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-<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>Thu, 25 Dec 2025 01:40:29 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating Machine Learning with Electrochemical Sensors for Intelligent Food Safety Monitoring</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000223?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry&lt;/p&gt;&lt;p&gt;Author(s): Aaryashree, Arti Devi&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Wed, 24 Dec 2025 18:29:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000223</guid></item><item><title>[ScienceDirect Publication: Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004453?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Wed, 24 Dec 2025 18:29:19 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004453</guid></item><item><title>[cond-mat updates on arXiv.org] Turing Pattern Engineering Enables Kinetically Ultrastable yet Ductile Metallic Glasses</title><link>https://arxiv.org/abs/2512.20196</link><description>arXiv:2512.20196v1 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>Thu, 25 Dec 2025 06:33:16 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[cond-mat updates on arXiv.org] Towards predictive atomistic simulations of SiC crystal growth</title><link>https://arxiv.org/abs/2512.20804</link><description>arXiv:2512.20804v1 Announce Type: new 
+Abstract: Simulations of SiC crystal growth using molecular dynamics (MD) have become popular in recent years. They, however, simulate very fast deposition rates, to reduce computational costs. Therefore, they are more akin to surface sputtering, leading to abnormal growth effects, including thick amorphous layers and large defect densities. A recently developed method, called the minimum energy atomic deposition (MEAD), tries to overcome this problem by depositing the atoms directly at the minimum energy positions, increasing the time scale.
+  We apply the MEAD method to simulate SiC crystal growth on stepped C-terminated 4H substrates with 4{\deg} and 8{\deg} off-cut angle. We explore relevant calculations settings, such as amount of equilibration steps between depositions and influence of simulation cell sizes and bench mark different interatomic potentials. The carefully calibrated methodology is able to replicate the stable step-flow growth, which was so far not possible using conventional MD simulations. Furthermore, the simulated crystals are evaluated in terms of their dislocations, surface roughness and atom mobility. Our methodology paves the way for future high fidelity investigations of surface phenomena in crystal growth.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20804v1</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:2512.21049v1 Announce Type: new 
+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 experiments 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 statistics can also arise from a purely local, dynamical mechanism, which we elucidate using a minimal Lorenz-like model of a walking droplet. In this model, a localized defect perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This attractor-driven, local mechanism opens new avenues for hydrodynamic quantum analogs based on active particles with internal degrees of freedom.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21049v1</guid></item><item><title>[cond-mat updates on arXiv.org] From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning</title><link>https://arxiv.org/abs/2512.21067</link><description>arXiv:2512.21067v1 Announce Type: new 
+Abstract: The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods, the exploration of the continuous evolution of clusters towards nanocrystal is still a big challenge. Here, we proposed a machine learning force field (MLFF) that can generalize well to various copper systems ranging from small clusters to large clusters and bulk. The continuous evolution of copper clusters CuN towards nanocrystal was revealed by investigating clusters in a wide size range (7 &lt;= N &lt;= 17885) based on MLFF simulated annealing. For small CuN (N &lt; 40), electron counting rule plays a major role in stability. For large CuN (N &gt; 80), geometric magic number rule plays a dominant role and the evolution of clusters is based on the formation of more and more icosahedral shells. For medium size CuN (40 &lt;= N &lt;= 80), both rules contribute. The critical size from cluster to nanocrystal was calculated to be around 8000 atoms (about 6 nm in diameter). Our work terminates the long-term challenge in nanoscience, and lay the methodological foundation for subsequent research on other cluster systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21067v1</guid></item><item><title>[cond-mat updates on arXiv.org] Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity</title><link>https://arxiv.org/abs/2512.21077</link><description>arXiv:2512.21077v1 Announce Type: new 
+Abstract: Two-dimensional (2D) materials are promising candidates for next-generation spintronic devices due to their tunable properties and potential for efficient spin-charge interconversion. However, discovering materials with intrinsically high spin Hall conductivity (SHC) is hindered by the vast chemical space and expensive nature of conventional experimental and first-principles methods. In this work, we employ an active learning framework to accelerate the discovery of high-SHC 2D materials. Machine learning (ML) models were trained on SHC values computed from density functional theory calculations, incorporating the Kubo formalism via tight-binding Hamiltonians constructed from maximally localized Wannier functions, with explicit treatment of spin-orbit coupling. Starting from random but chemically diverse 24 2D systems, the dataset was expanded to 41 cases (from an overall pool of around 2000 materials) over three active learning loops using an expected improvement acquisition strategy. The ML technique successfully identified several high SHC candidates with the best candidate exhibiting a SHC of 271.52 (hbar/e) Ohm^-1, nearly 23 times higher than the top performer in the initial round. Beyond candidate discovery, several features such as orbital symmetry near the Fermi energy, types of atomic species, material composition, covalent radii, and electronegativity of constituent atoms were found to play critical role in shaping the spin Hall response in 2D systems. The data generated is made publicly available to facilitate further advances in 2D spintronics.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21077v1</guid></item><item><title>[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials</title><link>https://arxiv.org/abs/2512.20785</link><description>arXiv:2512.20785v1 Announce Type: cross 
+Abstract: Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20785v1</guid></item><item><title>[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models</title><link>https://arxiv.org/abs/2512.21231</link><description>arXiv:2512.21231v1 Announce Type: cross 
