jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai V. Slaughter | Xiang Li | Molly S. Shoichet
Source: Advanced Drug Delivery Reviews (review, 2025-12-11)
Field: Materials Science — Materials Chemistry

Exploiting colloidal drug aggregation for drug delivery: From promise to prediction using computational tools Nanomedicine ; Drug delivery ; Drug ; Nanoparticle ; Colloidal particle ; Pharmaceutical sciences ; Drug carrier ; Colloid Pickering emulsions and particle stabilization ; Drug Solubulity and Delivery Systems ; Advanced Drug Delivery Systems
Authors: Thilo Bracht | K Käppler | Malte Bayer | Franziska Grell | Karin Schork | Lars Palmowski | Björn Koos | Tim Rahmel | Dominik Ziehe | Matthias Unterberg | Lars Bergmann | Katharina Rump | Martina Broecker‐Preuss | Ulrich Limper | Dietrich Henzler | Stefan Ehrentraut | Thilo von Groote | Alexander Zarbock | Stephanie Pfaender | Nina Babel | Katrin Marcus | Martin Eisenacher | Michael Adamzik | Barbara Sitek | Hartmuth Nowak
Source: medRxiv (preprint, 2025-05-23)
Field: Chemistry — Spectroscopy

Plasma proteomics identifies molecular subtypes in sepsis Abstract Background The heterogeneity of sepsis represents a significant challenge to the development of personalized sepsis therapies. Sepsis subtyping has therefore emerged as an important approach to this problem, but its impact on clinical practice was limited due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and provide molecular and mechanistical insights. In this study, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes. Methods Plasma samples were collected from 333 patients at days 1 and 4 of sepsis and analyzed using liquid chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and characterized based on clinical routine data, cytokine measurements, and proteomics data. A random forest machine learning classifier was generated to enable future assignment of patients to subtypes. Results Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe form of sepsis, with 100 % mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, as reflected by clinical data and cytokine measurements. At the proteome level, the subtypes were characterized by distinct molecular features. We observed an alternating immune response, with cluster 1 showing prominent activation of the adaptive immune system, as indicated by elevated levels immunoglobulin (Ig) levels, which were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated an ML classifier and optimized it for the minimum number of proteins that could realistically be implemented into routine diagnostics. The final model, which was based on 10 proteins and Ig quantities, allowed the assignment of patients to clusters 1, 2 and 3 with high confidence. Conclusion The identified plasma proteome subtypes provide insights into the immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures, enabling predictive enrichment in clinical trials. Thus, they represent a step forward in the development of targeted therapies and personalized medicine for sepsis. Proteomics ; Sepsis ; Computational biology ; Data science ; Computer science ; Medicine ; Biology ; Internal medicine ; Genetics ; Gene Advanced Proteomics Techniques and Applications
Authors: Yu Zhang | Zhuonan Jia | Xiaoqing Chen | Yong Zhang | Mingwen Zhang | Jianyu Wang | Liang Liu | Liang Fang | Chenyang Zhao | Ting Mei | Jianlin Zhao | Xuetao Gan
Source: Advanced Functional Materials (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Quadratically Nonlinear Photodetector Based on InSe/Si <i>p–n</i> Heterojunction Abstract A highly efficient quadratically nonlinear photodetector (QNPD) is demonstrated based on an InSe/Si p–n heterojunction that uniquely exploits the strong second‐order nonlinearity of few‐layer InSe integrated onto silicon substrates. Through second‐harmonic generation (SHG)‐assisted frequency upconversion, the QNPD can detect photons with energies below the InSe/Si electronic bandgap, extending the photodetection range to 1750 nm. Under 1550 nm pulsed excitation, it achieves a high normalized responsivity of 2.3 × 10 −3 A W −2 with a clear quadratic photocurrent dependence ( I ph ∝ P laser 2 ). Leveraging this quadratic response, the device functions as a compact optical autocorrelator, enabling accurate characterization of ultrashort pulses of 2.5 and 8.5 ps with a sensitivity of 5 × 10 −5 W 2 , surpassing commercial systems by an order of magnitude. Furthermore, the QNPD's nonlinear response is integrated as an activation function within convolutional neural networks, achieving a digital image classification accuracy of over 98.7% on the MNIST dataset. With the well‐developed large‐scale growth and transfer of 2D materials with Si, the demonstrated QNPD promises broad potential in integrated nonlinear photonics, ultrafast optical diagnostics, and neuromorphic computing. Photodetector ; Responsivity ; Photocurrent ; Ultrashort pulse ; Photodetection ; Heterojunction ; Nonlinear system ; Neuromorphic engineering 2D Materials and Applications ; Photorefractive and Nonlinear Optics ; Advanced Fiber Laser Technologies
Authors: Yongtao Ding (21500014) | Yuekun Yang (5626199) | Hao Hao (3643735) | Zhu-An Li (20683778) | Chen Pan (611009) | Pengfei Wang (123133) | Cong Wang (107450) | Huibin Tan (21500017) | Ruochun Jin (21500020) | Jun Zhang (48506) | Shiying Du (9074683) | Shi-Jun Liang (3634027) | Feng Miao (1788318) | Wenjing Yang (466552)
Source: Figshare (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

