diff --git a/filtered_feed.xml b/filtered_feed.xml
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@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Tue, 30 Dec 2025 12:42:44 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; nanocomposites</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048285?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048285</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049035?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049035</guid></item><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Tue, 30 Dec 2025 18:31:10 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011693?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 29 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia&lt;/p&gt;&lt;p&gt;Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 30 Dec 2025 18:31:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011693</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; nanocomposites</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048285?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048285</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049035?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049035</guid></item><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v1 Announce Type: new 
 Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22277v1</guid></item><item><title>[cond-mat updates on arXiv.org] The Role of THz Phonons in the Ionic Conduction Mechanism of $Li_7La_3Zr_2O_{12}$ Polymorphs</title><link>https://arxiv.org/abs/2512.22427</link><description>arXiv:2512.22427v1 Announce Type: new 
 Abstract: Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate superionic conduction. However, direct experimental measurement of the role of phonons in ionic conduction is challenging due to the fast intrinsic timescales of ion hopping and the difficulty of driving relevant phonon modes, which often lie in the low-energy THz regime. To overcome these limitations, we use laser-driven ultrafast impedance spectroscopy (LUIS). LUIS resonantly excites phonons using a THz field and probes ion hopping with picosecond time resolution. We apply LUIS to understand the dynamical role of phonons in $Li_7La_3Zr_2O_{12}$ (LLZO). When in its cubic phase (c-LLZO), this garnet-type solid electrolyte has an ionic conductivity two orders of magnitude greater than its tetragonal phase (t-LLZO). T-LLZO is characterized by an ordered and filled $Li^+$ sublattice necessitating synchronous ion hopping. In contrast, c-LLZO is characterized by a disordered and vacancy-rich $Li^+$ sublattice, and has a conduction mechanism dominated by single hops. We find that, upon excitation of phonons in the 0.5-7.5 THz range, the impedance of t-LLZO experiences a longer ion hopping decay signal in comparison to c-LLZO. The results suggest that phonon-mediated ionic conduction by THz modes may lead to larger ion displacements in ordered and fully occupied mobile ion sublattices as opposed to those that are disordered and vacancy-rich. Overall, this work highlights the interplay between static and dynamic factors that enables improved ionic conductivity in otherwise poorly conducting inorganic solids.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22427v1</guid></item><item><title>[cond-mat updates on arXiv.org] Thermally Activated Non-Affine Rearrangements in Amorphous Glass: Emergence of Intrinsic Length Scales</title><link>https://arxiv.org/abs/2512.22530</link><description>arXiv:2512.22530v1 Announce Type: new 
 Abstract: We present a systematic study of temperature-driven nonaffine rearrangements in a model amorphous solid across the full thermodynamic range, from a high-temperature liquid, through supercooled and sub-glass regimes, into deep glassy states. The central result is a quantitative characterisation of the componentwise nonaffine residual displacements, obtained by subtracting local affine maps from particle displacements. For each state point the tails of the probability distributions of these nonaffine components display clear exponential decay; linear fits to the logarithm of the tail region yield characteristic nonaffine length scales {\xi}NA,x and {\xi}NA,y , which quantify the spatial extent of purely nonaffine, local rearrangements. To compare with other length scales, we compute van Hove distributions Gx(ux), Gy (uy ) which capture the full particle displacement field (coherent affine-like motion plus residuals). A robust, key finding is that the van Hove length scale consistently exceeds the filtered nonaffine length scale, i.e. {\xi}VH &gt; {\xi}NA, across all temperatures, state points, and densities we studied. The nonaffine length {\xi}NA quantifies the distance over which complex deformation occurs, specifically nonlinear and anharmonic responses, irreversible (plastic) rearrangements, topological non-recoverable particle rearrangements, and other residual motions that cannot be represented by a local affine map. Moreover, near equality of {\xi}NA,x and {\xi}NA,y in all conditions provides further evidence that nonaffine rearrangements propagate isotropically under thermally driven deformation in contrast to externally driven shear.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22530v1</guid></item><item><title>[cond-mat updates on arXiv.org] Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms</title><link>https://arxiv.org/abs/2512.22934</link><description>arXiv:2512.22934v1 Announce Type: new 
