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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Sunday 23 November

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

Atomistic Framework for Glassy Polymer Viscoelasticity Across Twenty Frequency Decades

Ankit Singh, Vinay Vaibhav, Caterina Czibula et al. (+6 more)

Glassy polymers are central to engineering applications, yet their viscoelastic response over broad frequency and temperature ranges remains difficult to characterize. We extend non-affine deformation theory by incorporating a time-dependent memory kernel within the Generalized Langevin Equation for atomistic non-affine motions, yielding frequency-dependent mechanical response. Applied to poly(methyl methacrylate) (PMMA), the method captures the shear modulus and relaxation spectrum across more than twenty decades in frequency, from hundreds of terahertz to the millihertz regime, thus bridging polymer mechanics from ordinary to extreme scales. Our predictions show quantitative consistency with independent estimates from oscillatory-shear molecular dynamics, Brillouin scattering, ultrasonic spectroscopy, Split-Hopkinson testing, and dynamic mechanical analysis (DMA), demonstrating a unified theoretical-computational route for multiscale characterization of polymer glasses.
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All authors Ankit Singh, Vinay Vaibhav, Caterina Czibula, Astrid Macher, Petra Christoefl, Karin Bartl, Gregor Trimmel, Timothy W. Sirk, Alessio Zaccone

Materials Science

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks

Xiangzhou Zhu, Patrick Rinke and David A. Egger

The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials.
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Materials Science

Possibilities of the X-ray Diffraction Data Processing Method for Detecting Reflections with Intensity Below the Background Noise Component

S.V. Gabielkov, I.V. Zhyganiuk, A.D. Skorbun et al. (+4 more)

The values of the signal-to-noise ratio are determined, at which the method of processing X-ray diffraction data reveals reflections with intensity less than the noise component of the background. The possibilities of the method are demonstrated on weak reflections of $\alpha$-quartz. The method of processing X-ray diffraction data makes it possible to increase the possibilities of X-ray phase analysis in determining the qualitative phase composition of multiphase materials with a small (down to $0.1$ wt. %) content of several (up to eight) phases.
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All authors S.V. Gabielkov, I.V. Zhyganiuk, A.D. Skorbun, V.G. Kudlai, B.S. Savchenko, P.E. Parkhomchuk, S.O. Chikolovets

2511.18311 · 23 Nov · 27 pages, 6 figures. This version is the accepted author manuscript (postprint) and not the final publisher formatted version. The final version was published in Powder Diffraction (Cambridge University Press). This submission complies with the publisher self archiving policy

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Materials Science · Cross-listed from cs.LG

Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery

Rui Ding, Rodrigo Pires Ferreira, Yuxin Chen et al. (+1 more)

We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a locally deployable DR instance that integrates local retrieval-augmented generation with large language model reasoners, enhanced by a Deep Tree of Research (DToR) mechanism that adaptively expands and prunes research branches to maximize coverage, depth, and coherence. We systematically evaluate across 27 nanomaterials/device topics using a large language model (LLM)-as-judge rubric with five web-enabled state-of-the-art models as jurors. In addition, we conduct dry-lab validations on five representative tasks, where human experts use domain simulations (e.g., density functional theory, DFT) to verify whether DR-agent proposals are actionable. Results show that our DR agent produces reports with quality comparable to–and often exceeding–those of commercial systems (ChatGPT-5-thinking/o3/o4-mini-high Deep Research) at a substantially lower cost, while enabling on-prem integration with local data and tools.
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All authors Rui Ding, Rodrigo Pires Ferreira, Yuxin Chen, Junhong Chen

Materials Science

Terahertz oscillation of $180^{\circ}$ domain walls in ferroelectric membranes

Xiangwei Guo, Jiaxuan Wu, Yujie Zhu et al. (+5 more)

A fundamentally intriguing yet not well understood topic in the field of ferroelectrics is the collective excitation of domain walls (DWs), with potential applications to DW-based nanoelectronic and optoelectronic devices. Here we use dynamical phase-field simulations to identify the collective modes of an Ising-type $180^{\circ}$ DW in a uniaxially strained BaTiO3 membrane. The membrane, which concurrently functions as a cavity for polarization and acoustic waves, permits cavity-enhanced resonant excitation of polarization waves. The simulation reveals an unconventional DW sliding mode that exhibits a bulk-polarization-charge-driven nonzero resonant frequency in the sub-terahertz (THz) regime with a dynamically changing internal structure during sliding. These features differ from the previously reported zero-frequency DW sliding mode or the surface-polarization-charge-driven DW sliding mode. The effect of strain on the frequency of this unconventional DW sliding mode and other previously known THz DW eigenmodes is investigated by dynamical phase-field simulations and interpreted by eigenmode analysis or analytical calculation in a simplified one-dimensional (1D) system. These results provide new insights into the high-frequency dynamics of ferroelectric DWs and suggest opportunities for realizing strain control of phonon-DW resonance, and more broadly, for discovering and controlling unconventional DW modes in conventional domain patterns with applications to reconfigurable THz and optical devices.
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All authors Xiangwei Guo, Jiaxuan Wu, Yujie Zhu, Aiden Ross, Bo Wang, Paul G. Evans, Long-Qing Chen, Jia-Mian Hu

