tsurimoto, Arifin, Araki Wakiuchi, Kohei Noda, Junko Morikawa, Teruaki Hayakawa, Junichiro Shiomi, Masanobu Naito, Kazuya Shiratori, Tomoki Nagai, Norio Tomotsu, Hiroto Inoue, Ryuichi Sakashita, Masashi Ishii, Isao Kuwajima, Kenji Furuichi, Norihiko Hiroi, Yuki Takemoto, Takahiro Ohkuma, Keita Yamamoto, Naoya Kowatari, Masato Suzuki, Naoya Matsumoto, Seiryu Umetani, Hisaki Ikebata, Yasuyuki Shudo, Mayu Nagao, Shinya Kamada, Kazunori Kamio, Taichi Shomura, Kensaku Nakamura, Yudai Iwamizu, Atsutoshi Abe, Koki Yoshitomi, Yuki Horie, Katsuhiko Koike, Koichi Iwakabe, Shinya Gima, Kota Usui, Gikyo Usuki, Takuro Tsutsumi, Keitaro Matsuoka, Kazuki Sada, Masahiro Kitabata, Takuma Kikutsuji, Akitaka Kamauchi, Yusuke Iijima, Tsubasa Suzuki, Takenori Goda, Yuki Takabayashi, Kazuko Imai, Yuji Mochizuki, Hideo Doi, Koji Okuwaki, Hiroya Nitta, Taku Ozawa, Hitoshi Kamijima, Toshiaki Shintani, Takuma Mitamura, Massimiliano Zamengo, Yuitsu Sugami, Seiji Akiyama, Yoshinari Murakami, Atsushi Betto, Naoya Matsuo, Satoru Kagao, Tetsuya Kobayashi, Norie Matsubara, Shosei Kubo, Yuki Ishiyama, Yuri Ichioka, Mamoru Usami, Satoru Yoshizaki, Seigo Mizutani, Yosuke Hanawa, Shogo Kunieda, Mitsuru Yambe, Takeru Nakamura, Hiromori Murashima, Kenji Takahashi, Naoki Wada, Masahiro Kawano, Yosuke Harada, Takehiro Fujita, Erina Fujita, Ryoji Himeno, Hiori Kino, Kenji Fukumizu

Materials Science

Novel Transformations of PbTiO3 with Pressure and Temperature

Husam Farraj, Stefano Racioppi, Gaston Garbarino et al. (+8 more)

We investigated the behavior of lead titanate (PbTiO3) up to 100 GPa, both at room temperature and upon laser heating, using synchrotron X ray diffraction combined with density functional theory (DFT) computations. At the high pressure temperature (PT) conditions produced in laser heated diamond anvil cells, PbTiO3 dissociates into PbO and TiO2, consistent with our DFT computations showing that decomposition becomes enthalpically favored above 65 GPa. In contrast, on room temperature compression, PbTiO3 persists in the tetragonal I4mcm phase up to at least 100 GPa. Laser heating produces distinct PbO phases: a compressed form of alpha PbO and a previously unreported delta PbO polymorph, both of which transform to beta PbO on decompression. The calculations predict that alpha PbO undergoes pressure-induced band gap closure, metallizing above 70 GPa, whereas the delta and beta phases remain semiconducting with a band gap above 1 eV even at megabar pressures. The experimental and confirming theoretical results reveal an unanticipated dimension of the behavior of PbTiO3, showing that distinct equilibrium and metastable phases can be stabilized along different PT synthesis paths.
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All authors Husam Farraj, Stefano Racioppi, Gaston Garbarino, Muhtar Ahart, Anshuman Mondal, Samuel G. Parra, Jesse S. Smith, R. E. Cohen, Eva Zurek, Jordi Cabana, Russell J. Hemley

Materials Science

From Quantum Annealing to Alloy Discovery: Towards Accelerated Design of High-Entropy Alloys

Diego Ibarra-Hoyos, Peter Connors, Ho Jang et al. (+6 more)

Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima confounding the true optimal state. Classical methods often get trapped in these minima, while quantum annealing can escape them via quantum fluctuations, including tunneling, that overcome narrow energy barriers. We present a quantum-assisted machine-learning (QaML) framework that employs quantum annealing to address these combinatorial optimization challenges through feature selection, support-vector training formulated in QUBO form for classification and regression, and a new QUBO-based neural-network pruning formulation. Recursive batching enables quantum annealing to handle large feature spaces beyond current qubit limits, while quantum-pruned networks exhibit superior generalization over classical methods, suggesting that quantum annealing preferentially samples flatter, more stable regions of the loss landscape. Applied to high-entropy alloys (HEAs), a data-limited but compositionally complex testbed, the framework integrates models for fracture-strain classification and yield-strength regression under physics-based constraints. The framework identified and experimentally validated Al8Cr38Fe50Mn2Ti2 (at.%), a single-phase BCC alloy exhibiting a 0.2 % yield strength of 568 MPa, greater than 40 % compressive strain without fracture, and a critical current density in reducing acid nearly an order of magnitude lower than 304 stainless steel. These results establish QA as a practical route to overcome classical optimization limits and accelerate materials discovery.
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All authors Diego Ibarra-Hoyos, Peter Connors, Ho Jang, Nathan Grain, Israel Klich, Gia-Wei Chern, Peter K. Liaw, John R. Scully, Joseph Poon