@@ -14,7 +14,7 @@ Abstract: We study the replica-symmetric saddle point equations for the Ising pe
 Abstract: The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS (https://simplysqs.com), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generates all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb1-xSrxTiO3 (PSTO, including PbTiO3 (PTO) and SrTiO3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS, after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r2SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x = 0.5, with lattice parameters deviating by less than 1 % in the cubic region (x &gt; 0.5) and less than 4 % in the tetragonal region (x &lt; 0.5). Overall, SimplySQS transforms SQS generation with ATAT into intuitive, reproducible, and systematic framework for modeling disordered materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.18020v3</guid></item><item><title>[cond-mat updates on arXiv.org] Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems</title><link>https://arxiv.org/abs/2512.20230</link><description>arXiv:2512.20230v2 Announce Type: replace 
 Abstract: The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20230v2</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets</title><link>https://arxiv.org/abs/2512.21049</link><description>arXiv:2512.21049v2 Announce Type: replace 
 Abstract: Friedel oscillations are spatially decaying density modulations near localized defects and are a hallmark of quantum systems. Walking droplets provide a macroscopic platform for hydrodynamic quantum analogs, and Friedel-like oscillations were recently observed in droplet-defect scattering through wave-mediated speed modulation [P.~J.~S\'aenz \textit{et al.}, \textit{Sci.\ Adv.} \textbf{6}, eay9234 (2020)]. Here we show that Friedel-like oscillatory statistics can also arise from a purely local dynamical mechanism, revealed using a minimal Lorenz model description of a walking droplet viewed as an active particle with internal degrees of freedom. A localized defect directly perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This work opens new avenues for hydrodynamic quantum analogs by revealing how quantum-like statistics can emerge from local internal-state dynamics of active particles.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21049v2</guid></item><item><title>[cond-mat updates on arXiv.org] Functional Renormalization Group flows as diffusive Hamilton-Jacobi-type equations</title><link>https://arxiv.org/abs/2512.05973</link><description>arXiv:2512.05973v2 Announce Type: replace-cross 
-Abstract: In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of field-dependent flow equations as nonlinear partial differential equations. We demonstrate this novel approach by first applying it to a simple fermion-boson system in zero spacetime dimensions - which itself presents as an interesting playground for method development. Afterwards, we show, how the gained insights can be transferred to more interesting problems: One is the bosonic $\mathbb{Z}_2$-symmetric model in three Euclidean dimensions within a truncation that involves the field-dependent effective potential and field-dependent wave-function renormalization. The other example is the $(1 + 1)$-dimensional Gross-Neveu model within a truncation that involves a field-dependent potential and a field-dependent fermion mass/Yukawa coupling at nonzero temperature, chemical potential, and finite fermion number.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.05973v2</guid></item><item><title>[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</link><description>Coarse-grained molecular dynamics enables access to long length and time scales but often fails to reproduce atomistic kinetics when memory effects and slow collective motions are important. We introduce Probabilistic Forecasting for Coarse-Graining (PFCG), a machine learning framework that learns stochastic coarse-grained equations of motion directly from atomistic trajectories by formulating coarse-grained simulation as a probabilistic time-series forecasting problem with both Markovian and non-Markovian contributions. PFCG incorporates non-Markovian effects through finite trajectory history without requiring explicit memory kernels or learned effective potentials. We apply PFCG to miniproteins and polyalanine peptides and evaluate both configurational and dynamical fidelity using free energy surfaces, autocorrelation functions, and transition timescales from Markov state models. Across all systems, non-Markovian PFCG models significantly improve dynamical agreement with atomistic simulation relative to Markovian baselines while also maintaining excellent agreement with stationary distributions. These results highlight the importance of inductive biases at the level of equations of motion and establish PFCG as a complementary approach to existing machine learning-based coarse-graining methods for modeling biomolecular processes.