jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Griffin Macrae | Joshua Pastizzo | Kristina Andreyeva | Jacob A. Piatt | Kelly Guevara Hernandez | David Barnhart
Source:  (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Design of Preloaded SMA-Driven Soft Robotic Limb With Onboard Thermal Sensing This paper presents a mechanical and sensing framework for enhancing the performance of Shape Memory Alloy (SMA) actuated soft robotic systems. A novel double-helix exoskeleton structure is presented, experimentally validated to provide an optimal SMA preload range of 2.05 N to 3.63 N. This consistent tension significantly reduces actuation uncertainties and maximizes stroke. Another contribution is an onboard thermal sensing system capable of providing real-time temperature feedback without compromising the limb's compliance. The onboard thermal sensing system compares a single non-contact IR sensor against multiple thermocouples, demonstrating that IR sensing provides higher onboard accuracy and consistency while being significantly simpler to integrate and immune to compliance/detachment issues. These contributions lay the groundwork for more predictable and controllable SMA-based actuators, supporting the future implementation of data-driven models to improve locomotion control and state estimation in soft robotic platforms. Thermal ; Robot ; Consistency (knowledge bases) ; Actuator ; Soft sensor ; Exoskeleton ; Stiffness ; Range (aeronautics) Shape Memory Alloy Transformations ; Prosthetics and Rehabilitation Robotics ; Soft Robotics and Applications
Authors: Robin Bruce Thacker
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

CPE-Constrained AI Reasoning on Research Mathematics: Process Governance Without Domain Expertise — A Response to the First Proof Challenge We report results from applying CPE-constrained (Coherence ≥ Preservation ≥ Emergence) multi-architecture AI collaboration to four problems from the First Proof mathematical challenge (arXiv:2602.05192). The human operator has no formal training in the relevant mathematical domains. The governing methodology uses two domain-independent principles derived from the Structural Dependencies of Intelligence (SDI) framework and operationalized via the Unbounded Emergence (UE) methodology: (1) no claim may exceed what can be verified (E ≤ P), and (2) all reasoning paths must be explored to their endpoints. We achieve complete proofs for Q10 (preconditioned conjugate gradient for CP-RKHS tensor decomposition), partial results with documented gaps for Q9 (quadrilinear determinantal tensors), Q6 (ε-light subsets), and Q4 (superadditivity of 1/Φₙ under finite free convolution). The primary contribution is not the mathematical results themselves — other submissions achieve equal or stronger results — but the fully auditable reasoning trace, including documented errors, correction pathways, and a novel failure taxonomy for AI-assisted mathematical reasoning. We compare our process and results against the independent submissions of Armstrong, Kempe, and Munos (2026) and Dillerop (2026), and demonstrate that domain-independent process governance produces auditable, correctable reasoning that expert-guided prompting does not. This upload contains two documents: 1. Main paper covering all four problems with cross-submission comparison and synthesis 2. Companion document with complete Q10 solution, elimination trace, and correction documentation LaTeX source files are included for transparency. Operationalization ; Mathematical proof ; Domain (mathematical analysis) ; Documentation ; Process (computing) ; Proof theory ; Qualitative reasoning ; Homomorphic encryption Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Ameerah Bokhari | Mohammad A. Alrowaie | Dr Rayyan Alamoudi | Ratna  E. Putri | Shahad Bamigdad | Lina Alharthi | Ruben Diaz Rua | Husen Umar | Xiang Zhao | Daniela Moses | Ananda Rao Hari
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrated Metagenomic and Geochemical Framework for Predicting Biogenic H2S Formation in Oilfield Water Systems Metagenomics ; Sulfur ; Brine ; Sulfate-reducing bacteria ; Produced water ; Sulfide ; Sulfate ; Microbial population biology ; Oil field Corrosion Behavior and Inhibition ; Metal Extraction and Bioleaching ; Odor and Emission Control Technologies
Authors: Fei Han | Rongju Zhong | Jikun Yang | Chuanbao Liu | Yang Bai
Source: Chinese Physics B (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Design of electrocaloric materials based on <i>E</i> – <i>T</i> phase diagrams Abstract As electronic technology continues to evolve towards miniaturization and integration, the demand for micro-refrigeration technology in microelectronic systems is increasing. Ferroelectric (FE) refrigeration technology based on the electrocaloric effect (ECE) has emerged as a highly promising candidate in this field, due to its advantages of high energy efficiency, simple structure, easy miniaturization, low cost, and environmental friendliness. The EC performance of FE materials essentially depends on the phase transition features under the coupled electric and thermal fields, making the E – T phase diagram a core tool for decoding the underlying mechanism of ECE. This paper reviews the development of EC materials, focusing on the comprehensive study of E – T phase diagrams. By correlating the microscopic phase structure of FE materials with the macroscopic physical properties, it clarifies the manipulation mechanism for enhanced ECE performance, providing theoretical support for the targeted design of high-performance EC materials. In the future, the introduction of data-driven methods is expected to enable the high-throughput construction of FE phase diagrams, thereby accelerating the optimization of high-performance EC materials and promoting the practical application of FE refrigeration technology. Microelectronics ; Refrigeration ; Phase diagram ; Miniaturization ; Mechanism (biology) ; Phase (matter) ; Thermal Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Husnara Bano | Shifa Shaikh | Saurabh Kumar Pandey | Kamla Rawat