Materials Science · Cross-listed from cs.AI

Developing an AI Course for Synthetic Chemistry Students

Zhiling Zheng

Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.
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Saturday 22 November

Materials Science · Cross-listed from cs.CL

LLMs-Powered Accurate Extraction, Querying and Intelligent Management of Literature derived 2D Materials Data

Lijun Shang, Yadong Yu, Wenqiang Kang et al. (+7 more)

Two-dimensional (2D) materials have showed widespread applications in energy storage and conversion owning to their unique physicochemical, and electronic properties. Most of the valuable information for the materials, such as their properties and preparation methods, is included in the published research papers. However, due to the dispersion of synthe
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All authors Lijun Shang, Yadong Yu, Wenqiang Kang, Jian Zhou, Dongyue Gao, Pan Xiang, Zhe Liu, Mengyan Dai, Zhonglu Guo, Zhimei Sun

Materials Science

Engineering the Magnetocaloric Effect in Nd$T_4$B

Kyle W. Fruhling, Enrique O. González Delgado, Siddharth Nandanwar et al. (+4 more)

We present a comprehensive study of the magnetocaloric effect (MCE) in the Nd$T_4$B system where $T$ = Fe, Co, and Ni. These compounds are ferromagnetic kagome materials with tunable ordering temperatures, transition width, and magnetic moments depending on the choice of transition metal. Thus, they are good candidates for investigating the MCE. We characterize the MCE using standard metrics and construct ternary phase diagrams as functions of Fe, Co, and Ni concentrations. Using these phase diagrams, we engineer the composition NdFe$_{1.15}$Co$_{0.46}$Ni$_{2.39}$B to maximize the MCE. Interestingly, the Nd$T_4$B system shows a notable entropy change over a wide temperature range ($\sim$10 to 650 K), and particular compositions have notable MCEs spanning hundreds of Kelvin, making this a suitable system to study for technologies used in a wide range of temperatures. In a few cases, we observe a two-peak MCE. These two transitions, releasing comparable entropy, provide an interesting platform to study for applications in multi-stage cooling.
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All authors Kyle W. Fruhling, Enrique O. González Delgado, Siddharth Nandanwar, Xiaohan Yao, Zafer Turgut, Michael A. Susner, Fazel Tafti

Materials Science

Trigonal Distortion Driven Ground States in VX3 (X = Br and I)

Chamini S. Pathiraja, Deniz Wong, Christian Schulz et al. (+3 more)

Transition-metal halides V$X_3$ ($X$ = Br and I) have emerged as promising candidates for two dimensional spintronic and quantum applications due to their layer-dependent magnetism and tunable electronic states. However, experimental insights into their ground state electronic structures remain limited. Here, we present a comprehensive investigation of V$X_3$ using high resolution resonant inelastic x-ray scattering (RIXS) combined with ligand field multiplet calculations. The RIXS spectra reveal distinct $dd$ and charge-transfer excitations, allowing precise determination of electronic structure parameters, including the crystal field splitting, trigonal distortion, and Racah parameters. The determined parameters showed clear variation, indicating an increase in covalency from Br to I. The trigonal distortion parameters $\Delta_{D_{3d}}$ were determined to be -0.096 eV in VBr$_3$ and 0.07 eV in VI$_3$, indicating a sign opposition between the two compounds, reflecting good agreement with experimental RIXS data. Cluster model calculations yield a high-spin V$^{3+}$ $(S = 1)$ configuration, with an $e'^2_g$ ground state in VBr$_3$ and an $e'^1_ga^1_{1g}$ ground state in VI$_3$, consistent with trigonal elongation and compression, respectively. Our findings provide the most complete experimental determination of the low energy electronic structure in V$X_3$, offering valuable insights for designing 2D magnetic and spintronic materials based on vanadium halides.
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All authors Chamini S. Pathiraja, Deniz Wong, Christian Schulz, Yi-De Chuang, Yu-Cheng Shao, Byron Freelon

Materials Science

Tuning Breakdown-to-Coercive Field Ratio in Ultra-Thin Al1-xScxN Films via Reactive Nitrogen Atmosphere

Yinuo Zhang, Walter J. Smith, Giovanni Esteves et al. (+3 more)

