jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Annika Stellhorn | Juan G. C. Palma | Alicia Backs | Anders Bergman | A. B. Klautau | Emmanuel Kentzinger | C. Bednarski-Meinke | Steffen Tober | E. Blackburn | Juri Barthel | Nina‐Juliane Steinke | Helena M. Petrilli | I. P. Miranda
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Disorder-induced chirality in superconductor-ferromagnet heterostructures revealed by neutron scattering and multiscale modeling Chirality in superconductor-ferromagnet hybrids strongly influences phenomena such as the observable signatures of long-range triplet superconductivity, but its microscopic origin in nominally centrosymmetric ferromagnets is still unclear. Here, we combine structural characterization, polarization-analyzed grazing-incidence small-angle neutron scattering (PA-GISANS), first-principles calculations, and deep-learning-assisted multiscale modeling to study FePd and Nb/FePd heterostructures. Experimentally, we observe partial L1$_0$ order, atomic intermixing, anti-phase boundaries, and a depth-dependent defect gradient across the FePd layer, together with a finite net magnetic chirality at room temperature. The GISANS asymmetry indicates that the main chiral contribution lies in-plane, with an additional out-of-plane component associated with depth-dependent magnetic inhomogeneity. Theoretically, we show that chemical disorder in FePd, especially when combined with a compositional gradient, produces finite Dzyaloshinskii-Moriya interactions and stabilizes chiral finite-$\mathbf{q}$ magnetic modulations with mixed Bloch-Néel character. In the mesoscopic model, the resulting in-plane modulation length approaches the experimentally observed range. These results identify disorder and compositional gradients as intrinsic microscopic sources of net chirality in FePd-based films, showing that the observed chirality does not arise only from interface effects. Chirality (physics) ; Mesoscopic physics ; Asymmetry ; Neutron scattering ; Ferromagnetism ; Multiscale modeling ; Scattering Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Magnetic properties of thin films
Authors: Lei Zhu | Yubin Liu | Liang Dong | Xumin Ding
Source: Physica Scripta (article, 2026-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A dual-branch transformer architecture for inverse design of reconfigurable metasurface Abstract Metasurfaces enable advanced multifunctional applications by controlling electromagnetic (EM) wavefronts. While deep learning has aided EM design, existing methods struggle with global spectral features and discontinuous phase transitions and require large datasets. To address these limitations, we propose a Transformer-based framework that captures long-range spectral correlations and integrates physics-informed adaptive weighting to perform well under data-scarce conditions. Our framework includes two parts: a hybrid Transformer-Gated Recurrent Unit (GRU) forward prediction network and a Mirror Dual-Branch Transformer (MDT) inverse design network. The forward network maps structural parameters to EM responses via adaptive weighting that emphasizes abrupt phase transitions. The inverse network uses an innovative MDT architecture with three modules: polarization decoupling, phase-sensitive attention, and a spectral mirror branch, which decouple polarization features, refine spectral representation, and improve modeling accuracy. We validate the approach with a vanadium dioxide (VO 2 ) reconfigurable holographic metasurface. Results show that MDT reduces design loss by 45.5% compared to conventional Transformer methods, notably improving accuracy under data-scarce conditions. The method moves beyond static design, offering a scalable framework for phase-change material control, supporting temperature-responsive modulation and secure information encoding. Inverse ; Weighting ; Transformer ; Scalability ; Holography ; Architecture ; Embedding ; Polarization (electrochemistry) Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: I Made Arie Widyasthana Wartana Putra | Ririn Ristovani | Ade  Heri Mulyati | Eti Rohaeti | Anang Kurnia | Mohamad Rafi | Utami Dyah Syafitri