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</guid></item><item><title>[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods</title><link>https://www.nature.com/articles/s41524-025-01918-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 30 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01918-6"&gt;doi:10.1038/s41524-025-01918-6&lt;/a&gt;&lt;/p&gt;Toward high entropy material discovery for energy applications using computational and machine learning methods</description><author>npj Computational Materials</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01918-6</guid></item><item><title>[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</link><description>Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Hybrid ChemBERTa and DFT Machine Learning Framework for Predicting Enantioselectivity in Organosilanes Mediated Carbonyl Reduction Reactions</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</link><description>Predicting the small yet meaningful enantioselectivity differences in organosilanes mediated carbonyl reductions remains challenging because multiple steric, electronic and conformational factors interact in ways that traditional mechanistic rules struggle to describe. To address this challenge, this study integrates quantum chemical de- scriptors with ChemBERTa based molecular embeddings to construct a machine learn- ing framework capable of capturing these subtle structure selectivity relationships. A systematic model comparison was performed using a carefully curated dataset, where LightGBM demonstrated the highest predictive accuracy with RMSE value of 8.381 for %ee. SHAP based interpretability analysis clarified which steric, electronic and geometrical descriptors most strongly influence facial selectivity across these reduc- tions. Together, this hybrid computational approach provides both predictive power and mechanistic insight offering a practical tool for understanding selectivity trends.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09186A, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang&lt;br /&gt;As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202517851?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202517851</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515555?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515555</guid></item><item><title>[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507018?af=R</link><description>Small, Volume 21, Issue 52, December 29, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 20:38:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507018</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.71868?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.71868</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202412757?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202412757</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 29 Dec 2025 18:30:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Small: Table of Contents] Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509479?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 08:31:34 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509479</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[cond-mat updates on arXiv.org] Upper bounds on the separation efficiency of diffusiophoresis</title><link>https://arxiv.org/abs/2512.21758</link><description>arXiv:2512.21758v1 Announce Type: new 
+Abstract: In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of field-dependent flow equations as nonlinear partial differential equations. We demonstrate this novel approach by first applying it to a simple fermion-boson system in zero spacetime dimensions - which itself presents as an interesting playground for method development. Afterwards, we show, how the gained insights can be transferred to more interesting problems: One is the bosonic $\mathbb{Z}_2$-symmetric model in three Euclidean dimensions within a truncation that involves the field-dependent effective potential and field-dependent wave-function renormalization. The other example is the $(1 + 1)$-dimensional Gross-Neveu model within a truncation that involves a field-dependent potential and a field-dependent fermion mass/Yukawa coupling at nonzero temperature, chemical potential, and finite fermion number.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.05973v2</guid></item><item><title>[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</link><description>Coarse-grained molecular dynamics enables access to long length and time scales but often fails to reproduce atomistic kinetics when memory effects and slow collective motions are important. We introduce Probabilistic Forecasting for Coarse-Graining (PFCG), a machine learning framework that learns stochastic coarse-grained equations of motion directly from atomistic trajectories by formulating coarse-grained simulation as a probabilistic time-series forecasting problem with both Markovian and non-Markovian contributions. PFCG incorporates non-Markovian effects through finite trajectory history without requiring explicit memory kernels or learned effective potentials. We apply PFCG to miniproteins and polyalanine peptides and evaluate both configurational and dynamical fidelity using free energy surfaces, autocorrelation functions, and transition timescales from Markov state models. Across all systems, non-Markovian PFCG models significantly improve dynamical agreement with atomistic simulation relative to Markovian baselines while also maintaining excellent agreement with stationary distributions. These results highlight the importance of inductive biases at the level of equations of motion and establish PFCG as a complementary approach to existing machine learning-based coarse-graining methods for modeling biomolecular processes.