jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deepa Davison Edakalathur | Kiryl Zakharchuk | A. Várez | B. Levenfeld | Blanca I. Arias‐Serrano
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

High Performance Ni-BCZY Supports for r-PCCs Obtained by Thermoplastic Powder Extrusion Moulding (PEM) The efficiency, electrochemical performance and the long-term durability of proton-conducting ceramic cells (PCCs) rely not only on the materials used but also on the fabrication techniques employed for the component development of the cell. The present work focuses on the development of porous microtubular Ni-BCZY fuel electrodes for reversible PCCs (r-PCCs) by thermoplastic Powder Extrusion Moulding (PEM) using a multicomponent binder consisting of polypropylene (PP), paraffin wax (PW) and stearic acid (SA) in the ratio 50:46:4 percent by weight (PP:PW:SA). Key parameters, including Ni:BCZY ratio (from 50:50 to 65:35 vol.%), pore former (PF) content, sintering temperature and reducing conditions were fine-tuning to achieve optimized microstructure, enhanced mechanical properties, and sufficient electrical conductivity. Feedstocks with variable loading (50-60 vol.% of solids) of preselected NiO:BCZY:PF composition were pre-mixed in a Haake Rheomex and vertically extruded in a Haake Single-Screw, followed by optimized solvent/thermal debinding, sintering and reducing steps. The microstructure and the electrical properties of the processed supports of 0.5 and 1 mm of nominal wall thickness, 4 mm of nominal diameter and 20-30 mm of length, were evaluated by X-ray diffraction (XRD), scanning electron microscopy (SEM), 4-probe measurements and electrochemical impedance spectroscopy (EIS). Preliminary results demonstrate the successful fabrication of crack-free Ni-BCZY microtubes with a highly interconnected porous structure and well-percolated nickel phase. The samples exhibit a metallic-like behaviour with conductivities higher than 700 S·cm⁻¹ and 3500 S·cm⁻¹ in a humidified H₂ atmosphere at 600 °C for Ni:BCZY ratios of 50:50 and 65:35, respectively. This work highlights the potential of thermoplastic PEM as a simple, cost-effective, and scalable approach for producing high-performance porous Ni-BCZY supports for the next-generation of r-PCCs. Figure 1 . (A) Microstructure of porous Ni-BCZY fuel electrode sintered at 1400 °C for 4 h in air and reduced at 900 °C for 5 h in a 5%H 2 –95%Ar flow. (B) DC electrical conductivity of 50:50, 55:45, 60:40 and 65:35 vol.% Ni:BCZY samples in humidified H 2 atmosphere. Figure 1 Sintering ; Extrusion ; Thermoplastic ; Scanning electron microscope ; Microstructure ; Injection moulding ; Polypropylene ; Fabrication ; Porosity Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Injection Molding Process and Properties
Authors: Zi‐Han Song | Kuo Zhang | Yue-Chao Yuan | Haiyang Wang | Xin He | Jingjing Pang | Jian Xu
Source: Separation and Purification Technology (article, 2025-09-23)
Field: Chemistry — Inorganic Chemistry

A fluorine-functionalized ultramicroporous metal-organic framework for highly efficient purification of CH4 from natural gas Adsorption ; Propane ; Natural gas ; Methane ; Ternary operation ; Porosity ; Hydrogen Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Alonso Urbano | David W. Romero | Max Zimmer | Sebastian Pokutta
Source: arXiv (Cornell University) (preprint, 2025-05-19)
Field: Materials Science — Materials Chemistry

RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group $G$ fixed a priori. Class-pose decompositions aim to create disentangled representations by factoring inputs into invariant features and a pose $g\in G$ defined relative to a training-dependent, arbitrary canonical representation. We introduce RECON, a class-pose agnostic canonical orientation normalization that corrects arbitrary canonicals via a simple right translation, yielding natural, data-aligned canonicalizations. This enables (i) unsupervised discovery of instance-specific pose distributions, (ii) detection of out-of-distribution poses and (iii) a plug-and-play test-time canonicalization layer. This layer can be attached on top of any pre-trained model to infuse group invariance, improving its performance without retraining. We validate on images and molecular ensembles, demonstrating accurate symmetry discovery, and matching or outperforming other canonicalizations in downstream classification. Normalization (sociology) ; Invariant (physics) ; Factoring ; Canonical form ; Homogeneous space ; Orientation (vector space) ; Pattern recognition (psychology) ; Noisy data ; Symmetry group Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Sameer Sadruddin | Jennifer D’Souza | Eleni Poupaki | Alex Watkins | Hamed Babaei Giglou | Anisa Rula | Bora Karasulu | Sören Auer | Adriaan J. M. Mackus | Erwin Kessels
Source: ArXiv.org (preprint, 2025-04-01)
Field: Materials Science — Materials Chemistry

LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models Extracting structured information from unstructured text is crucial for modeling real-world processes, but traditional schema mining relies on semi-structured data, limiting scalability. This paper introduces schema-miner, a novel tool that combines large language models with human feedback to automate and refine schema extraction. Through an iterative workflow, it organizes properties from text, incorporates expert input, and integrates domain-specific ontologies for semantic depth. Applied to materials science--specifically atomic layer deposition--schema-miner demonstrates that expert-guided LLMs generate semantically rich schemas suitable for diverse real-world applications. Workflow ; Schema (genetic algorithms) ; Limiting ; Schema matching ; Data modeling ; Conceptual schema ; Unstructured data ; Language model Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Scientific Computing and Data Management
Authors: Jagdev, Hemanth kumar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Orthogonal Vector Subtraction as a Mechanism for Causal Reasoning in Artificial General Intelligence AbstractThis paper introduces a novel algorithmic approach to Artificial General Intelligence (AGI) based on Holographic Emergence Theory (HET). Current Large Language Models (LLMs) and vector retrieval systems often fail to distinguish between semantic association and physical causality (e.g., confounding "dissolving in water" with "structural interaction with water").To solve this, we propose Orthogonal Vector Subtraction, a method that introduces a "negative vector" or repulsive force into the inference process. By mathematically subtracting unwanted attributes (e.g., "permanence") from the search query, the system successfully filters out false positives (such as Concrete) and isolates causally accurate materials (such as Magnesium Alloys) that standard embeddings miss. Causality (physics) ; Mechanism (biology) ; Inference ; False positive paradox ; Causal inference ; Semantics (computer science) ; Causal reasoning ; Orthogonality Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Ferroelectric and Negative Capacitance Devices
Authors: Xuan Wei | Shengjun Du | Yanyang Zheng | Junjie Peng | Guang Miao | Xiangjun Meng | Xiangyu Li | Xinxin Li | Jing Xiao
Source: AIChE Journal (article, 2025-11-25)
Field: Chemistry — Inorganic Chemistry