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Modified Cuckoo Search Algorithm for Optimizing Hyperparameters in Bagging and Boosting Models for Identification and Authentication of Strobilanthes crispa from Piper aduncum Boosting (machine learning) ; Gradient boosting ; Pattern recognition (psychology) ; Preprocessor ; Data pre-processing ; Linear discriminant analysis ; Cuckoo search Spectroscopy and Chemometric Analyses ; Piperaceae Chemical and Biological Studies ; Advanced Chemical Sensor Technologies
Authors: Yongjoon Choe | Sungwon Kim | Susan E. Burns | Chanyoung Park
Source: Water Research (article, 2026-03-21)
Field: Chemistry — Physical and Theoretical Chemistry

Physics-embedded graph neural operator for interaction-controlled colloidal aggregation Colloidal aggregation in natural and engineered waters is a complex function of both particle and groundwater electrochemical conditions, yet predicting aggregation behavior across diffusion-limited and reaction-limited regimes remains challenging due to the nonlinear dependence of collision efficiency on ionic strength and surface potential. This study presents a graph neural operator surrogate model that captures particle aggregation dynamics by embedding transport physics directly into the network architecture. Particle size classes are represented as graph nodes with Brownian collision kernels encoded in edge features, while attention mechanisms conditioned on ionic strength and zeta potential learn collision efficiency variations governed by extended DLVO interactions. The proposed model predicts aggregation kinetics across a range of electrochemical conditions, achieving R<sup>2</sup> > 0.99 for both held-out parameter combinations and temporal extrapolation, and outperforming baseline models that do not embed physics in the architecture, as well as loss-based, physics-regularized neural networks. Validation against experimental measurements from bacteriophage, polystyrene, and cerium oxide systems confirms reproduction of aggregation regime transitions and kinetic saturation. Attention analysis reveals physically consistent reorganization from multiple information pathways under strong electrostatic repulsion to integrated processing under weak repulsion. This framework enables rapid, physically grounded exploration of colloidal aggregation relevant to water quality prediction and treatment optimization. Agrégation ; Graph ; Operator (biology) ; Artificial neural network ; Graph theory ; Colloid Electrostatics and Colloid Interactions ; Pickering emulsions and particle stabilization ; Polymer Surface Interaction Studies
Authors: Philipp Weigand | Nikolas Ebert | Shad A. Mohammed | Denis Abu Sammour | Carsten Hopf | Oliver Wasenmüller
Source: arXiv (Cornell University) (preprint, 2026-03-11)
Field: Chemistry — Spectroscopy

Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging Mass spectrometry imaging (MSI) enables label-free visualization of molecular distributions across tissue samples but generates large and complex datasets that require effective peak picking to reduce data size while preserving meaningful biological information. Existing peak picking approaches perform inconsistently across heterogeneous datasets, and their evaluation is often limited to synthetic data or manually selected ion images that do not fully represent real-world challenges in MSI. To address these limitations, we propose an autoencoder-based spatial self-supervised peak learning neural network that selects spatially structured peaks by learning an attention mask leveraging both spatial and spectral information. We further introduce an evaluation procedure based on expert-annotated segmentation masks, allowing a more representative and spatially grounded assessment of peak picking performance. We evaluate our approach on four diverse public MSI datasets using our proposed evaluation procedure. Our approach consistently outperforms state-of-the-art peak picking methods by selecting spatially structured peaks, thus demonstrating its efficacy. These results highlight the value of our spatial self-supervised network in comparison to contemporary state-of-the-art methods. The evaluation procedure can be readily applied to new MSI datasets, thereby providing a consistent and robust framework for the comparison of spatially structured peak picking methods across different datasets. Pattern recognition (psychology) ; Artificial neural network ; Segmentation ; Visualization ; Spatial analysis Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Proteomics Techniques and Applications