Materials Science

Exchange field induced symmetry breaking in quantum hexaborides

D. Rivera, Fernando P. Sabino, H. Raebiger et al. (+3 more)

Symmetry breaking (SB) has proven to be a powerful approach for describing quantum materials: strong correlation, mass renormalization, and complex phase transitions are among the phenomena that SB can capture, even when coupled to a mean-field-like theory. Traditionally, corrective schemes were required to account for these effects; however, SB has emerged as an alternative that can also successfully describe the intricate physics of quantum materials. Here, we explore spin SB on EuB6 and SmB6 and how its relation to the exchange field can determine onsite properties, depending on the type of symmetry breaking. Using spin-polarized Density Functional Theory (DFT) calculations with the r2SCAN functional, we systematically compare four magnetic configurations, one totally symmetric - non-magnetic (NM) configuration - and three with different types of symmetry breaking: ferromagnetic (FM), antiferromagnetic (AFM) and a paramagnetic (PM) configuration - modeled through a Special Quasirandom Structure (SQS) method - to capture local symmetry-breaking effects. Our results show that the PM configuration produces distinct magnetic environments for the rare-earth atoms, leading to different exchange fields. These, in turn, induce symmetry breaking in the electronic and magnetic properties of Eu and Sm. Those results provide an alternative explanation for the experimental results on both materials, EuB6 and SmB6, where X-ray Absorption Spectroscopy (XAS) and X-ray Absorption Near Edge Structure (XANES) measurements suggest the presence of multiple atomic environments, previously attributed to a mixed-valence configuration.
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All authors D. Rivera, Fernando P. Sabino, H. Raebiger, A. Ruzsinszky, J. P. Perdew, G. M. Dalpian

Materials Science

Surface acoustic wave enabled all-optical determination of the interlayer elastic constants of van der Waals interface

N.Yu. Frolov, A.Yu. Klokov, A. I. Sharkov et al. (+2 more)

Understanding the properties of two-dimensional materials interfaces with the substrate is necessary for device applications. Surface acoustic wave propagation through the layered material flake on a substrate could provide unique information on the transverse rigidity of the flake-to-substrate interaction. We generate ultrasonic waves by a focused femtosecond laser pulse at the surface of the model system – fused silica with h-BN flake transferred above. Using an all-optical spatially resolved pump-probe interferometric technique, we measure the spatial dependencies of the surface vertical velocity profiles. Our measurements reveal the appearance of the surface acoustic wave dispersion in the hBN flake region compared to fused silica surface. Multilayer modeling allows us to gain access to longitudinal and shear elastic coupling constants $c^*_{33}$ and $c^*_{44}$ between hexagonal BN and substrate.
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All authors N.Yu. Frolov, A.Yu. Klokov, A. I. Sharkov, M.V. Pugachev, A.Yu. Kuntsevich

Materials Science · Cross-listed from nlin.PS

Stability theory of flat band solitons in nonlinear wave systems

Cheng Shi, Ross Parker, Panayotis G. Kevrekides et al. (+1 more)

We establish a sharp criterion for the stability of a class of compactly supported, homogeneous density“minimal compact solitons” or MCS states, of the time-dependent discrete nonlinear Schrödinger equation on a multi-lattice, $\mathbb L$ ($\mathbb L$-DNLS). MCS states arise for multi-lattices where a nearest neighbor Laplace-type operator on $\mathbb L$ has a flat band. Our stability criterion is in terms of the explicit form of the nonlinearity and the projection of distinguished vectors onto the flat band eigenspace. We apply our general results to MCS states of DNLS for the diamond, Kagomé and checkerboard lattices. In lattices where MCS states are unstable, we demonstrate how to engineer the nonlinearity to stabilize small amplitude MCS states. Finally, via systematic numerical computations, we put our analytical results in the context of global bifurcation diagrams.
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All authors Cheng Shi, Ross Parker, Panayotis G. Kevrekides, Michael I. Weinstein

Materials Science

Single-Atom Photocatalysts on TiO2 : Insights from X-ray Absorption Spectroscopy

Yingying Li, David Morris and Peng Zhang

Surface modification of TiO2 with single-atom catalysts (SACs) is an effective strategy for enhancing photocatalytic efficiency. However, thorough characterization of SACs at the atomic scale remains challenging. X-ray absorption spectroscopy (XAS) offers unique advantages for the in-depth analysis of TiO2-supported SACs. By employing XAS, the local atomic structure, oxidation state, and electronic properties of the SACs, as well as the underlying photocatalytic mechanism, can be revealed. Herein, we present a short review on the application of XAS in studying TiO2-supported SACs. We first elucidate the key role of XAS in simultaneously probing the structure and electronic properties of monometallic SACs across different periods. Next, we discuss XAS studies of bimetallic SACs from the perspective of each constituent element and highlight the element-specific capabilities of XAS for analyzing multi-element SACs. Finally, we demonstrate how in situ XAS can effectively monitor structural and electronic property changes in SACs under real photocatalytic reaction conditions. Additionally, we provide practical suggestions for utilizing XAS more efficiently in the analysis of various SAC systems.
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Materials Science · Cross-listed from cond-mat.str-el