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</guid></item><item><title>[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods</title><link>https://www.nature.com/articles/s41524-025-01918-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 30 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01918-6"&gt;doi:10.1038/s41524-025-01918-6&lt;/a&gt;&lt;/p&gt;Toward high entropy material discovery for energy applications using computational and machine learning methods</description><author>npj Computational Materials</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01918-6</guid></item><item><title>[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</link><description>Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Hybrid ChemBERTa and DFT Machine Learning Framework for Predicting Enantioselectivity in Organosilanes Mediated Carbonyl Reduction Reactions</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</link><description>Predicting the small yet meaningful enantioselectivity differences in organosilanes mediated carbonyl reductions remains challenging because multiple steric, electronic and conformational factors interact in ways that traditional mechanistic rules struggle to describe. To address this challenge, this study integrates quantum chemical de- scriptors with ChemBERTa based molecular embeddings to construct a machine learn- ing framework capable of capturing these subtle structure selectivity relationships. A systematic model comparison was performed using a carefully curated dataset, where LightGBM demonstrated the highest predictive accuracy with RMSE value of 8.381 for %ee. SHAP based interpretability analysis clarified which steric, electronic and geometrical descriptors most strongly influence facial selectivity across these reduc- tions. Together, this hybrid computational approach provides both predictive power and mechanistic insight offering a practical tool for understanding selectivity trends.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09186A, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang&lt;br /&gt;As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202517851?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202517851</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515555?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515555</guid></item><item><title>[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507018?af=R</link><description>Small, Volume 21, Issue 52, December 29, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 20:38:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507018</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.71868?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.71868</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202412757?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202412757</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03415</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03415/asset/images/medium/nz5c03415_0017.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c03415&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Mon, 29 Dec 2025 19:20:24 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03415</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 29 Dec 2025 18:30:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Small: Table of Contents] Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509479?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 08:31:34 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509479</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[cond-mat updates on arXiv.org] Upper bounds on the separation efficiency of diffusiophoresis</title><link>https://arxiv.org/abs/2512.21758</link><description>arXiv:2512.21758v1 Announce Type: new 
 Abstract: The separation of colloidal particles from fluids is essential to ensure a safe global supply of drinking water yet, in the case of microscopic particles, it remains a highly energy-intensive process when using traditional filtration methods. Water cleaning through diffusiophoresis $\unicode{x2014}$spontaneous colloid migration in chemical gradients$\unicode{x2014}$ effectively circumvents the need for physical filters, representing a promising alternative. This separation process is typically realized in internal flows where a cross-channel electrolyte gradient drives particle accumulation at walls, with colloid separation slowly increasing in the streamwise direction. However, the maximum separation efficiency, achieved sufficiently downstream as diffusiophoretic migration (driving particle accumulation) is balanced by Brownian motion (inducing diffusive spreading), has not yet been characterized. In this work, we develop a theory to predict this upper bound, and derive the colloid separation efficiency