BioinspiredTarget Detection Pipeline Based on Two-DimensionalOptoelectronic van der Waals Heterostructures Noncooperative target detection in real-world scenarios relies on large-scale deep neural networks after image capture. However, directly implementing this detection pipeline under conventional optoelectronic sensors and computing units leads to physical bottlenecks in latency and energy consumption. Here, inspired by the biological visual attention mechanism and leveraging fabricated two-dimensional optoelectronic van der Waals heterostructure devices, we present a highly efficient neuromorphic in-sensor target detection pipeline. The inherent physical process of infrared self-driven visible photoresponse in heterostructures is used to simplify the originally complex processing in artificial intelligence (AI) optical detection algorithms. Specifically, the high-cost target localization and image fusion process can be directly implemented in the sensing unit. This manipulation decreases redundant information at the sensor level, reducing the burden on data transmission and backend computation. The results show that our bioinspired pipeline achieves a mean average precision (mAP) of 95.85% in detecting real-world scenes, even in extreme environments. Meanwhile, significant reductions in computing load (31.65%), latency (95.66%), and energy consumption (21.25%) can be attained compared with previous research. Our work provides a scalable material-for-AI solution for real-time, highly efficient target detection applications in real-world settings. Pipeline (software) ; Scalability ; Heterojunction ; Energy consumption ; Process (computing) ; Latency (audio) ; Neuromorphic engineering ; Efficient energy use 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Neural Networks and Reservoir Computing
Authors: D. L. Muccignat | G. J. Boyle | Nathan Garland | Ronald D. White
Source: Plasma Sources Science and Technology (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