Al1-xScxN has attracted significant interest due to its large remnant polarization and low processing temperature when compared to other ferroelectric material systems. However, device dielectric failure before ferroelectric switching remains a critical limitation for AlScN-based memory devices. With the continuing trend toward device miniaturization, expanding the operating window is essential for next-generation memory development. In this work, we optimized the breakdown field (EBD) and coercive field (EC) in ultra-thin Al1-xScxN films by controlling defect density via adjustment of nitrogen gas flow during sputter deposition. The characteristic breakdown field, EBD, was evaluated using Weibull statistics, yielding optimal characteristic breakdown fields of 12.47 MV/cm (EBD+) and -12.63 MV/cm (EBD-) for samples deposited under 27.5 sccm N2 flow. The minimum EC was achieved at a nitrogen flow of 25 sccm and increased for higher gas flows, a trend that is opposite to previous reports in much thicker films. The highest EBD over EC ratio of 2.25 occurred at 27.5 sccm, effectively expanding the operational window. Using a combination of X-ray diffraction and photoluminescence spectroscopy to study changes in crystal orientation and defects, device performance can be tuned by controlling the point defect concentration in the ultra-thin film via adjusting the sputtering N2 process gas flow rate.
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All authors Yinuo Zhang, Walter J. Smith, Giovanni Esteves, Eric A. Stach, Thomas E. Beechem, Roy H. Olsson III

Materials Science · Cross-listed from cond-mat.mes-hall

Interface-engineered voltage-driven magnetic tunnel junctions with ultra-low-energy magnetization switching

Yu Zhang, Meng Xu, Bowei Zhou et al. (+9 more)

Electric-field control of spin states offers a promising route to ultra-low-power, ultra-fast magnetization switching in spintronic devices such as magnetic tunnel junctions (MTJs). Recent progress in modulating spin-orbit interactions at the interfaces between 3d transition-metal ferromagnets and dielectric layers has underscored the role of atomic-scale heavy-metal doping in optimizing device performance. Here, we experimentally demonstrate highly energy-efficient, voltage-driven magnetization switching in MTJs exhibiting large tunnel magnetoresistance (TMR), enabled by a remote doping technique that precisely controls the iridium (Ir) concentration near the MgO-CoFeB interface in the free layer. Our devices achieve a switching energy of only 3.5 fJ per bit for nanoscale MTJs operating in the sub-nanosecond regime, while maintaining a TMR ratio up to 160 percent after 400 C post-annealing. These findings establish a viable pathway toward scalable, ultra-low-power nonvolatile memory, positioning voltage-driven MTJs as strong contenders for next-generation magnetoresistive random-access memory (MRAM) and other emerging spintronic applications.
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All authors Yu Zhang, Meng Xu, Bowei Zhou, Carter Eckel, Supriya Ghosh, Hwanhui Yun, Ali Habiboglu, Deyuan Lyu, Daniel B Gopman, Jian-Ping Wang, K. Andre Mkhoyan, Weigang Wang

Materials Science

Hydrogen diffusion in TiCr$_2$H$_x$ Laves phases: A combined ab initio and machine-learning-potential study

Pranav Kumar, Fritz Körmann, Kaveh Edalati et al. (+2 more)

The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ (0 < $x$ <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent paths between the first-nearest-neighbor face-sharing interstitial sites. The hydrogen migration barriers are substantially higher for the paths that require breaking a Ti-H bond than for those that require breaking a Cr-H bond. Molecular dynamics (MD) simulations with the MLIPs also demonstrate that hydrogen migration occurs more frequently within the hexagonal rings made of the A$_2$B$_2$ interstitial paths, each requiring the breaking of Cr-H bonds, than along the inter-ring paths. The diffusion coefficients of hydrogen obtained from the MD simulations reveal a non-monotonic dependence on hydrogen concentration, which is more pronounced at lower temperatures. Time-averaged radial distribution functions of hydrogen further show that hydrogen avoids face-sharing positions during diffusion and that the hydrogen occupancy at the second-nearest-neighbor edge-sharing positions increases with increasing hydrogen concentration. The diffusion coefficients of hydrogen within 400-1000 K follow an Arrhenius relationship, with activation barriers consistent with most experimental values. One-order of magnitude overestimation of diffusion coefficients compared with some experiments suggests a substantial impact of hydrogen trapping by defects such as Cr vacancies and Ti anti-sites in non-stoichiometric TiCr$_2$ in experiments.
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All authors Pranav Kumar, Fritz Körmann, Kaveh Edalati, Blazej Grabowski, Yuji Ikeda

Materials Science

PyAPX: Python toolkit for atomic configuration pattern exploration

Akira Kusaba, Tetsuji Kuboyama, Karol Kawka et al. (+2 more)