Reconfigurable flat bands from cooperative moiré and charge order

B.K. Saika, S. Buchberger, S. Mo et al. (+9 more)

The formation of flat electronic bands from long-wavelength superperiodic moiré potentials in van der Waals heterostructures underpins the creation and control of a host of highly-tunable correlated and topological phases. While the underlying moiré periodicity is typically a fixed property of the heterostructure, here we show how the development of a charge-density wave (CDW) in one of the constituent materials can create an emergent moiré lattice. We demonstrate this experimentally in TiSe$_2$/graphite epitaxial heterostructures, using angle-resolved photoemission and scanning-tunnelling microscopy and spectroscopy to directly image the resulting long-wavelength moiré potential and concomitant flat-band formation. We show how the intrinsically low-energy, deformable landscape of the CDW imparts significant tunability, stabilising quasi-one-dimensional moiré domains from symmetry breaking within the CDW, and allowing the complete suppression of flat-band formation by carrier doping across the CDW phase transition. Our findings thus open a new avenue for engineering moiré matter by exploiting the rich many-body states of the parent compounds of 2D heterostructures.
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All authors B.K. Saika, S. Buchberger, S. Mo, A. Rajan, D. Halliday, Y.-C. Yao, L.C. Rhodes, H. Decitre, T. Balasubramanian, C. Polley, P. Wahl, P.D.C. King

Materials Science

An improved reliability factor for quantitative low-energy electron diffraction

Alexander M. Imre, Lutz Hammer, Ulrike Diebold et al. (+2 more)

Quantitative low-energy electron diffraction [LEED $I(V)$ or LEED $I(E)$, the evaluation of diffraction intensities $I$ as a function of the electron energy] is a versatile technique for the study of surface structures. The technique is based on optimizing the agreement between experimental and calculated intensities. Today, the most commonly used measure of agreement is Pendry's $R$ factor $R_\mathrm{P}$. While $R_\mathrm{P}$ has many advantages, it also has severe shortcomings, as it is a noisy target function for optimization and very sensitive to small offsets of the intensity. Furthermore, $R_\mathrm{P} = 0$, which is meant to imply perfect agreement between two $I(E)$ curves can also be achieved by qualitatively very different curves. We present a modified $R$ factor $R_\mathrm{S}$, which can be used as a direct replacement for $R_\mathrm{P}$, but avoids these shortcomings. We also demonstrate that $R_\mathrm{S}$ is as good as $R_\mathrm{P}$ or better in steering the optimization to the correct result in the case of imperfections of the experimental data, while another common $R$ factor, $R_\mathrm{ZJ}$ (suggested by Zanazzi and Jona) is worse in this respect.
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All authors Alexander M. Imre, Lutz Hammer, Ulrike Diebold, Michele Riva, Michael Schmid

Materials Science

Point Defects Limited Carrier Mobility in Janus MoSSe monolayer

Nguyen Tran Gia Bao, Ton Nu Quynh Trang, Phan Bach Thang et al. (+3 more)

Point defects, often formed during the growth of Janus MoSSe, act as built-in scatterers and affect carrier transport in electronic devices based on Janus MoSSe. In this study, we employ first-principles calculations to investigate the impact of common defects, such as sulfur vacancies, selenium vacancies, and chalcogen substitutions, on electron transport, and compare their influence with that of mobility limited by phonons. Here, we define the saturation defect concentration ($C_{\mathrm{sat}}$) as the highest defect density that still allows the total mobility to remain within 90% of the phonon-limited value, providing a direct measure of how many defects a device can tolerate. Based on $C_{\mathrm{sat}}$, we find a clear ranking of defect impact: selenium substituting for sulfur is relatively tolerant, with $C_{\mathrm{sat}}\approx2.07\times10^{-4}$, while selenium vacancies are the most sensitive, with $C_{\mathrm{sat}}\approx3.65\times10^{-5}$. Our $C_{\mathrm{sat}}$ benchmarks and defect hierarchy provide quantitative, materials-specific design rules that can guide the fabrication of high-mobility field-effect transistors, electronic devices, and sensors based on Janus MoSSe.
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All authors Nguyen Tran Gia Bao, Ton Nu Quynh Trang, Phan Bach Thang, Nam Thoai, Vu Thi Hanh Thu, Nguyen Tuan Hung

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

Anomalous Nodal Gap in a Doped Spin-1/2 Antiferromagnetic Mott Insulator

Yong Hu, Christopher Lane, Xiang Chen et al. (+10 more)