HFO-1234ze(E): electron-scattering cross section set through an iterative deep learning procedure Abstract Recently, HFO-1234ze(E)-based gas mixtures have been proposed as an environmentally friendly replacement for SF 6 in medium voltage gas insulated switchgear. The prediction of electron transport coefficients and reaction rates is therefore crucial for the optimisation of gas mixtures to achieve the desired performance. These calculations rely on the determination of a complete and self-consistent electron scattering cross section set for HFO-1234ze(E). Therefore, in this study, we use a recently developed iterative deep learning method to determine a self-consistent set of electron scattering cross sections for HFO-1234ze(E). The network is trained using perturbations of cross section data available on the LXCat database and is applied to HFO-1234ze(E) transport data measured by the ETH Zürich High Voltage Laboratory group. Overall, the resulting cross-sectional regression achieves a reasonable reproduction of transport coefficients for the pure gas state along with mixtures of CO 2 and SF 6 . Critically, for its application as a dielectric insulator, the set of cross sections reproduces the positive synergistic effect observed in mixtures with SF 6 . Cross section (physics) ; Scattering ; Set (abstract data type) ; Electron scattering ; Dielectric ; Iterative method ; Voltage ; Cross-validation ; Electron High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhenzhao Zhang | Zhuofeng Liu | Wenjian Guo | Fenglin Wang | Yunpeng Guo | M. Lou
Source:  (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Machine learning Accelerated the Prediction of Thermal Conductivity in Silicon Nitride Ceramics With the rapid development of electronic power technology, electronic devices are evolving towards higher power and packaging density, which imposes higher demands on substrate materials. Due to the excellent mechanical properties and outstanding theoretical thermal conductivity, silicon nitride ceramics have been widely applied in electronic packaging. Improving thermal conductivity has substantial practical benefits. In this work, six machine learning models were selected, namely Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), and Linear Regression (LR). Ten input features were chosen to train the models and predict thermal conductivity. The results indicate that the XGBoost model achieved the highest prediction accuracy (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.79, MAE=9.3). SHAP (SHapley Additive exPlanations) analysis was employed to interpret the model and rank the influence of the ten features on thermal conductivity. The results revealed that the sintering time and sintering temperature are the most significant factors influencing thermal conductivity, SHAP values indicated that higher sintering temperatures and longer sintering times are conducive to enhancing thermal conductivity. A silicon nitride ceramic was prepared to further validate the predictive accuracy of the model. Silicon nitride ; Sintering ; Ceramic ; Thermal conductivity ; Multilayer perceptron ; Gradient boosting ; Decision tree ; Thermal Thermal properties of materials ; Semiconductor materials and devices ; Machine Learning in Materials Science
Authors: Kazuma Ito
Source: arXiv (Cornell University) (preprint, 2025-12-28)
Field: Materials Science — Metals and Alloys

Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. However, the substantial computational expense associated with existing MLIPs has limited their applicability in practical, large-scale simulations. In this study, we construct a new MLIP within the Performant Implementation of the Atomic Cluster Expansion (PACE) framework, trained on a comprehensive HE-related dataset generated through a concurrent-learning strategy. The resulting potential achieves density functional theory-level accuracy in reproducing a wide range of lattice defects in alpha-Fe and their interactions with hydrogen, including both screw and edge dislocations. More importantly, it accurately captures the deformation and fracture behavior of nanopolycrystals containing hydrogen-segregated general grain boundaries-phenomena not explicitly represented in the training data. Despite its high fidelity, the developed potential requires computational resources only several tens of times greater than empirical potentials and is more than an order of magnitude faster than previously reported MLIPs. By delivering both a high-precision and computationally efficient potential, as well as a generalizable methodology for constructing such models, this study significantly advances the atomic-scale understanding of HE across a broad range of metallic materials. Interatomic potential ; Hydrogen embrittlement ; Binary number ; Range (aeronautics) ; Embrittlement ; Lattice (music) ; Hydrogen Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Long Cai
Source: Journal of The Institution of Engineers (India) Series C (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning-Based Corrosion Prediction of Steels Exposed to Marine Environments Corrosion ; Artificial intelligence ; Computer science ; Environmental science ; Machine learning ; Metallurgy ; Materials science Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Tarsh Shah (21802466) | Jackson A. Fitzpatrick (21802469) | Benjamin C. Orsburn (9306361)
Source: Figshare (article, 2025-07-29)
Field: Chemistry — Spectroscopy