+Abstract: Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21231v1</guid></item><item><title>[cond-mat updates on arXiv.org] Dislocation-mediated short-range order evolution during thermomechanical processing</title><link>https://arxiv.org/abs/2508.13484</link><description>arXiv:2508.13484v2 Announce Type: replace 
+Abstract: Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al., 2025) showed that the same dislocation motion both creates and destroys chemical short-range order (SRO), driving alloys into far-from-equilibrium SRO states. However, the connection between this dislocation-mediated SRO evolution and processing parameters remains largely unexplored. Here, we perform large-scale atomistic simulations of thermomechanical processing of equiatomic TiTaVW to determine how temperature and strain rate control SRO via competing creation ($\Gamma$) and annihilation ($\lambda$) rates. The simulations employ systems containing 2.4 million atoms and utilize a machine learning interatomic potential optimized to capture chemical complexity through the motif-based sampling technique. Using information-theoretic metrics, we quantify that the magnitude and chemical character of SRO vary systematically with processing parameters. We identify two regimes: a low-temperature regime with weak strain-rate sensitivity, and a high-temperature regime in which reduced dislocation density and increased screw character amplify chemical bias and accelerate SRO formation. The resulting steady-state SRO is far-from-equilibrium and cannot be produced by equilibrium thermal annealing. Together, these results provide a mechanistic and predictive link between processing parameters, dislocation physics, and SRO evolution in chemically complex alloys.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.13484v2</guid></item><item><title>[cond-mat updates on arXiv.org] Moir\'e spintronics: Emergent phenomena, material realization and machine learning accelerating discovery</title><link>https://arxiv.org/abs/2509.04045</link><description>arXiv:2509.04045v2 Announce Type: replace 
+Abstract: Twisted van der Waals (vdW) materials have emerged as a promising platform for exploring exotic quantum phenomena and engineering novel material properties in two dimensions, potentially revolutionizing developments in spintronics. This Review provides an overview of recent progress on emerging moir\'e spintronics in twisted vdW materials, with a particular focus on two-dimensional magnetic materials. Following a brief introduction to the general features of twisted vdW materials, we discuss recent theoretical and experimental studies on stacking-dependent interlayer magnetism, non-collinear spin textures, moir\'e magnetic exchange interactions, moir\'e skyrmions and moir\'e magnons. We further highlight the potential of machine learning to accelerate the discovery and design of multifunctional materials for moir\'e spintronics. Finally, we conclude by addressing the most pressing challenges and potential opportunities in this rapidly expanding field.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.04045v2</guid></item><item><title>[Nature Communications] Machine learning-assisted kinetic matching model for rational electrode design in aqueous zinc-ion batteries</title><link>https://www.nature.com/articles/s41467-025-67996-8</link><description>&lt;p&gt;Nature Communications, Published online: 25 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-67996-8"&gt;doi:10.1038/s41467-025-67996-8&lt;/a&gt;&lt;/p&gt;Aqueous zinc-ion batteries are safe and affordable but limited by incompatible electrode kinetics. Here, authors present a machine learning framework that resolves this mismatch, enabling the rational design of durable and stretchable zinc-ion batteries.</description><author>Nature Communications</author><pubDate>Thu, 25 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67996-8</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating Machine Learning with Electrochemical Sensors for Intelligent Food Safety Monitoring</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000223?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry&lt;/p&gt;&lt;p&gt;Author(s): Aaryashree, Arti Devi&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Wed, 24 Dec 2025 18:29:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000223</guid></item><item><title>[ScienceDirect Publication: Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004453?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Wed, 24 Dec 2025 18:29:19 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004453</guid></item><item><title>[cond-mat updates on arXiv.org] Turing Pattern Engineering Enables Kinetically Ultrastable yet Ductile Metallic Glasses</title><link>https://arxiv.org/abs/2512.20196</link><description>arXiv:2512.20196v1 Announce Type: new 
 Abstract: Enhancing the kinetic stability of glasses often necessitates deepening thermodynamic stability, which typically compromises ductility due to increased structural rigidity. Decoupling these properties remains a critical challenge for functional applications. Here, we demonstrate that pattern engineering in metallic glasses (MGs) enables unprecedented kinetic ultrastability while retaining thermodynamic metastability and intrinsic plasticity. Through atomistic simulations guided by machine-learning interatomic potentials and replica-exchange molecular dynamics, we reveal that clustering oxygen contents, driven by reaction-diffusion-coupled pattern dynamics, act as localized pinning sites. These motifs drastically slow structural relaxation, yielding kinetic stability comparable to crystal-like ultrastable glasses while retaining an energetic as-cast state. Remarkably, the thermodynamically metastable state preserves heterogeneous atomic mobility, allowing strain delocalization under mechanical stress. By tailoring oxygen modulation via geometric patterning, we achieve an approximately 200 K increase in the onset temperature of the glass transition (Tonset) while maintaining fracture toughness akin to conventional MGs. This work establishes a paradigm of kinetic stabilization without thermodynamic compromise, offering a roadmap to additively manufacture bulk amorphous materials with combined hyperstability and plasticity.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20196v1</guid></item><item><title>[cond-mat updates on arXiv.org] Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset</title><link>https://arxiv.org/abs/2512.20228</link><description>arXiv:2512.20228v1 Announce Type: new 
 Abstract: We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20228v1</guid></item><item><title>[cond-mat updates on arXiv.org] Benchmarking Universal Interatomic Potentials on Elemental Systems</title><link>https://arxiv.org/abs/2512.20230</link><description>arXiv:2512.20230v1 Announce Type: new 
 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>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20230v1</guid></item><item><title>[cond-mat updates on arXiv.org] Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics</title><link>https://arxiv.org/abs/2512.20424</link><description>arXiv:2512.20424v1 Announce Type: new