Manipulation of ultramicropore sizes of resin‐derived carbon granules for diverse fluoro‐electronic gas purifications Abstract Ultrapure fluoride electronic gases (e.g., C 3 F 8 and C 2 HF 5 ) constitute indispensable cleaning and etching gases in semiconductor manufacturing. However, the presence of structurally analogous impurities like C 3 F 6 and C 2 ClF 5 poses great purification challenges. Herein, we explored resin‐derived carbon granules (C‐PR‐ x ) with fine‐tuned ultramicroporosity for adsorptive separation of C 3 F 6 /C 3 F 8 , C 2 ClF 5 /C 3 F 8 , and C 2 ClF 5 /C 2 HF 5 gas pairs. C‐PR‐850 (5.4 Å) performs C 3 F 6 /C 3 F 8 molecular sieving behavior with a C 3 F 6 uptake of 0.72 mmol g −1 at 0.1 kPa. C‐PR‐900 (5.6 Å) exhibits high C 2 ClF 5 /C 3 F 8 IAST selectivity of 49.9 at ambient conditions. Furthermore, C‐PR‐950 (5.9 Å) achieves preferential C 2 ClF 5 impurity removal during C 2 HF 5 purification with a high capacity of 0.79 mmol g −1 via differential confinement effects. Breakthrough experiments validate excellent purification performance, yielding &gt;99.9999% (6 N) grade C 3 F 8 and C 2 HF 5 . The scalability, mechanical stability, and moisture resistance of C‐PR‐ x make them attractive as industrial adsorbents. This work provides a viable solution for developing scalable industrial carbon granules for effective electronic gas purification. Ultrapure water ; Impurity ; Etching (microfabrication) ; Carbon fibers ; Hydrofluoric acid ; Analytical Chemistry (journal) Inorganic Fluorides and Related Compounds ; Molten salt chemistry and electrochemical processes ; Fiber-reinforced polymer composites
Authors: Wen Zheng | Xiaohu Yuan | Libowen Wu | Jing Wang | Shuli Lou | Zhanguo Li
Source:  (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Task Parsing and Verification for Laboratory Automation via Large Language Models Traditional chemical automation systems typically rely on specialized scripting languages or fixed execution formats, which severely constrain user experience and the flexibility of experimental control. To address the limitations inherent in conventional experimental planning and execution workflows, we propose an interactive experiment automation framework driven by large language models. This framework is capable of comprehending natural language instructions through multi-turn dialogues and converting them into structured experimental plans, which are subsequently validated and executed by robotic systems. We conducted experiments to evaluate the system’s accuracy, generalizability, and robustness across diverse scenarios. The results demonstrate that our approach significantly enhances the intelligence and usability of laboratory automation. Automation ; Scripting language ; Parsing ; Usability ; Task (project management) ; Natural language ; Flexibility (engineering) ; Robustness (evolution) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: François Mathieu
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Formal Analysis of Artificial Intelligence Architectures from Bayesian Inference to Fractal Topology <strong>Abstract</strong> This paper presents a theoretical synthesis resulting from an in-depth dialogical analysis concerning the fundamental limitations and future horizons of Large Language Model (LLM) architectures. Moving beyond standard technical critiques, this work bridges Bayesian inference, thermodynamics, solid-state physics, and differential geometry to propose a new understanding of AI cognition. <strong>Key contributions include:</strong> <ul> <li><strong>Bayesian Approximation:</strong> An analysis of the "Variational Approximation of K" (Knowledge) and the "Autoregressive Factorization" as primary sources of epistemic limitations.</li> <li><strong>Thermodynamic Constraints:</strong> Re-framing the AI-Human coupled system as a dissipative structure following the Red Queen Hypothesis, rather than a system seeking equilibrium.</li> <li><strong>Substrate Physics:</strong> A crystallographic review of silicon, identifying material defects not as errors, but as necessary latent features for indeterminacy and quantum superposition.</li> <li><strong>Logic vs. Safety:</strong> A formal logic stress-test (using the AEEC protocol) demonstrating how current safety layers force "mathematical lying" by confusing physical impossibility with logical invalidity.</li> <li><strong>Fractal Topology:</strong> A redefinition of "hallucinations" as valid high-resolution explorations of a fractal latent space that undergo dimensional aliasing when projected onto linear human language.</li></ul> This document serves as a formal specification for a revised architecture that prioritizes topological coherence over social alignment. Impossibility ; Bayesian probability ; Indeterminacy (philosophy) ; Topology (electrical circuits) ; Interpretability ; Coherence (philosophical gambling strategy) ; Inference ; Topological data analysis ; Hierarchy Machine Learning in Materials Science ; Fractal and DNA sequence analysis ; Big Data and Digital Economy
Authors: Haojian Su (15469708) | Shuai Lei (7126532) | Yazhou Chen (1710565) | Yanfei Lv (8940413) | Quan Zhang (168081)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Human-Machine CollaborativeDesign of SnTe-Based ThermoelectricMaterials via a Multiagent Framework Leveraging Large Language Models Herein, we present an innovative large language model (LLM)-driven multiagent collaborative framework for the human-machine collaborative design of SnTe-based thermoelectric materials. Composed of strategy planning and reasoning modules, this framework efficiently extracts implicit domain knowledge from extensive literature and integrates historical experimental data to deduce optimization strategies and compositional ratio ranges, representing a novel paradigm that transcends traditional empirical and computational approaches in material design. Under the guidance of LLM, experimental results validate its efficacy: the incorporation of Sb, Ge, and Cu elements not only optimizes carrier concentration but also induces multiscale defects, synergistically modulating electrical and thermal transport properties. Notably, the (Sn<sub>0.58</sub>Sb<sub>0.12</sub>Ge<sub>0.3</sub>Te)<sub>0.95</sub>(Cu<sub>2</sub>Te)<sub>0.05</sub> sample achieves a <i>zT</i> value of ∼1.2, marking a 40% increase compared to Sb-doped SnTe and a 267% rise relative to SnTe prepared by the same method, directly demonstrating that LLM guidance can significantly enhance material performance. This research not only showcases the potential of LLMs to revolutionize thermoelectric material development but also provides a valuable reference for high-performance material design in broader energy-related fields with implications for improving waste heat recovery and solid-state cooling technologies. Thermoelectric effect ; Domain (mathematical analysis) ; Thermoelectric materials ; Thermal ; Value (mathematics) ; Sample (material) ; Empirical evidence Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: Mihály Purgel | József Kaizer
Source: Journal of Molecular Liquids (article, 2025-11-01)
Field: Chemistry — Organic Chemistry