Backyard Proteomics:A Case Study with the Black WidowSpider Nearly all methods of mass-spectrometry-based proteomics rely on knowing the proteome of the species. In less studied organisms without annotated genomes, it can seem impossible to perform proteomic analysis. In this study, we sought to answer the question: does enough information exist to do proteomics on any organism we want? As a case study, we started with material available due to an infestation of a home with black widow spiders. Thanks to the recent publication of an annotated genome for one species of black widow spider, we were able to identify 5502 protein groups and assign putative annotations using ortholog mapping. We also demonstrate that had we not had this resource, over 2000 proteins could be identified using other available spider genome annotations, despite their unrelatedness. Moreover, regardless of the spider proteome used, proteins annotated as toxins were almost exclusively observed in the main body of the mature female black widow spider. Overall, these results provide a draft proteome map for the black widow spider and valuable data for validating machine learning models while also suggesting that the door to insightful quantitative proteomics may already be open for millions of less studied organisms. All raw and processed proteomic data are available through the ProteomeXchange repository as accession PXD051601. Proteome ; Proteomics ; Genome ; Model organism ; Organism ; Spider Advanced Proteomics Techniques and Applications ; Antimicrobial Peptides and Activities ; Venomous Animal Envenomation and Studies
Authors: Ke Wang | Yuwen Wu | X. Zhou | Yang Yu | Lizhi Xu | Guangfa Gao
Source: Experimental Mechanics (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Understanding the Incident Wave Errors in Split Hopkinson Pressure Bar Test with Machine Learning Method Bar (unit) ; Solid mechanics ; Split-Hopkinson pressure bar ; Mechanics ; Test (biology) ; Acoustics ; Materials science ; Structural engineering ; Engineering ; Physics ; Geology ; Composite material High-Velocity Impact and Material Behavior ; Nuclear Physics and Applications ; Laser-Plasma Interactions and Diagnostics
Authors: Kazuma Ito
Source: ArXiv.org (article, 2025-12-28)
Field: Materials Science — Metals and Alloys

Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. However, the substantial computational expense associated with existing MLIPs has limited their applicability in practical, large-scale simulations. In this study, we construct a new MLIP within the Performant Implementation of the Atomic Cluster Expansion (PACE) framework, trained on a comprehensive HE-related dataset generated through a concurrent-learning strategy. The resulting potential achieves density functional theory-level accuracy in reproducing a wide range of lattice defects in alpha-Fe and their interactions with hydrogen, including both screw and edge dislocations. More importantly, it accurately captures the deformation and fracture behavior of nanopolycrystals containing hydrogen-segregated general grain boundaries-phenomena not explicitly represented in the training data. Despite its high fidelity, the developed potential requires computational resources only several tens of times greater than empirical potentials and is more than an order of magnitude faster than previously reported MLIPs. By delivering both a high-precision and computationally efficient potential, as well as a generalizable methodology for constructing such models, this study significantly advances the atomic-scale understanding of HE across a broad range of metallic materials. Interatomic potential ; Hydrogen embrittlement ; Binary number ; Range (aeronautics) ; Embrittlement ; Lattice (music) ; Hydrogen Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Chen Xu | Chao Zhi | Yiqin Xu | Zhe Liu | Xingzhong Gao | Huanhuan Zhang | Yanli Sun | Zijing Dong | Lingjie Yu
Source: Materials Today Communications (article, 2025-12-02)
Field: Materials Science — Polymers and Plastics