Authors: Lewis J. Conway | Chris J. Pickard
Source: Annalen der Physik (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Accelerating Crystal Structure Prediction Using Data‐Derived Potentials: High‐Pressure Binary Hydrides ABSTRACT Crystal structures can be predicted from first‐principles using ab initio random structure searching ( AIRSS ) to sample the potential energy landscape, and density functional theory (DFT) to robustly and accurately describe it. While classical interatomic potentials lower computational costs at the expense of robustness and accuracy, modern machine‐learning potentials offer a compromise, providing both at a fraction of the DFT cost. In this work, we use Ephemeral Data‐Derived Potentials ( EDDP s) to accelerate AIRSS calculations for binary hydrides at 100 GPa. Since the training data is generated iteratively using AIRSS , the searches require no prior knowledge of hydrides. These potentials allow more diverse searches, sampling diverse compositions, larger unit cells, and many more structures. Besides recovering known structures, the searches reveal hydrogen‐rich phases of H 22 (BrH), H 23 Pb, and H 32 Mg, supermolecular phases of H 25 Cs and H 26 Rn, and many ‘substoichiometric’ variants of known hydrides. Our results indicate that using the current generation of universal MLIPs to search for novel high‐pressure hydrides is less effective due to model instabilities or markedly slower inference speeds and highlight the necessity of generating new, targeted data. Binary number ; Robustness (evolution) ; Crystal structure prediction ; Density functional theory ; Ab initio ; Crystal structure ; Inference ; Interatomic potential Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Inorganic Chemistry and Materials
Authors: Wubshet Getachew Mengesha
Source: Discover Materials (article, 2026-01-29)
Field: Materials Science — Biomaterials

Optimization of magnetic nanomaterials for enhanced medical imaging and hyperthermia Magnetic nanomaterials (MNMs) have emerged as a transformative platform in nanomedicine, enabling integrated theranostic applications that combine medical imaging and hyperthermia therapy. Their unique properties, such as superparamagnetism and biocompatibility, are crucial for enhancing diagnostic accuracy and targeted cancer treatment. However, the field faces significant challenges, including the difficulty of optimizing multiple interdependent parameters to simultaneously maximize imaging contrast and heating efficiency, alongside issues of synthesis reproducibility, biocompatibility, and clinical translation. This paper addresses these gaps by providing a comprehensive analysis of the strategic optimization of MNMs through a critical review of recent advances. We demonstrate that advanced architectures—such as core–shell structures, doping, and defect engineering—can dramatically enhance performance, yielding specific absorption rate (SAR) values exceeding 950 W/g for hyperthermia and transverse relaxivities (r2) greater than 400 mM−1 s−1 for MRI contrast, substantially outperforming conventional spherical iron oxide nanoparticles. Furthermore, we highlight that artificial intelligence (AI) and machine learning are pivotal in accelerating the data-driven design of high-performance MNMs, predicting optimal properties, and personalizing treatment parameters, reducing development cycles by up to 40%. The implications of this paper are substantial, offering a coherent theoretical framework and a strategic roadmap for developing next-generation theranostic agents. By synthesizing current insights and proposing actionable recommendations, this work bridges critical gaps between laboratory innovation and clinical application, paving the way for more effective, personalized cancer therapies and advancing the field of precision oncology. Nanomedicine ; Medical imaging ; Clinical Practice ; Nanomaterials ; Cancer therapy ; Transformative learning ; Magnetic hyperthermia Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Micro and Nano Robotics
Authors: Hang Lv | Xihui Bian | Yuan Gao | Meisong Wang | Haiyue Wu | Zhongxin Yan | Yang XIANG