In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical composition, and elemental substitution, which explores chemical compositions that yield desirable properties in a given crystal structure. However, even when both the crystal structure and chemical composition are fixed, material properties can still vary depending on the atomic arrangements (configurations) at crystallographic sites. To support detailed material design, we present PyAPX, a Python toolkit that performs Bayesian searches of stable atomic configurations. A distinctive feature of this initial release is the introduction of encoding methods suitable for configuration search, and we evaluate their performance using the h-BCN system. As a result, they were confirmed to yield superior convergence compared to commonly used one-hot encoding. PyAPX is broadly applicable to crystalline materials and is expected to further advance materials discovery.
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All authors Akira Kusaba, Tetsuji Kuboyama, Karol Kawka, Pawel Kempisty, Yoshihiro Kangawa

Materials Science

Density functional theory for core-level X-ray absorption

Seokkyu An and Taisuke Ozaki

We establish a rigorous density functional theory (DFT) framework for core-level X-ray absorption spectroscopy (XAS) by formulating a constrained search for core-excited states based on the Gunnarsson-Lundqvist theorem. Within this framework, the explicit-core Delta SCF scheme enables shift-free absolute edge alignment and a consistent treatment of L/M edges with spin-orbit-resolved projectors. In addition, by exploiting dipole selection rules, we recast the evaluation of the dipole matrix elements, which otherwise requires many independent Slater determinant calculations, into a compact single determinant form. This reduces the computational scaling from $\mathcal{O}(N^4)$ to $\mathcal{O}(N^3)$, where $N$ is the number of electrons, without introducing additional approximations. Across representative C, B, O, and Li K-edge benchmarks in molecules and solids, the method reproduces line shapes, polarization anisotropies, and absolute onsets without empirical shifts, providing a robust and scalable route to quantitatively reliable XAS simulations within DFT.
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Materials Science

2D-RIXS: Resonant inelastic x-ray scattering microscopy with high energy and spatial resolutions

Kohei Yamamoto, Hakuto Suzuki and Jun Miyawaki

A two-dimensional resonant inelastic x-ray scattering (2D-RIXS) microscopy system has been developed at the beamline BL02U of NanoTerasu. The instrument combines a Wolter type-I mirror for spatial imaging with a varied-line-spacing grating spectrometer, simultaneously achieving micrometer-scale spatial resolution and ultrahigh energy resolution in the soft x-ray regime. Test chart measurements confirm a vertical spatial resolution of 1.0 um near the field-of-view center, and the horizontal resolution determined by the incident beam footprint is 0.8 um. RIXS imaging capabilities have been demonstrated by the measurements of a patterned NanoTerasu logo and exfoliated NiPS${}_3$ nanoflakes, highlighting its efficiency in locating specific microscale regions within inhomogeneous samples. These results establish 2D-RIXS microscopy as a position-sensitive probe of elementary excitations in quantum materials and functional devices.
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Materials Science

Hexagonal polymorphism induced structural disorder and dielectric anomalies of Ca/Mn modified BaTiO3

P. Maneesha, Dilip Sasmal, Rakhi Saha et al. (+7 more)

This work involves the local structural investigation of the samples using Extended X-ray Absorption Spectra (EXAFS) analysis to investigate structural changes due to the Ca and Mn-modified BaTiO3. TEM investigation of the crystal structure reveals the coexistence of the tetragonal and hexagonal phases of BaTiO3. Band gap modification and Urbach tail variation in UV-DRS measurements reveal a reduction of band gap from UV to Visible range (3.2 eV to 2 eV). This band gap change is correlated with the valence band modification observed in PES measurements due to localised defects in the material, and supported by theoretical Density of States calculations. The electron localisation function calculation is used to analyse the changes in the localised electron density near the dopant atoms. It reveals the local expansion and contraction of the lattice surrounding the dopant atom. All these structural modifications lead to variations in the dielectric properties and diffusive nature of the phase transition. The defect-induced structural modifications, multiple phase coexistence, band gap variations and dielectric properties are explored and correlated.
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All authors P. Maneesha, Dilip Sasmal, Rakhi Saha, Kiran Baraik, Soma Banik, R. Mittal, Mayanak K. Gupta, Abdelkrim Mekki, Khalil Harrabi, Somaditya Sen

Materials Science

Appraising the absolute limits of nanotubes and nanospheres to preserve high-pressure materials

Yin L. Xu, Guang F. Yang, Yi Sun et al. (+5 more)