Many emergent phenomena appear in doped Mott insulators near the insulator-to-metal transition. In high-temperature cuprate superconductors, superconductivity arises when antiferromagnetic (AFM) order is gradually suppressed by carrier doping, and a $\textit{d}$-wave superconducting gap forms when an enigmatic nodal gap evolves into a point node. Here, we examine electron-doped Sr$_{2}$IrO$_{4}$, the 5$\textit{d}$-electron counterpart of cuprates, using angle-resolved photoemission spectroscopy. At low doping levels, we observe the formation of electronic states near the Fermi level, accompanied by a gap at the AFM zone boundary, mimicking the AFM gap in electron-doped cuprates. With increasing doping, a distinct gap emerges along the (0,0)-($\pi$,$\pi$) nodal direction, paralleling that observed in hole-doped cuprates. This anomalous nodal gap persists after the collapse of the AFM gap and gradually decreases with further doping. It eventually vanishes into a point node of the reported $\textit{d}$-wave gap. These observations replicate the characteristic features in both electron- and hole-doped cuprates, indicating a unified route toward nodal metallicity in doped spin-1/2 AFM Mott insulators.
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All authors Yong Hu, Christopher Lane, Xiang Chen, Shuting Peng, Zeliang Sun, Makoto Hashimoto, Donghui Lu, Tao Wu, Robert S. Markiewicz, Xianhui Chen, Arun Bansil, Stephen D. Wilson, Junfeng He

Materials Science · Cross-listed from cond-mat.str-el

Structural modulation, physical properties, and electronic band structure of the kagome metal UCr$_6$Ge$_6$

Z. W. Riedel, P. A. E. Murgatroyd, C. S. Kengle et al. (+13 more)

The chemical flexibility of the $RM_6X_6$ stoichiometry, where an $f$-block element is intercalated in the CoSn structure type, allows for the tuning of flatbands associated with kagome lattices to the Fermi level and for emergent phenomena due to interactions between the $f$- and $d$-electron lattices. Yet, 5$f$ members of the “166" compounds are underrepresented compared with 4$f$ members. Here, we report single-crystal growth of UCr$_6$Ge$_6$, which crystallizes in a monoclinically distorted Y$_{0.5}$Co$_3$Ge$_3$-type structure. The real-space character of the modulation, which is unique within the $RM_6X_6$ family, is approximated by a 3$\times$1$\times$2 supercell of the average monoclinic cell. The compound has kagome-lattice flatbands near the Fermi level and a moderately enhanced electronic heat capacity, as evidenced by its low-temperature Sommerfeld coefficient ($\gamma=86.5$~mJ~mol$^{-1}$~K$^{-2}$) paired with band structure calculations. The small, isotropic magnetization and featureless resistivity of UCr$_6$Ge$_6$ suggest itinerant uranium 5$f$ electrons and Pauli paramagnetism. Angle-resolved photoemission spectroscopy results provide evidence for uranium 5$f$ weight at the Fermi level and for a flatband near the Fermi level associated with the chromium $3d$ kagome lattice. The isotropic magnetic behavior of the uranium 5$f$ electrons starkly contrasts with localized behavior in other uranium 166 compounds, highlighting the high tunability of the magnetic ground state across the material family.
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All authors Z. W. Riedel, P. A. E. Murgatroyd, C. S. Kengle, P. M. T. Vianez, A. Schmidt, X. Du, K. Allen, T. K. Kim, C. Lane, Ying Wai Li, Jian-Xin Zhu, J. D. Thompson, F. Ronning, S. M. Thomas, P. F. S. Rosa, E. D. Bauer

Materials Science

Antisolvent-Assisted Growth of Centimeter-Scale CsPbBr$_3$ Perovskite Single Crystals: A Theory-Guided Approach

I.O. Simonenko, R.G. Nazmitdinov and T.N. Vershinina

The fabrication of large, high-quality single crystals (SCs) of all-inorganic cesium lead bromide (CsPbBr$_3$) via accessible methods remains a significant challenge. This work presents a systematic approach to optimize the antisolvent vapor-assisted crystallization (AVC) method, where the experimental design is guided by a theoretical methods at each step. A synergistic 9:1 (v/v) DMSO/DMF binary solvent was selected to balance solubility and kinetics, a choice rationalized by an analysis of Gutmann's donor numbers. Subsequently, ethanol was selected as a promising antisolvent by evaluating its properties against key criteria of miscibility and diffusion rate using Hansen Solubility Parameters (HSP) and Fick's law expressed in terms of saturated vapor pressure. Within this rationally-defined chemical system, the "growth window" was experimentally mapped, identifying an optimal precursor concentration of 0.35 M and a preliminary titration step to induce a controlled metastable state. The optimized protocol consistently yields phase-pure, orthorhombic CsPbBr$_3$ SCs up to 1 cm in size within one week at room temperature. The resulting crystals exhibit high crystallinity and thermal stability up to \SI{550}{\celsius}
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Materials Science