by analyzing the asymptotic structure of the governing equations. We find that the mechanism by which the chemical permeates in the channel, as well as the reaction kinetics governing its dissociation into ions, play key roles in the process. Moreover, we identify four distinct regimes in which separation is controlled by different scaling laws involving a Damk\"ohler and a P\'eclet number, which measure the ratio of reaction kinetics to ion diffusion and of diffusiophoresis to Brownian motion, respectively. We also confirm the scaling of one of these regimes using microfluidic experiments where separation is driven by CO$_\text{2}$ gradients. Our results shed light on pathways towards new, more efficient separations, and are also applicable to quantify colloidal accumulation in the presence of chemical gradients in more general situations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21758v1</guid></item><item><title>[cond-mat updates on arXiv.org] Temperature- and Pressure-Dependent Vibrational Properties and Phase Stability of Pristine and Sb-Doped Vacancy-Ordered Double Perovskite</title><link>https://arxiv.org/abs/2512.21810</link><description>arXiv:2512.21810v1 Announce Type: new 
 Abstract: Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulating these properties remains poorly understood. We investigate Sb-doped Cs$_2$TiCl$_6$ through temperature-dependent Raman spectroscopy (4 to 273 K), high-pressure studies (0 to 30 GPa), powder XRD, and photoluminescence measurements. Sb doping dramatically improves phase purity, eliminating all impurity-related Raman modes present in pristine and Bi-doped samples while retaining only the three fundamental [TiCl$_6$]$^{2-}$ octahedral vibrations. This enhanced purity reveals a previously unobserved structural phenomenon: Sb-doped samples (2\% doped and 3\%) incorporated) exhibit a sharp anomaly at 100 K marked by the emergence of a new Raman mode M$_1$ at 314--319 cm$^{-1}$ and abrupt changes in the temperature coefficient $\chi$ (factor of 2--8$\times$ change) and anharmonic constant $A$ across this threshold. No such transition occurs in pristine Cs$_2$TiCl$_6$, indicating Sb-dopant-induced order-disorder transformation. The enhanced phonon anharmonicity in Sb-doped samples directly manifests in photoluminescence: self-trapped exciton emission at 448 nm shows 19\% broader FWHM (164.73 nm) compared to Bi-doped samples (138.2 nm), confirming stronger electron-phonon coupling. High-pressure measurements reveal structural robustness to 30 GPa with no phase transitions. These findings establish that strategic Sb doping not only improves material quality but also enables a novel low-temperature structural transition, providing fundamental insights into dopant-mediated phase control in vacancy-ordered perovskites for next-generation optoelectronic devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21810v1</guid></item><item><title>[cond-mat updates on arXiv.org] Incorporating rank-free coupling and external field via an amplitude-only modulated spatial photonic Ising machine</title><link>https://arxiv.org/abs/2512.21587</link><description>arXiv:2512.21587v1 Announce Type: cross 
 Abstract: Ising machines have emerged as effective solvers for combinatorial optimization problems, such as NP-hard problems, machine learning, and financial modeling. Recent spatial photonic Ising machines (SPIMs) excel in multi-node optimization and spin glass simulations, leveraging their large-scale and fully connected characteristics. However, existing laser diffraction-based SPIMs usually sacrifice time efficiency or spin count to encode high-rank spin-spin coupling and external fields, limiting their scalability for real-world applications. Here, we demonstrate an amplitude-only modulated rank-free spatial photonic Ising machine (AR-SPIM) with 200 iterations per second. By re-formulating an arbitrary Ising Hamiltonian as the sum of Hadamard products, followed by loading the corresponding matrices/vectors onto an aligned amplitude spatial light modulator and digital micro-mirrors device, we directly map a 797-spin Ising model with external fields (nearly 9-bit precision, -255 to 255) into an incoherent light field, eliminating the need for repeated and auxiliary operations. Serving as encoding accuracy metrics, the linear coefficient of determination and Pearson correlation coefficient between measured light intensities and Ising Hamiltonians exceed 0.9800, with values exceed 0.9997 globally. The AR-SPIM achieves less than 0.3% error rate for ground-state search of biased Max-cut problems with arbitrary ranks and weights, enables complex phase transition observations, and facilitates scalable spin counts for sparse Ising problems via removing zero-valued Hadamard product terms. This reconfigurable AR-SPIM can be further developed to support large-scale machine-learning training and deployed for practical applications in discrete optimization and quantum many-body simulations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21587v1</guid></item><item><title>[cond-mat updates on arXiv.org] Accelerating Scientific Discovery with Autonomous Goal-evolving Agents</title><link>https://arxiv.org/abs/2512.21782</link><description>arXiv:2512.21782v1 Announce Type: cross