Matter under high pressure often exhibits attractive properties, which, unfortunately, are typically irretrievable when released to ambient conditions. Intuitively, nanostructure engineering might provide a promising route to contain high-pressure phase of materials because of the exceptional mechanical strength at nanoscale. However, there is no available theoretical model that can analyze this possibility, not to mention to quantitatively evaluate the pressure-bearing capability of nano-cavities. Here, a physical model is proposed to appraise the absolute theoretical limit of various nanotubes/nanospheres to preserve high-pressure materials to ambient conditions. By incorporating with first-principles calculations, we screen and select four types of representative nanomaterials: graphene, hexagonal boron nitride (h-BN), biphenylene, and {\gamma}-graphyne, and perform systematic investigations. The results indicate that nanotube/nanosphere of graphene exhibits the best pressure-bearing capability, followed by h-BN, biphenylene and {\gamma}-graphyne. Our model reveals that the structure with the largest average binding energy per bond and the highest density of bonds will have the highest absolute limit to contain pressure materials, while electron/hole doping and interlayer interactions have minor effects. Our finding suggests that one can utilize nanotube/nanosphere with multiple layers to retrieve compressed material with higher pressures. For example, a single layer graphene sphere can retrieve compressed LaH10 with a volume size of 26 nm3 that corresponding to a pressure of 170 GPa and with a near room temperature superconductor transition of Tc=250 K. Similarly, in order to retrieve the metastable atomic hydrogen or molecular metallic hydrogen at about 250 GPa, it requires only three layers of a nanosphere to contain a volume size of 173 nm^3.
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All authors Yin L. Xu, Guang F. Yang, Yi Sun, Hong X. Song, Yu S. Huang, Hao Wang, Xiao Z. Yan, Hua Y. Geng

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

Validation of the copper equation of state via shock loading experiments of loosely associated powders

Yufeng Wang, Long Hao, Lixin Liu et al. (+5 more)

High-fidelity shock experiments were performed on copper powders with controlled porosity via improved target fabrication and assembly. Optical velocimetry and multi-channel pyrometry were used to obtain Hugoniot data, isentropic release paths, and interface temperature histories. The results validate a modified two-phase equation of state (EOS) for copper based on the framework of Greeff et al. The measured Hugoniot shows good agreement with the present model but exhibits significant softening above ~156 GPa relative to the original Greeff EOS, indicating that reduction in lattice specific heat becomes essential when shock temperatures exceed three times the melting point (T > 3Tm). Unloading behavior matches hydrodynamic simulations incorporating the recalibrated EOS, confirming its accuracy for off-Hugoniot states. Theoretical analysis of temperature release profiles suggests that the thermal conductivity of shocked copper powders may be considerably higher than first-principles predictions. Crucially, despite heterogeneity in shock heating, the macroscopic dynamic response of copper powders with a porosity of ~1.7 is well captured by an average-density EOS model, supporting the use of porous material experiments for EOS validation under extreme conditions.
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All authors Yufeng Wang, Long Hao, Lixin Liu, Fengchao Wu, Shijia Ye, Yuanchao Gan, Yi Sun, Hua Y. Geng

Materials Science · Cross-listed from physics.optics

Hyperbolic Dispersion and Low-Frequency Plasmons in Electrides

Qi-Dong Hao, Hao Wang, Hong-Xing Song et al. (+2 more)

Natural hyperbolic materials have attracted significant interest in the field of photonics due to their unique optical properties. Based on the initial successful explorations on layered crystalline materials, hyperbolic dispersion was associated with extreme structural anisotropy, despite the rarity of natural materials exhibiting this property. Here we show that non cubic electrides are generally promising natural hyperbolic materials owing to charge localization in interstitial sites. This includes elemental and binary electrides, as well as some two-dimensional materials that show prominent in-plane hyperbolic dispersion. They exhibit low plasma frequencies and a broad hyperbolic window spanning the infrared to the ultraviolet. In semiconductor electrides, anisotropic interband transitions provide an additional mechanism for hyperbolic behaviour. These findings remove the previously held prerequisite of structural anisotropy for natural hyperbolic materials, and open up new opportunities, which might change the current strategy for searching and design photonic materials.
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All authors Qi-Dong Hao, Hao Wang, Hong-Xing Song, Xiang-Rong Chen, Hua Y. Geng

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

Structural Relaxation and Anisotropic Elasticity of Ordered Block Copolymer Melts

Krista G. Schoonover, Gaurav Rawat, Emily B. Pentzer et al. (+1 more)