Source: Journal of Food Quality (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Adulteration Detection of Yak Meat Based on Near‐Infrared Spectroscopy and Chemometrics Near‐infrared spectroscopy (NIRS), which leverages molecular absorption of infrared light, possesses strong “fingerprint” identification capabilities. It has become an indispensable tool in compound identification, molecular structure analysis, and quantitative analysis. In recent years, NIRS, combined with chemometrics, has shown tremendous potential for rapid, nondestructive detection in complex systems. In this study, near‐infrared spectra of 48 yak meat samples with varying adulteration levels (400–2500 nm) were collected. After dividing the samples by concentration gradient, six multivariate calibration methods were compared: principal component regression (PCR), partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), backpropagation artificial neural network (BP‐ANN), and extreme learning machine (ELM). The PLS model was chosen as the base model. Furthermore, 14 spectral preprocessing methods were evaluated, including Savitzky–Golay smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC), derivative spectra, and continuous wavelet transform (CWT). The CWT method was found to provide the best results. On this basis, seven variable selection algorithms, including uninformative variable elimination (UVE), Monte Carlo UVE (MCUVE), randomization test (RT), competitive adaptive reweighted sampling (CARS), whale optimization algorithm (WOA), gray wolf optimizer (GWO), and butterfly optimization algorithm (BOA), were applied. The WOA algorithm significantly improved model performance, selecting 11 and 21 key features for yak meat and beef content predictions, respectively. The final model achieved RMSEP values of 4.8607 (Rp = 0.9157) for yak meat and 3.8066 (Rp = 0.9486) for beef content. This study established the optimal modeling strategy for quantitative analysis of yak meat adulteration. Partial least squares regression ; Chemometrics ; Principal component analysis ; Feature selection ; Pattern recognition (psychology) ; Variable elimination ; Extreme learning machine ; Artificial neural network ; Backpropagation Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Food Supply Chain Traceability
Authors: Bohan Zhang | Xin Yan | Ping Lin | Ruping Zheng | Ruiyun You | Yizhi Wu | Shan Chen | Yudong Lu
Source: International Journal of Biological Macromolecules (article, 2026-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cellulose-based hydrogel SERS sensor for label-free detection and identification of antithyroid drugs Antithyroid agent ; Drug ; Therapeutic drug monitoring ; Cellulose ; Drug detection ; Antithyroid drugs ; Biological fluids Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Qian Ye | Sheng Wang | Zhaoyang Zhang
Source: AAPS PharmSciTech (article, 2026-01-08)
Field: Chemistry — Physical and Theoretical Chemistry

ML-Driven Pharmaceutical Cocrystal Technology: Advances in Screening, Property Prediction and Applications Cocrystal ; Drug discovery ; Scope (computer science) ; Virtual screening ; Pharmaceutical sciences ; Solubility ; Drug Crystallography and molecular interactions ; Luminescence and Fluorescent Materials ; Mass Spectrometry Techniques and Applications
Authors: Jiangang Chen | Changming Ke | Renji Bian | Er Pan | Peter Kun | Zefen Li | Biao Dong | Fan Yang | Qing Liu | Levente Tapaszto | Xiao Luo | Shi Liu | Fucai Liu
Source: Physical Review X (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Deterministic 1D Domain Wall Motion with Nucleation-Free Nature in Sliding Ferroelectric Switching Inhomogeneous domain nucleation and stochastic motion of the domain wall (DW) during the polarization switching process in conventional ferroelectrics introduce a spectrum of intricate kinetic challenges for the reliable and precise manipulation of polarization states, a prerequisite for many ferroelectric implementations. Combining deep-learning-assisted molecular dynamics simulations, microscopic-scale observations, and device-scale electrical measurements, it reveals that sliding ferroelectrics inherently circumvent these issues, with an intrinsic domain-nucleation-free polarization reversal. Using <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:mrow> <a:mn>3</a:mn> <a:mi>R</a:mi> </a:mrow> <a:mtext>−</a:mtext> <a:msub> <a:mrow> <a:mi>MoS</a:mi> </a:mrow> <a:mrow> <a:mn>2</a:mn> </a:mrow> </a:msub> </a:mrow> </a:math> as a model system, we demonstrate that DW motion occurs along a well-defined 1D pathway in a collective manner, without domain