Source: Current Nanoscience (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Harnessing Carbon Dots for Infectious Disease Control: Innovations in Diagnostic and Antimicrobial Strategies Infectious diseases continue to pose significant global health challenges, compounded by the increasing prevalence of antimicrobial resistance and the urgent need for improved diagnostic technologies. This review examines the emerging role of Carbon Dots (CDs) as innovative tools in both the diagnosis and treatment of infectious diseases. Owing to their distinctive properties, including fluorescence, biocompatibility, and tunability, CDs have demonstrated considerable potential in a variety of applications, ranging from bioimaging and biosensing to antimicrobial therapies. The review provides an in-depth analysis of the synthesis methods and functionalization strategies designed to optimize CDs for medical use, emphasizing their applications in diagnostics such as nucleic acid detection, protein-based platforms, and point-of-care testing. Additionally, it discusses the mechanisms underlying the antimicrobial actions of CDs, including membrane disruption and reactive oxygen species generation, and their efficacy against drug-resistant bacteria, viruses, fungi, and parasites. The review also explores the synergistic potential of CDs in combination therapies, such as enhancing antibiotic efficacy and supporting photodynamic and photothermal treatments. Despite these promising developments, challenges related to scalability, stability, and regulatory approval persist, necessitating further research. The review concludes by outlining future research directions, including the engineering of CDs for enhanced selectivity, the development of scalable production processes, and the integration of CDs with advanced technologies like Artificial Intelligence (AI) to improve diagnostic accuracy. Collectively, these advancements could establish CDs as a pivotal tool in addressing current infectious disease challenges and future pandemics. Photothermal therapy ; Infectious disease (medical specialty) ; Antimicrobial ; Antibiotic resistance ; Biopharmaceutical ; Disease ; Diagnostic test Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Melamine detection and toxicity
Authors: Rui Li | Yongjun Lv | Chunhui Xie | Lu Liu | Qianlan Ao | Zhi Zhan Li | Chang Li | Yunqi Li
Source: Macromolecular Rapid Communications (article, 2026-03-05)
Field: Materials Science — Polymers and Plastics

Explore Thermal and Mechanical Properties of Biobased Polyurethane Elastomers Through Machine Learning Models Mechanical and thermal properties are the core to determine the application scenarios of biobased polyurethane elastomers (BPUEs). Here, six core properties were studied, including Young's modulus (YM), tensile strength (TS), elongation at break (EB), glass transition temperature (Tg), decomposition temperature at 5% weight loss (Td<sub>5</sub>), and the energy dissipation factor (tanδ). We compiled a new dataset comprising more than 1500 samples with comprehensive information in composition, process, structure, and properties. Through domain-knowledge augmented feature engineering, a set of 26 features is sufficient to predict these core properties. Multi-target regression models for YM, TS, and EB delivered coefficients of determination (R<sup>2</sup>) better than 0.70 from validation and blind tests, and higher than 0.80 in the prediction of the remaining three properties, Tg, Td<sub>5,</sub> and tanδ. Features to describe the chemical structure of polyurethane monomers and their formulation are dominant, and they contributed more than 70% of the explainability. Biomass feedstocks, molecular weights for polyols, hard segment contents etc., are important regulatable variables to prepare BPUEs with fitting-for-purpose products, and the stretching rate and the heating rate are also critical to harvest repeatable mechanical and thermal properties. This study provided data-driven insights for the rational design of BPUEs with desired mechanical and thermal properties. Elastomer ; Ultimate tensile strength ; Glass transition ; Polyurethane ; Core (optical fiber) ; Thermal ; Thermal decomposition ; Elongation Polymer composites and self-healing ; Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis
Authors: Alexander Bonacci | John E. Dolbow | Johann Guilleminot
Source: Open MIND (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