Influence of Bi Alloying on GaAs Valence Band Structure

Joshua J. P. Cooper, Jared W. Mitchell, Shane Smolenski et al. (+9 more)

Bi alloying is predicted to transform GaAs from a semiconductor to a topological insulator or semi-metal. To date, studies of the GaAs$_{1-x}$Bi$_x$ alloy band structure have been limited, and the origins of Bi-induced enhancement of the spin-orbit splitting energy, $\Delta_\mathrm{SO}$, are unresolved. Here, we present high-resolution angle-resolved photoemission spectroscopy (ARPES) of droplet-free epitaxial GaAs$_{1-x}$Bi$_x$ films with $x_{\mathrm{Bi}}$ = 0.06. In addition to quantifying the Bi-induced shifts of the light-hole and heavy-hole valence bands, we probe the origins of the Bi-enhanced $\Delta_\mathrm{SO}$. Using exact-two-component density functional theory calculations, we identify the key role of Bi p-orbitals in the upward shift of the light-hole and heavy-hole bands that results in the Bi-enhanced $\Delta_\mathrm{SO}$.
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All authors Joshua J. P. Cooper, Jared W. Mitchell, Shane Smolenski, Ming Wen, Eoghan Downey, Chris Jozwiak, Aaron Bostwick, Eli Rotenberg, Kai Sun, Dominika Zgid, Na Hyun Jo, Rachel S. Goldman

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

Spin covalent chemistry of carbon

E.F. Sheka

This review presents the covalent chemistry of carbon within the spin-radical concept of electron interaction. Using the language of valence bond trimodality, the regions of classical spinless covalence and its spin counterpart are defined. Carbon is the only element exhibiting spin covalent chemistry. Classical covalent chemistry of carbon concerns molecular substances whose valence bond structure includes segregate or chained single sp3C-C bonds. Substances with double sp2C-C and triple sp1C-C bonds are the subject of spin covalent chemistry of carbon. The mathematical apparatus of spin covalence forms the basis of algorithms governing the chemical modification of carbon substances, polymerization processes, and catalysis involving them, making it possible to supplement the empirical spin covalent chemistry of carbon with its virtual analog.
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Materials Science · Cross-listed from physics.chem-ph

Fuzzy Neural Network Performance and Interpretability of Quantum Wavefunction Probability Predictions

Pedro H. M. Zanineli, Matheus Zaia Monteiro, Vinicius Francisco Wasques et al. (+2 more)

Predicting quantum wavefunction probability distributions is crucial for computational chemistry and materials science, yet machine learning (ML) models often face a trade-off between accuracy and interpretability. This study compares Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in modeling quantum probability distributions for the H$_{2}^+$ ion, leveraging data generated via Physics-Informed Neural Networks (PINNs). While ANN achieved superior accuracy (R$^2$ = 0.99 vs ANFIS's 0.95 with Gaussian membership functions), it required over 50x more parameters (2,305 vs 39-45). ANFIS, however, provided unique interpretability: its Gaussian membership functions encoded spatial electron localization near proton positions ($\mu = 1.2 A$), mirroring Born probability densities, while fuzzy rules reflected quantum superposition principles. Rules prioritizing the internuclear direction revealed the system's 1D symmetry, aligning with Linear Combination of Atomic Orbitals theory–a novel data-driven perspective on orbital hybridization. Membership function variances ($\sigma$) further quantified electron delocalization trends, and peak prediction errors highlighted unresolved quantum cusps. The choice of functions critically impacted performance: Gaussian/Generalized Bell outperformed Sigmoid, with errors improving as training data increased, showing scalability. This study underscores the context-dependent value of ML: ANN for precision and ANFIS for interpretable, parameter-efficient approximations that link inputs to physical behavior. These findings advocate hybrid approaches in quantum simulations, balancing accuracy with explainability to accelerate discovery. Future work should extend ANFIS to multi-electron systems and integrate domain-specific constraints (e.g., kinetic energy terms), bridging data-driven models and fundamental physics.
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All authors Pedro H. M. Zanineli, Matheus Zaia Monteiro, Vinicius Francisco Wasques, Francielle Santo Pedro Simões, Gabriel R. Schleder

Materials Science

Uniaxial stress tuning of the anomalous Hall effect in Mn3Ge

G. A. Lombardi, L. O. Kutelak, M. M. Piva et al. (+6 more)

Tunable electronic properties in magnetic materials lead to novel physical phenomena that have the potential to be exploited in the design of new spintronic devices. Here, we report the effect of uniaxial stress on the anomalous Hall effect (AHE) in the hexagonal frustrated antiferromagnetic Heusler compound Mn3Ge. Our x-ray diffraction results show that the c/a ratio varies linearly with strain when stress is applied along the a axis, as well as a significantly higher Young's modulus along the c direction. The linear behavior of the c/a ratio under uniaxial stress mirrors that seen under hydrostatic pressure up to 1.8 GPa, but results in a characteristically different behavior of the AHE. Stress applied along the a axis induces a distortion in the ab plane, smoothing the abrupt jump in the AHE signal at zero magnetic field. In contrast, stress applied along the c axis has little effect, presumably due to the higher Young's modulus. We argue that this is due to pronounced changes in magnetic order.
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All authors G. A. Lombardi, L. O. Kutelak, M. M. Piva, V. E. S. Frehse, G. A. Calligaris, K. Manna, C. Felser, R. D. dos Reis, M. Nicklas