nucleation. Leveraging this predictable domain dynamics, we demonstrate deterministic multistate polarization switching, achieving remarkable precision and repeatability with a variation coefficient of less than 0.2%, 10 times improved over conventional ferroelectrics. This work provides valuable insights into the unique kinetics of DW motion in sliding ferroelectrics and offers opportunities for ferroelectric multistate devices with ultraprecision control. Motion (physics) ; Domain (mathematical analysis) ; Domain wall (magnetism) ; Ferroelectricity ; Work (physics) Ferroelectric and Piezoelectric Materials ; Solid-state spectroscopy and crystallography ; Photorefractive and Nonlinear Optics
Authors: Qi Dong | Tao Zhang | Jianhua Zhang | Tong Zhang | Yanze Du | Jinghong Ma | Rui Li | Bo Qin | Haijun Jiao
Source: Angewandte Chemie (article, 2026-02-04)
Field: Chemistry — Inorganic Chemistry

Encapsulated Non‐Exchangeable Na <sup>+</sup> Ions Determining the Upper Limit of Al Inclusion in FAU—A Multiscale Simulation ABSTRACT Na + ion as charge‐balance agent controlling the distribution of framework aluminum atoms in Y‐zeolite from 1 to 14 Al atoms (Si/Al = 47‐2.4) and the acid strength have been investigated based on DFT computations, descriptors from machine learning, AIMD simulations, and experimental analysis. Different from previous results with proton (H + ) as charge‐balancing agent preferring next‐nearest‐neighbor Al separation (3N‐Al), the framework Al atoms, in the presence of Na + ions, prefer larger separations (4N‐Al and 5N‐Al) due to the balance between Na + /AlO 4 − attractive and AlO 4 − /AlO 4 − repulsive interaction and the preferential occupancy of Na + ions following the order of six‐membered ring (6MR) &gt; double six‐membered ring (D6R) &gt; four‐membered ring (4MR). The computed sequential substitution enthalpy for 1‐14 Al shows the thermodynamically favorable upper limit of rather low Si/Al ratio (≤ 3) and explains the difficult synthesis of Y‐zeolite with higher Si/Al ratios. Y‐zeolite with non‐exchangeable Na + ions has stronger acid strength based on the adsorption energy of pyridine and ammonia and exhibits higher catalytic activity in propane cracking. Ion ; Adsorption ; Enthalpy ; Ring (chemistry) ; Pyridine ; Proton ; Stoichiometry ; Aluminium Zeolite Catalysis and Synthesis ; Advancements in Battery Materials ; Thermal Expansion and Ionic Conductivity
Authors: Philipp Weigand | Nikolas Ebert | Shad A. Mohammed | Denis Abu Sammour | Carsten Hopf | Oliver Wasenmüller
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Chemistry — Spectroscopy

Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging Mass spectrometry imaging (MSI) enables label-free visualization of molecular distributions across tissue samples but generates large and complex datasets that require effective peak picking to reduce data size while preserving meaningful biological information. Existing peak picking approaches perform inconsistently across heterogeneous datasets, and their evaluation is often limited to synthetic data or manually selected ion images that do not fully represent real-world challenges in MSI. To address these limitations, we propose an autoencoder-based spatial self-supervised peak learning neural network that selects spatially structured peaks by learning an attention mask leveraging both spatial and spectral information. We further introduce an evaluation procedure based on expert-annotated segmentation masks, allowing a more representative and spatially grounded assessment of peak picking performance. We evaluate our approach on four diverse public MSI datasets using our proposed evaluation procedure. Our approach consistently outperforms state-of-the-art peak picking methods by selecting spatially structured peaks, thus demonstrating its efficacy. These results highlight the value of our spatial self-supervised network in comparison to contemporary state-of-the-art methods. The evaluation procedure can be readily applied to new MSI datasets, thereby providing a consistent and robust framework for the comparison of spatially structured peak picking methods across different datasets. Pattern recognition (psychology) ; Artificial neural network ; Segmentation ; Visualization ; Spatial analysis Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Advanced Proteomics Techniques and Applications