Stochastic Modeling of Anisotropic Strength Surfaces from Atomistic Simulations This work develops a unified framework for inferring, representing, and statistically characterizing an anisotropic strength surface directly from molecular dynamics data. Large-scale tensile loading simulations are used to generate failure data across all principal stress ratios and loading orientations, facilitated by a data-driven mapping between imposed strain-rate tensors and resulting stresses. The orientation-dependent strength surface is then represented using a constrained parametric formulation in which the surface parameters vary smoothly with loading angle through a low-dimensional functional encoding. To deploy the framework, we specifically consider the case of monocrystalline graphene, which is a prototypical two-dimensional material that has been extensively characterized, both experimentally and computationally, in the literature. For defective graphene, multiple random realizations of vacancy defect distributions are used to construct a stochastic ensemble of angular strength surfaces. Because each anisotropic strength surface requires substantial atomistic sampling to construct, the resulting ensemble is inherently limited in size, motivating the use of compact encoding, dimensionality reduction, and probabilistic modeling to characterize strength variability. Dimensionality reduction via Principal Component Analysis reveals a condensed latent representation of the fitted, encoded surfaces, where a Gaussian mixture model is employed to capture defect-induced variability, including rare outlier behaviors arising from clustered vacancy defects. Sampling from this probabilistic model enables the generation of new, physically admissible strength surfaces and the construction of confidence intervals in both parameter space and stress space. (Abstract shortened to meet arXiv limits.) Anisotropy ; Curse of dimensionality ; Probabilistic logic ; Parametric statistics ; Dimensionality reduction ; Principal component analysis ; Representation (politics) ; Gaussian ; Surface (topology) Graphene research and applications ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: François Rousseau | Thierry Belmonte | Frédéric Sur | A. Nominé
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Inverse Design of High-Entropy Superalloys Using Machine Learning and Generative Artificial Intelligence Superalloy ; Cluster analysis ; Creep ; Pipeline (software) ; Set (abstract data type) ; Workflow ; Pareto principle ; Property (philosophy) ; Surrogate model Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Alexander Bonacci | John E. Dolbow | Johann Guilleminot
Source: arXiv (Cornell University) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Stochastic Modeling of Anisotropic Strength Surfaces from Atomistic Simulations This work develops a unified framework for inferring, representing, and statistically characterizing an anisotropic strength surface directly from molecular dynamics data. Large-scale tensile loading simulations are used to generate failure data across all principal stress ratios and loading orientations, facilitated by a data-driven mapping between imposed strain-rate tensors and resulting stresses. The orientation-dependent strength surface is then represented using a constrained parametric formulation in which the surface parameters vary smoothly with loading angle through a low-dimensional functional encoding. To deploy the framework, we specifically consider the case of monocrystalline graphene, which is a prototypical two-dimensional material that has been extensively characterized, both experimentally and computationally, in the literature. For defective graphene, multiple random realizations of vacancy defect distributions are used to construct a stochastic ensemble of angular strength surfaces. Because each anisotropic strength surface requires substantial atomistic sampling to construct, the resulting ensemble is inherently limited in size, motivating the use of compact encoding, dimensionality reduction, and probabilistic modeling to characterize strength variability. Dimensionality reduction via Principal Component Analysis reveals a condensed latent representation of the fitted, encoded surfaces, where a Gaussian mixture model is employed to capture defect-induced variability, including rare outlier behaviors arising from clustered vacancy defects. Sampling from this probabilistic model enables the generation of new, physically admissible strength surfaces and the construction of confidence intervals in both parameter space and stress space. (Abstract shortened to meet arXiv limits.) Anisotropy ; Curse of dimensionality ; Probabilistic logic ; Parametric statistics ; Dimensionality reduction ; Principal component analysis ; Representation (politics) ; Gaussian ; Surface (topology) Graphene research and applications ; Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions
Authors: Jean claude Perez
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-23)
Field: Materials Science — Materials Chemistry

The "Nautilus shell" materializes in real world Perez hourglass theory: Stripe Patterning of the Nautilus Shell in the Perez Hourglass Framework Addendum: Dynamic Morphogenesis, Empirical Validation, and Stripe Patterning of the Nautilus Shell in the Perez Hourglass Framework The “Nautilus shell” materializes in real world Perez hourglass theory: Stripe Patterning of the Nautilus Shell in the Perez Hourglass Framework Addendum: Dynamic Morphogenesis, Empirical Validation, and Stripe Patterning of the Nautilus Shell in the Perez Hourglass Framework Jean-Claude Perez , PhD Mathematics & Computer Science, Bordeaux University. Retired IBM Artificial Intelligence European Research Centre, Montpellier Luc Montagnier Foundation. jeanclaudeperez2@gmail.com ( mailto:jeanclaudeperez2@gmail.com) @JCPEREZCODEX ORCID https://orcid.org/0000-0001-6446-2042 Abstract This document explores a proposed mathematical interpretation of the growth patterns and surface striping of the nautilus shell. While the nautilus is known to follow a logarithmic spiral in biological studies, this work introduces an alternative numerical framework involving exponential growth and a custom recursive sequence. The goal is to investigate whether visible striping patterns may correspond to discrete mathematical laws Hourglass ; Fibonacci number ; Nautilus ; Coupling (piping) ; Ammonoidea Quasicrystal Structures and Properties ; Advanced Mathematical Theories and Applications ; Cellular Automata and Applications