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

Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique

Kousuke Nakano, Stefano Battaglia and Jürg Hutter

Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schrödinger equation, suitable for modern many-core supercomputer architectures. Despite its potential, one of the major drawbacks that still hinders QMC applications, especially when targeting dynamical properties of large systems or extensive datasets, is the lack of an affordable method to compute atomic forces that are consistent with the corresponding potential energy surfaces (PESs), also known as unbiased atomic forces. Recently, one of the authors in the present paper proposed a way to obtain unbiased forces with the Jastrow-correlated Slater determinant ansatz, where the determinant part is frozen to the values obtained by a mean-field method, such as Density Functional Theory. However, the proposed method has a significant drawback for its applications: for a system with $N$ nuclei, one requires 6$N$ additional DFT calculations to get unbiased forces. This paper presents a way to replace the 6$N$ DFT calculations with a single coupled-perturbed Kohn-Sham calculation, following the so-called Lagrangian technique established in quantum chemistry. We also demonstrate that the developed unbiased VMC force calculation improves not only the consistency with PESs, but also its accuracy, by investigating three molecules from the rMD17 benchmark set, and comparing the unbiased VMC forces with those obtained by CCSD(T) calculations. We found that the bare VMC forces are biased from the CCSD(T) ones, while the unbiased ones give values closer to those of the CCSD(T) ones. Our benchmark test also reveals that the unbiased VMC forces yield very consistent values with hybrid and meta GGAs, but do not necessarily yield values that are very close to those of CCSD(T).
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Materials Science

Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials

Hayato Wakai, Shintaro Ishiwata and Atsuto Seko

Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global structure searches are performed for 11 bismuth-based binary systems, including Na-Bi, Ca-Bi, and Eu-Bi, under pressures ranging from 0 to 20 GPa, employing polynomial MLPs developed specifically for these systems. The searches reveal numerous compounds not previously reported in the literature and identify all experimentally known compounds that are representable within the explored configurational space. These results highlight the robustness and reliability of the current MLP-based structure search. The study provides valuable insights into the discovery and design of novel bismuth-based materials under both ambient and high-pressure conditions.
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Materials Science

LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases

Martin Siron, Inel Djafar, Ali Ramlaoui et al. (+10 more)

The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability. Robust standardization and de-duplication methods are needed to address these issues and streamline materials research. We present LeMat-Bulk, a unified dataset combining Materials Project, OQMD, and Alexandria, encompassing over 5.3 million PBE-calculated materials and also representing the largest collection of PBESol and SCAN functional calculations. Our methodology standardizes calculations across databases that utilize different parameters, effectively addressing redundancy and enhancing cross-compatibility. To de-duplicate, we propose a hashing function which we termed the Bonding Algorithm Weisfeiller-Lehman (BAWL). We comprehensively benchmark this fingerprint under atomic noise, lattice strain, and symmetry transformations, demonstrating that it outperforms existing fingerprinting techniques such as SLICES, and CLOUD in robustness while offering greater computational efficiency than similarity-based approaches such as Pymatgen's StructureMatcher. Additionally, the fingerprint facilitates the analysis of functional-dependent trends (PBE, PBESol, SCAN) offering a scalable framework for data-driven materials science.
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All authors Martin Siron, Inel Djafar, Ali Ramlaoui, Etienne du Fayette, Amandine Rossello, Edvin Fako, Matthew McDermott, Felix Therrien, Luis Barroso-Luque, Flaviu Cipcigan, Philippe Schwaller, Thomas Wolf, Alexandre Duval

Materials Science

Probing the atomic dynamics of ultrafast melting with femtosecond electron diffraction

M. Z. Mo, M. B. Maigler, T. Held et al. (+12 more)

Melting is an everyday phase transition that is determined by thermodynamic parameters like temperature and pressure. In contrast, ultrafast melting is governed by the microscopic response to a rapid energy input and, thus, can reveal the strength and dynamics of atomic bonds as well as the energy flow rate to the lattice. Accurately describing these processes remains challenging and requires detailed insights into transient states encountered. Here, we present data from femtosecond electron diffraction measurements that capture the structural evolution of copper during the ultrafast solid to liquid phase transformations. At absorbed energy densities 2 to 4 times the melting threshold, melting begins at the surface slightly below the nominal melting point followed by rapid homogeneous melting throughout the volume. Molecular dynamics simulations reproduce these observations and reveal a weak electron lattice energy transfer rate for the given experimental conditions. Both simulations and experiments show no indications of rapid lattice collapse when its temperature surpasses proposed limits of superheating, providing evidence that inherent dynamics limits the speed of disordering in ultrafast melting of metals.
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All authors M. Z. Mo, M. B. Maigler, T. Held, B. K. Ofori-Okai, A. Bergermann, Z. Chen, R. K. Li, X. Shen, K. Sokolowski-Tinten, R. Redmer, X. J. Wang, J. Schein, D. O. Gericke, B. Rethfeld, S. H. Glenzer