Data-driven prediction and inverse design of fabric tensile properties via machine learning Accurate prediction of fabric mechanical behavior constitutes a fundamental challenge in textile material design and optimization. As the primary mechanical property, tensile failure determines fabric durability and fastness, making its reliable prediction essential for quality assessment. To address the high cost and low efficiency issues arose from empirical fabric design, this study aims to establish a hybrid finite modeling - machine learning (FEM-ML) framework for predicting and inversely designing woven fabric tensile properties based on structural parameters. The FEM was firstly conducted to generate simulation datasets for ML training; then DNN ML models, were adopted to predict the tensile properties; An inverse design model was further established to directly determine structural parameters from the targeted tensile properties. Experimental results demonstrate that the optimal model achieved a coefficient of determination ( R ²) of 0.913 and a root mean square error (RMSE) of 0.032, with mean prediction errors 8.3 %. In addition, the inverse design model designed six sets of structural parameters, which were practically fabricated and tested. The average deviation between the measured and target tensile values remained 7.8 %, confirming the model’s predictive accuracy and practical applicability. Overall, the proposed FEM–ML hybrid approach enables both forward prediction and inverse optimization of fabric structures, offering a prospective data-driven strategy for intelligent textile design. Ultimate tensile strength ; Inverse ; Finite element method ; Mean squared error ; Durability ; Tensile testing ; Inverse problem ; Textile ; Woven fabric Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Mechanical Behavior of Composites
Authors: X Li | Wenqian Tu | Run Lv | L Liu | Ding‐Fu Shao | Yuping Sun | W. J. Lu
Source: ArXiv.org (preprint, 2025-09-03)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated search of superconductors in B-C-N based compounds and R3Ni2O7-type nickelates Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery. Here, we integrated ML with density functional theory (DFT) calculations to efficiently screen conventional B-C-N based superconductors and identify potential high-TC candidates among R3Ni2O7-type bilayer nickelates. We identified 12 new binary and ternary B-C-N based superconductors with TC &gt;= 10 K, including 3 with TC &gt;= 25 K, such as two structural forms of B2CN (TC = 44.8 K and 41.5 K) and TiNbN2 (TC = 26.2 K). These materials share a common feature of strong σ-bonds, which is key to achieving relatively high TC. Moreover, we proposed Tb3Ni2O7 (TC = 61.6 K) and Ac3Ni2O7 (TC = 70.3 K) as potential high-TC nickelate superconductors under high pressure. Their electronic structures closely resemble those of La3Ni2O7, especially in the hole-type band dominated by Ni-3dz2 orbital character. We also analyzed feature importance in the ML results for both conventional and high-TC superconductors. These results advance the search for new superconductors and enhance the fundamental understanding of superconducting mechanisms. Superconductivity ; Ternary operation ; Bilayer ; Binary number ; High-temperature superconductivity ; Feature (linguistics) ; Density functional theory Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: Haiyan Zhu | Hongyu Yu | Wancheng Zhu | Guoliang Yu | Changsong Xu | Hongjun Xiang
Source: ArXiv.org (preprint, 2025-07-18)
Field: Materials Science — Materials Chemistry

Moiré-Induced Magnetoelectricity in Twisted Bilayer NiI2 Twisted magnetic van der Waals (vdW) materials offer a promising route for multiferroic engineering, yet modeling large-scale moiré superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled systems, we develop a comprehensive interatomic machine learning (ML) potential and apply it to twisted bilayer NiI2 (TBN). Structural relaxation introduces moiré-periodic "bumps" that modulate the interlayer spacing by about 0.55~Å and in-plane ionic shifts up to 0.48~Å. Concurrently, our ML potential, which faithfully captures all key spin interactions, produces reliable magnetic configurations; combined with the generalized KNB mechanism, it yields accurate spin-driven polarization. For twist angles 1.89^{\circ} \leq θ\leq 2.45^{\circ}, both mechanisms become prominent, yielding rich polarization textures that combine ionic out-of-plane dipoles with purely electronic in-plane domains. In the rigid (unrelaxed) bilayer, skyrmions are absent; lattice relaxation is essential for generating polar-magnetic topologies. In contrast, near θ \approx 60^{\circ}, stacking-dependent ferroelectric displacements dominate, giving rise to polar meron-antimeron networks. These results reveal cooperative ionic and spin-driven ferroelectricity in TBN, positioning twisted vdW magnets as adaptable platforms for tunable multiferroic devices. Ferroelectricity ; Multiferroics ; Dipole ; van der Waals force ; Ionic bonding ; Skyrmion ; Polarization (electrochemistry) ; Bilayer 2D Materials and Applications ; Multiferroics and related materials ; Topological Materials and Phenomena