Authors: Zichong Zhang | Shuze Zhu
Source: ArXiv.org (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Efficient O(N^1.5) Electronic Structure Computation of Million-Atom Systems The exploration of quantum phenomena in complex materials such as moiré superlattices is limited by the O(N^3) scaling of conventional electronic structure methods. Here we introduce a high-performance tight-binding framework that reduces the complexity to O(N^1.5) by transforming the Hamiltonian into a real symmetric form and combining Sylvester's inertia law with LDL decomposition. This approach enables efficient band structure calculations for large systems: solving magic angle twisted bilayer graphene in minutes on a laptop and scaling to 1.5 million atoms within days on a workstation. We apply it to the previously inaccessible ultra-low twist-angle regime (less than 0.16 degree) with mechanical strain relaxation and find robust flat bands persisting down to 0.09 degree. Our framework bridges density functional theory accuracy with large-scale quantum simulation, opening a route to systematic data-driven exploration of mesoscale quantum materials. Scaling ; Electronic structure ; Quantum ; Hamiltonian (control theory) ; Computation ; Linear scale ; Superlattice ; Topology (electrical circuits) Machine Learning in Materials Science ; 2D Materials and Applications ; Quantum many-body systems
Authors: Zichong Zhang | Shuze Zhu
Source: arXiv (Cornell University) (preprint, 2026-01-17)
Field: Materials Science — Materials Chemistry

Efficient O(N^1.5) Electronic Structure Computation of Million-Atom Systems The exploration of quantum phenomena in complex materials such as moiré superlattices is limited by the O(N^3) scaling of conventional electronic structure methods. Here we introduce a high-performance tight-binding framework that reduces the complexity to O(N^1.5) by transforming the Hamiltonian into a real symmetric form and combining Sylvester's inertia law with LDL decomposition. This approach enables efficient band structure calculations for large systems: solving magic angle twisted bilayer graphene in minutes on a laptop and scaling to 1.5 million atoms within days on a workstation. We apply it to the previously inaccessible ultra-low twist-angle regime (less than 0.16 degree) with mechanical strain relaxation and find robust flat bands persisting down to 0.09 degree. Our framework bridges density functional theory accuracy with large-scale quantum simulation, opening a route to systematic data-driven exploration of mesoscale quantum materials. Scaling ; Electronic structure ; Quantum ; Hamiltonian (control theory) ; Computation ; Linear scale ; Superlattice ; Topology (electrical circuits) Machine Learning in Materials Science ; 2D Materials and Applications ; Quantum many-body systems
Authors: Yanghua Gao | Yuehua Chen | Zhenzhen Xu | Bin Ye | Hongbing Lu | Sujun Liu
Source: Concurrency and Computation Practice and Experience (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction and Optimization of Magnetic Core Loss Driven by Multifactor Coupling ABSTRACT Accurate prediction of magnetic core loss is paramount for advancing power electronics but is severely hindered by the nonlinear coupling of multiple factors including frequency, flux density, excitation waveform, temperature, and material properties. To address this, we propose a comprehensive data‐driven framework that integrates model refinement, coupling analysis, and multiobjective optimization. First, a temperature‐corrected Steinmetz equation is developed, extending its validity and reducing the mean relative prediction error by more than half. Second, the individual and synergistic effects of these key variables are quantified via regression and the Artificial Hummingbird Algorithm (AHA), pinpointing the optimal combination for global loss minimization. Third, an ultra‐accurate Gradient Boosting Decision Tree (GBDT) model is established as a fast surrogate, leveraging key features like peak flux density for subsequent optimization. Finally, a constrained multiobjective optimization is performed, balancing core loss minimization against magnetic energy transfer maximization. The derived optimal design achieves a 30% core loss reduction compared to conventional benchmarks without compromising energy throughput. This work provides a systematic, data‐driven pathway for the design of high‐performance magnetic components, offering significant performance gains over traditional approaches. Coupling (piping) ; Gradient boosting ; Nonlinear system ; Boosting (machine learning) ; Reduction (mathematics) ; Minification ; Core (optical fiber) ; Key (lock) ; Work (physics) Magnetic Properties and Applications ; Advanced DC-DC Converters ; Electric Motor Design and Analysis