Materials Science

Ultrafast Terahertz Photoconductivity and Near-Field Imaging of Nanoscale Inhomogeneities in Multilayer Epitaxial Graphene Nanoribbons

Arvind Singh, Jan Kunc, Tinkara Troha et al. (+2 more)

We study broadband terahertz (THz) conductivity and ultrafast photoconductivity spectra in lithographically fabricated multilayer epitaxial graphene nanoribbons grown on C- face of 6H-SiC substrate. THz near-field spectroscopy reveals local conductivity variations across nanoscale structural inhomogeneities such as wrinkles and grain boundaries within the multilayer graphene. Ultrabroadband THz far-field spectroscopy (0.15-16 THz) distinguishes doped graphene layers near the substrate from quasi-neutral layers (QNLs) further from the substrate. Temperature-dependent THz conductivity spectra are dominated by intra-band transitions both in the doped and QNLs. Photoexcitation then alters mainly the response of the QNLs: these exhibit a very high carrier mobility and a large positive THz photoconductivity with picosecond lifetime. The response of QNLs strongly depends on the carrier temperature $T_c$: the scattering time drops by an order of magnitude down to ~10 fs upon an increase of $T_c$ from 50 K to $T_c >$ 1000 K, which is attributed to an enhanced electron-electron and electron-phonon scattering and to an interaction of electrons with mid-gap states.
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All authors Arvind Singh, Jan Kunc, Tinkara Troha, Hynek Němec, Petr Kužel

Materials Science

Disorder-broadened topological Hall phase and anomalous Hall scaling in FeGe

Chaman Gupta, Chris Matsumura, Hongbin Yang et al. (+9 more)

Magnetic skyrmions are topologically protected spin textures that are promising candidates for low-power spintronic memory and logic devices. Realizing skyrmion-based devices requires an understanding of how structural disorder affects their stability and transport properties. This study uses Ne$^{+}$ ion irradiation at fluences from $10^{11}$ to $10^{14}$ ions-cm$^{-2}$ to systematically vary defect densities in 80 nm epitaxial FeGe films and quantify the resulting modifications to magnetic phase boundaries and electronic scattering. Temperature- and field-dependent Hall measurements reveal that increasing disorder progressively extends the topological Hall signal from a narrow window near 200K̃ in pristine films down to 4K̃ at the highest fluence, with peak amplitude more than doubling. Simultaneously, the anomalous Hall effect transitions from quadratic Berry curvature scaling to linear skew scattering behavior, with the skew coefficient increasing threefold. These results establish quantitative correlations between defect concentration, skyrmion phase space, and transport mechanisms in a chiral magnet. It demonstrates that ion-beam modification provides systematic control over both topological texture stability and electrical detectability.
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All authors Chaman Gupta, Chris Matsumura, Hongbin Yang, Sarah Edwards, Rebeca M. Gurrola, Jiun-Haw Chu, Hanjong Paik, Yongqiang Wang, David A. Muller, Robert Streubel, Tzu-Ming Lu, Serena Eley

Materials Science

Intrinsic Fracture Nonreciprocity at the Nanoscale

Siwei Zhao, Penghua Ying, Guoqiang Zhang et al. (+4 more)

We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric prestrain states at crack tips, governing bond-breaking thresholds through charge redistribution. The failure criterion obeys a universal exponential scaling law between normalized charge density and bond strain, insensitive to bonding chemistry and local atomic environment. The magnitude of nonreciprocity scales systematically with lattice mismatch, reaching 49% at 10% mismatch. Validation across hexagonal, square, rectangular, and oblique two-dimensional lattices confirms universality, establishing interface strain engineering as a general design principle that bridges electronic structure to nanoscale failure, enabling rational design of damage-tolerant nanostructures.
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All authors Siwei Zhao, Penghua Ying, Guoqiang Zhang, Ke Zhou, Shengying Yue, Yan Chen, Yilun Liu

Materials Science

Review of the tight-binding method applicable to the properties of moiré superlattices

Xueheng Kuang, Federico Escudero, Pierre A. Pantaleón et al. (+2 more)