Block copolymer (BCP) melts play a critical role in the design of thermoplastics, owing in large part to the creation of alternating nano-scale domains of soft and stiff components. Considerable attention has been given to the short-to-intermediate time response of BCP melts, when the storage modulus is expected to dominate the viscoelastic properties. However, less attention has been paid to the long-time relaxation and rigidity of microphase separated BCP melts or the role that domain morphology plays in modulating near-equilibrium response. We take advantage of the ability of self-consistent field theory (SCFT) to calculate equilibrium properties of BCP melts to explore the anisotropic elastic response of ordered ABA and AB copolymer melts as quasistatic deformation processes. This allows us to determine the anisotropic stiffness of the liquid crystal-like lamellar and columnar phases due to modulations in domain spacing, as well as the full stiffness tensor of the cubic BCC sphere and double gyroid phases. We explore elastic modulus landscapes for both single grain materials and random polycrystals over architectural parameters and segregation strengths, using AB diblock and ABA triblock melts as key examples. Comparing AB and ABA melts, we show slight differences in equilibrium rigidity, which are shown to be due to a difference in effective domain segregation. Finally, we consider the equilibrium bending stiffnesses of lamellae and columnar phases, showing that the columnar phase is significantly stiffer than lamellae, with a characteristic bending length scale that is an order of magnitude larger.
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All authors Krista G. Schoonover, Gaurav Rawat, Emily B. Pentzer, Michael S. Dimitriyev

Materials Science · Cross-listed from cs.LG

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks

Zhihui Tian, Ethan Suwandi, Tomas Oppelstrup et al. (+3 more)

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we propose a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160^3), our method reduces memory usage and runtime in inference by 117x and 115x, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder's ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. The training was optimized to learn from the stochastic Potts Monte Carlo method. Our findings provide a highly scalable approach for simulating grain growth.
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All authors Zhihui Tian, Ethan Suwandi, Tomas Oppelstrup, Vasily V. Bulatov, Joel B. Harley, Fei Zhou

Friday 21 November

Materials Science · Cross-listed from cs.LG

Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling

Peter Hedström, Victor Lamelas Cubero, Jón Sigurdsson et al. (+6 more)

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks to complex industrial materials such as steel remains a challenge. A key obstacle is accurately capturing the intricate relationship between chemical composition, processing parameters, and the resulting microstructure and properties. To address this, we introduce a computational framework that combines physical insights with ML to develop a physics-informed continuous cooling transformation (CCT) model for steels. Our model, trained on a dataset of 4,100 diagrams, is validated against literature and experimental data. It demonstrates high computational efficiency, generating complete CCT diagrams with 100 cooling curves in under 5 seconds. It also shows strong generalizability across alloy steels, achieving phase classification F1 scores above 88% for all phases. For phase transition temperature regression, it attains mean absolute errors (MAE) below 20 {\deg}C across all phases except bainite, which shows a slightly higher MAE of 27 {\deg}C. This framework can be extended with additional generic and customized ML models to establish a universal digital twin platform for heat treatment. Integration with complementary simulation tools and targeted experiments will further support accelerated materials design workflows.
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All authors Peter Hedström, Victor Lamelas Cubero, Jón Sigurdsson, Viktor Österberg, Satish Kolli, Joakim Odqvist, Ziyong Hou, Wangzhong Mu, Viswanadh Gowtham Arigela

Materials Science

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem

Ashley S. Dale, Kangming Li, Brian DeCost et al. (+4 more)

Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing information missing from the model. In this work, the effect of different uncertainty estimation and calibration methods are evaluated for active learning when using ensembles of ALIGNN, eXtreme Gradient Boost, Random Forest, and Neural Network model architectures. We compare uncertainty estimates from ALIGNN deep ensembles to loss landscape uncertainty estimates obtained for solubility, bandgap, and formation energy prediction tasks. We then evaluate how the quality of the uncertainty estimate impacts an active learning campaign that seeks model generalization to out-of-distribution data. Uncertainty calibration methods were found to variably generalize from in-domain data to out-of-domain data. Furthermore, calibrated uncertainties were generally unsuccessful in reducing the amount of data required by a model to improve during an active learning campaign on out-of-distribution data when compared to random sampling and uncalibrated uncertainties. The impact of poor-quality uncertainty persists for random forest and eXtreme Gradient Boosting models trained on the same data for the same tasks, indicating that this is at least partially intrinsic to the data and not due to model capacity alone. Analysis of the target, in-distribution uncertainty, out-of-distribution uncertainty, and training residual distributions suggest that future work focus on understanding empirical uncertainties in the feature input space for cases where ensemble prediction variances do not accurately capture the missing information required for the model to generalize.
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All authors Ashley S. Dale, Kangming Li, Brian DeCost, Hao Wan, Yuchen Han, Yao Fehlis, Jason Hattrick-Simpers

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

Fractional Chern insulator edges: crystalline effects and optical measurements

Yan-Qi Wang, Johannes Motruk, Andrey Grankin et al. (+1 more)