Moiré superlattices have emerged as a versatile platform for exploring a wide range of ex- otic quantum phenomena. Unlike angstrom-scale materials, the moiré length-scale system contains a large number of atoms, and its electronic structure is significantly modulated by the lattice relaxation. These features pose a huge theoretical challenge. Among the available theoretical approaches, tight-binding (TB) methods are widely employed to predict the electronic, transport, and optical properties of systems such as twisted graphene, twisted transition-metal dichalcogenides (TMDs), and related moiré materials. In this review, we pro- vide a comprehensive overview of atomistic TB Hamiltonians and the numerical techniques commonly used to model graphene-based, TMD-based and hBN-based moiré superlattices. We also discuss the connection between atomistic TB descriptions and effective low-energy continuum models. Two examples of different moiré materials and geometries are provided to emphasize the advantages of the TB methods. This review is intended to serve as a theoretical and practical guide for those seeking to apply TB methods to the study of various properties of moiré superlattices.
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All authors Xueheng Kuang, Federico Escudero, Pierre A. Pantaleón, Francisco Guinea, Zhen Zhan

Materials Science

Preparation and evaluation of alexandrite, forsterite, and topaz substrates for the epitaxial growth of rutile oxides

Monique Kubovsky, Yorick A. Birkhölzer, Luka B. Mitrovic et al. (+3 more)

Metal-insulator transitions and superconductivity in rutile-structured oxides hold promise for advanced electronic applications, yet their thin film synthesis is severely hindered by limited substrate options. Here, we present three single- crystalline substrates, BeAl2O4, Mg2SiO4, and Al2SiO4(F,OH)2, prepared via optimized thermal and chemical treatments to achieve atomically smooth surfaces suitable for epitaxial growth. Atomic force microscopy confirms atomic step-and-terrace surface morphologies, and oxide molecular-beam epitaxy growth on these substrates demonstrates successful heteroepitaxy of rutile TiO2, VO2, NbO2, and RuO2 films. Among these unconventional substrates, BeAl2O4 exhibits exceptional thermal and chemical stability, making it a versatile substrate candidate. These findings introduce new substrate platforms that facilitate strain engineering and exploration of rutile oxide thin films, potentially advancing the study of their strain-dependent physical properties.
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All authors Monique Kubovsky, Yorick A. Birkhölzer, Luka B. Mitrovic, Hanjong Paik, George R. Rossman, Darrell G. Schlom

Materials Science

Mixed-Valent Ce Disrupts Magnetic Ordering in CeFe$_2$Ga$_8$

Hui-Fei Zhai, Sergey L. Bud'ko, Jacob W. Fritsky et al. (+1 more)

Mixed valency in intermetallics with lanthanide cations is well established as a pathway to unusual charge transport, complex magnetism, and superconductivity. In this work, we report a comprehensive study of the structural, magnetic, electronic, and thermal properties of the mixed valent compound CeFe$_2$Ga$_8$. Powder X-ray diffraction (PXRD) and X-ray photoelectron spectroscopy (XPS) characterize CeFe$_2$Ga$_8$ as a quasi-one-dimensional (Q1D) compound with mixed-valent Ce$^{3+}$ and Ce$^{4+}$ on a single crystallographic site. $^{57}$Fe Mössbauer spectroscopy indicates that the Fe sublattice is nonmagnetic, in direct contrast with recent reports of this compound. Low-temperature electrical resistivity and heat capacity measurements show no evidence of magnetic ordering, and a modest Sommerfeld coefficient ($\gamma$) of 22.7 mJ/mol$\cdot$K$^2$ make extensive Kondo hybridization unlikely. DC and AC magnetic susceptibility data suggest short-range magnetic order at $\sim$5.2 and 7.6 K with no frequency dependence, ruling out canonical spin-glass behavior in this compound. Additionally, the magnetic susceptibility data does not contain any broad features that are typically associated with an intermediate valence state in Ce, suggesting either high-temperature valence fluctuation or a different mechanism of mixed valency. This work demonstrates that mixed-valent Ce inhibits magnetic ordering in CeFe$_2$Ga$_8$ and provides a broader picture for how to analyze short-range spin interactions in Q1D intermetallics.
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All authors Hui-Fei Zhai, Sergey L. Bud'ko, Jacob W. Fritsky, Jason F. Khoury

Thursday 6 November

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

Gas Mixture Diffusion and Distribution in the Porous ZIF-90 Framework

Ashok Yacham, Tarak K. Patra and Jithin John Varghese

Understanding how gas mixtures diffuse and distribute within porous frameworks is central to designing advanced separation and storage materials. Here, we investigate the transport and spatial distribution of binary gas mixtures in a porous metal organic framework, viz., ZIF-90, using molecular simulations. We perform grand canonical Monte Carlo (GCMC) simulations to examine the competitive adsorption of carbon dioxide and nitrogen from a binary gas mixture in ZIF-90, while molecular dynamics (MD) simulations are conducted to investigate the transport behavior of the adsorbed molecules within the framework. These integrated simulations reveal that the framework topology and pore chemistry jointly dictate diffusion pathways and preferential occupancy of gas species, underscoring their intrinsic interdependence. Competitive adsorption leads to distinct spatial partitioning within the pores, which in turn modulates mixture diffusivity inside the porous medium compared to their bulk properties. Our results provide molecular-level insight into how ZIF-90 accommodates and separates gas mixtures, offering design principles for optimizing metal-organic frameworks in energy and environmental applications.
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