Edge states of chiral topologically ordered phases are commonly described by chiral Luttinger liquids, effective theories that are exact only in the hydrodynamic limit. Motivated by recent bulk observations of fractional Chern insulators (FCIs) in two-dimensional materials and by synthetic realizations in ultracold atoms, we revisit this framework and quantify deviations from the hydrodynamic limit due to lattice effects. Using a combination of analytical arguments and numerical simulations, we disentangle universal from nonuniversal edge properties. We outline experimental probes in excitonic FCIs and in ultracold atom systems, and in particular propose time-resolved edge spectroscopy to directly access the predicted exponents and velocities.
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All authors Yan-Qi Wang, Johannes Motruk, Andrey Grankin, Mohammad Hafezi

Materials Science

Frustration driven magnetic correlations in the spin-$5/2$ triangular lattice antiferromagnet RbFe(HPO$_{3}$)$_{2}$

V. Nagpal, Sebin J. Sebastian, Surya P. Patra et al. (+4 more)

A detailed study of the structural and magnetic properties of a spin-$5/2$ triangular lattice antiferromagnet RbFe(HPO$_{3}$)$_{2}$ is presented using x-ray diffraction, magnetization, heat capacity, and $^{31}$P nuclear magnetic resonance (NMR) experiments on a polycrystalline sample. The crystal structure features an equilateral triangular lattice of Fe$^{3+}$ ions. The thermodynamic measurements reveal the onset of a magnetic long-range order at $T_{\rm N1} \simeq 7.8$ K in zero-field, followed by another low temperature field induced ordering at $T_{\rm N2}$ in higher fields. The transition at $T_{\rm N1}$ is further confirmed from the NMR spin lattice relaxation measurements. The value of the frustration ratio ($f \simeq 7$) implies moderate spin frustration in the compound. The $^{31}$P NMR spectra exhibit two distinct spectral lines corresponding to two inequivalent phosphorus sites (P1 and P2), consistent with the crystal structure. The P1 site is strongly coupled with an isotropic hyperfine coupling of $A_{\rm hf}^{\rm iso} = 0.55(2)$ T/$\mu_{\rm B}$ while the P2 site is weakly coupled with $A_{\rm hf}^{\rm iso} = 0.25(3)$ T/$\mu_{\rm B}$ with the Fe$^{3+}$ ions. The magnetic susceptibility and NMR shift data are described well assuming a spin-$5/2$ isotropic triangular lattice antiferromagnetic model with an average exchange coupling of $J/k_{\rm B} = 2.8(2)$ K. Below $T_{\rm N1}$, the spectra evolve into a nearly rectangular powder pattern, indicating a commensurate antiferromagnetic type order. The $^{31}$P spin-lattice relaxation rate well below $T_{\rm N1}$ follows a $T^3$ temperature dependence, implying a two-magnon Raman scattering mechanism in the ordered state. Three well-defined phase regimes are clearly ascertained in the $H-T$ phase diagram, reflecting a weak magnetic anisotropy in the compound.
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All authors V. Nagpal, Sebin J. Sebastian, Surya P. Patra, S. Shibash, Q.-P. Ding, Y. Furukawa, R. Nath

Materials Science

Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles

Ao Wang, Junwen Yin, Félix Antoine Goudreault et al. (+3 more)

Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron-phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al.
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All authors Ao Wang, Junwen Yin, Félix Antoine Goudreault, Michel Côté, Olle Hellman, Samuel Poncé

Materials Science

Minimalist machine-learned interatomic potentials can predict complex structural behaviors accurately

Iñigo Robredo-Magro, Binayak Mukherjee, Hugo Aramberri et al. (+1 more)

The past decade has witnessed a spectacular development of machine-learned interatomic potentials (MLIPs), to the extent that they are already the approach of choice for most atomistic simulation studies not requiring an explicit treatment of electrons. Typical MLIP usage guidelines emphasize the need for exhaustive training sets and warn against applying the models to situations not considered in the corresponding training space. This restricts the scope of MLIPs to interpolative calculations, essentially denying the possibility of using them to discover new phenomena in a serendipitous way. While there are reasons to be cautious, here we adopt a more sanguine view and challenge the predictive power of two representative and widely available MLIP approaches. We work with minimalist training sets that rely on little prior knowledge of the investigated materials. We show that the resulting models – for which we adopt modest/default choices of the defining hyperparameters – are very successful in predicting non-trivial structural effects (competing polymorphs, energy barriers for structural transformations, occurrence of non-trivial topologies) in a way that is qualitatively and quasi-quantitatively correct. Our results thus suggest an expanded scope of modern MLIP approaches, evidencing that somewhat trivial – and easy to compute – models can be an effective tool for the discovery of novel and complex physical phenomena.
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All authors Iñigo Robredo-Magro, Binayak Mukherjee, Hugo Aramberri, Jorge Íñiguez-González

Materials Science

Origin of the large topological Hall effect in the EuCd$_2$Sb$_2$ antiferromagnet

Faheem Gul, Orest Pavlosiuk, Tetiana Romanova et al. (+2 more)

We study the origin of large topological Hall effect in the single-crystalline EuCd$_2$Sb$_2$, which orders antiferromagnetically at the Néel temperature $T_{\rm N}=7.4$ K. Measurements of magnetoresistance and Hall resistivity disclose anomalies that evolve with temperature and magnetic field, closely tracking the magnetization process. Analysis of these data identifies three possible mechanisms responsible for the enhanced Berry curvature driving the observed topological Hall effect. Below and above $T_{\rm N}$, Weyl states are the main sources of large momentum-space Berry curvature, though their formation mechanisms differ in these two temperature ranges. Below $T_{\rm N}$, breaking of $C_{3}$ symmetry generates Dirac points that split into Weyl nodes in applied magnetic field, whereas above $T_{\rm N}$, strong spin fluctuations can induce Weyl states. The third contribution, which occurs below $T_{\rm N}$, arises from scalar spin chirality developing within antiferromagnetic domain walls, which generates a real-space Berry curvature.
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All authors Faheem Gul, Orest Pavlosiuk, Tetiana Romanova, Dariusz Kaczorowski, Piotr Wiśniewski

Materials Science

Momentum-Resolved Electronic Structure and Orbital Hybridization in the Layered Antiferromagnet CrPS$_4$

Lasse Sternemann, David Maximilian Janas, Eshan Banerjee et al. (+8 more)

Chromium thiophosphate (CrPS$_4$) is a layered two-dimensional antiferromagnetic semiconductor exhibiting intriguing spintronic and magneto-optical properties, yet its electronic band structure has remained experimentally uncharacterized. Here, we employ momentum-resolved photoemission spectroscopy above and below the Néel temperature, complemented by density functional theory with Hubbard U corrections (DFT+U), to reveal a valence band dominated by Cr $3d$ and S $3p$ states with a ligand-to-metal charge-transfer band gap. We identify weakly hybridized t$_{2g}$ orbitals responsible for magnetic ordering and strongly hybridized e$_{g}$ orbitals that relax dipole selection rules, enabling optically active orbital transitions. These findings establish a foundational understanding of CrPS$_4$'s electronic structure, providing a benchmark for theoretical models and informing future investigations into its orbital physics and potential device applications.
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All authors Lasse Sternemann, David Maximilian Janas, Eshan Banerjee, Richard Leven, Jonah Elias Nitschke, Marco Marino, Leon Becker, Ahmet Can Ademoğlu, Frithjof Anders, Stefan Tappertzhofen, Mirko Cinchetti

Materials Science

Crystal Growth and Physical Properties of Orthorhombic Kagome Lattice Magnets $R$Fe$_6$Ge$_6$ ($R$=Y, Tb, Dy)

Abhijeet Nayak, Sk Jamaluddin, Fan Wu et al. (+10 more)

Kagome magnets represent a promising class of materials that exhibit intriguing electronic and magnetic properties, and they have recently garnered significant attention. While most kagome-lattice compounds are hexagonal, we report here single-crystal growth and physical property measurements of $R$Fe$_6$Ge$_6$ ($R$ = Y, Dy, Tb) compounds, which crystallize in an orthorhombic structure. The structure can be derived from a hexagonal prototype $R$Fe$_3$Ge$_2$ by replacing every other $R$ atom with a covalent Ge$_2$ dimer. Ordering of these dimers renders the structure orthorhombic, slightly distorts the kagome net, and makes the three Fe sites formally inequivalent. The iron and rare-earth sublattices order independently. Fe moments order above 400 K, forming ferromagnetic kagome planes stacked antiferromagnetically, while rare-earth moments order below 9 K. TbFe$_6$Ge$_6$ exhibits a single magnetic ordering transition associated with the Tb atoms, whereas DyFe$_6$Ge$_6$ shows two distinct magnetic phase transitions, strongly influenced by crystal electric field effects on the Dy$^{3+}$ ions. Density functional theory (DFT) calculations indicate that the ferromagnetic ordering of the Fe planes is driven by a high density of states at the Fermi energy. They also reveal three dramatically different structural energy scales: $R$ and Ge$_2$ form alternating 1D chains perpendicular to the kagome planes, and violating this alternation incurs a large energy cost. Aligning these chains is less costly, and achieving a two-dimensional order of anti-aligned chains requires very little energy. These compounds represent a unique class of materials, offering new opportunities to investigate the interplay between the distinct crystal lattice geometry and the underlying electronic and magnetic properties.
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All authors Abhijeet Nayak, Sk Jamaluddin, Fan Wu, Emily Rapp, Resham Babu Regmi, Mohamed El Gazzah, Bence G. Márkus, László Forró, Madhav P. Ghimire, Allen Oliver, Kateryna Foyevtsova, Igor I. Mazin, Nirmal J. Ghimire