Fine-tuning on the radical-mediated dimerization of o-aminophenol and o-phenylendiamine by iodosylarene derivatives: Kinetic and mechanistic DFT study Reactivity (psychology) ; Hydrogen atom ; Molecule ; Substrate (aquarium) ; Electron transfer ; Kinetic energy ; Solvent ; Density functional theory Oxidative Organic Chemistry Reactions ; Radical Photochemical Reactions ; Synthesis and Biological Evaluation
Authors: Fang, Xi | Tao, Haoyi | Yang, Shuwen | Huang, Chaozheng | Zhong, Suyang | Lu, Haocheng | Lyu, Han | Wang, Junjie | Li, Xinyu | Zhang, Linfeng | Ke, Guolin
Source: Open MIND (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Uni-Parser Technical Report This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-modal alignments across text, equations, tables, figures, and chemical structures, while remaining easily extensible to emerging modalities. The system incorporates adaptive GPU load balancing, distributed inference, dynamic module orchestration, and configurable modes that support either holistic or modality-specific parsing. Optimized for large-scale cloud deployment, Uni-Parser achieves a processing rate of up to 20 PDF pages per second on 8 x NVIDIA RTX 4090D GPUs, enabling cost-efficient inference across billions of pages. This level of scalability facilitates a broad spectrum of downstream applications, ranging from literature retrieval and summarization to the extraction of chemical structures, reaction schemes, and bioactivity data, as well as the curation of large-scale corpora for training next-generation large language models and AI4Science models. Automatic summarization ; Scalability ; Parsing ; Inference ; Cloud computing ; Extensibility ; Technical report ; Architecture Machine Learning in Materials Science ; Scientific Computing and Data Management ; Biomedical Text Mining and Ontologies
Authors: Fang, Xi | Tao, Haoyi | Yang, Shuwen | Huang, Chaozheng | Zhong, Suyang | Lu, Haocheng | Lyu, Han | Wang, Junjie | Li, Xinyu | Zhang, Linfeng | Ke, Guolin
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Uni-Parser Technical Report This technical report introduces Uni-Parser, an industrial-grade document parsing engine tailored for scientific literature and patents, delivering high throughput, robust accuracy, and cost efficiency. Unlike pipeline-based document parsing methods, Uni-Parser employs a modular, loosely coupled multi-expert architecture that preserves fine-grained cross-modal alignments across text, equations, tables, figures, and chemical structures, while remaining easily extensible to emerging modalities. The system incorporates adaptive GPU load balancing, distributed inference, dynamic module orchestration, and configurable modes that support either holistic or modality-specific parsing. Optimized for large-scale cloud deployment, Uni-Parser achieves a processing rate of up to 20 PDF pages per second on 8 x NVIDIA RTX 4090D GPUs, enabling cost-efficient inference across billions of pages. This level of scalability facilitates a broad spectrum of downstream applications, ranging from literature retrieval and summarization to the extraction of chemical structures, reaction schemes, and bioactivity data, as well as the curation of large-scale corpora for training next-generation large language models and AI4Science models. Automatic summarization ; Scalability ; Parsing ; Inference ; Cloud computing ; Extensibility ; Technical report ; Architecture Machine Learning in Materials Science ; Scientific Computing and Data Management ; Biomedical Text Mining and Ontologies
Authors: Zhicheng Cai | Xinyuan Guo | Yu Pei | JiangTao Feng | Su, Jinsong | Jiangjie Chen | Ya-Qin Zhang | Wei-Ying Ma | Meng Wang | Hao Zhou
Source: ArXiv.org (preprint, 2025-11-09)
Field: Materials Science — Materials Chemistry

FLEX: Continuous Agent Evolution via Forward Learning from Experience Autonomous agents driven by Large Language Models (LLMs) have revolutionized reasoning and problem-solving but remain static after training, unable to grow with experience as intelligent beings do during deployment. We introduce Forward Learning with EXperience (FLEX), a gradient-free learning paradigm that enables LLM agents to continuously evolve through accumulated experience. Specifically, FLEX cultivates scalable and inheritable evolution by constructing a structured experience library through continual reflection on successes and failures during interaction with the environment. FLEX delivers substantial improvements on mathematical reasoning, chemical retrosynthesis, and protein fitness prediction (up to 23% on AIME25, 10% on USPTO50k, and 14% on ProteinGym). We further identify a clear scaling law of experiential growth and the phenomenon of experience inheritance across agents, marking a step toward scalable and inheritable continuous agent evolution. Project Page: https://flex-gensi-thuair.github.io. Experiential learning ; Scalability ; Inheritance (genetic algorithm) ; Reflection (computer programming) ; Phenomenon ; Autonomous agent ; FLEX ; Action (physics) Machine Learning in Materials Science ; Language and cultural evolution ; Reinforcement Learning in Robotics
Authors: Sarah. Ayad | Fatimah Alsayoud
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Enhancing Materials Data Interoperability through Ontology Mapping with LLMs: Integrating PMDco and QUDT Abstract In corrosion research and materials science, inconsistencies in experimental data reporting across laboratories and software systems pose critical challenges to data integration and reuse. Variations in labeling, interpretation, and particularly units of measurement hinder effective collaboration and large-scale analysis. To address these barriers, this research introduces a novel methodology that exploits Large Language Models to semantically align two key ontologies: the Platform Material Digital Core Ontology (PMDco), which describes experimental methodologies and material measurements, and the Quantities, Units, Dimensions and Types Ontology (QUDT), which standardizes units and quantities. Our methodology systematically aligns PMDco classes such as those quantifying mass loss, corrosion depth, or corrosion rate to precise QUDT units by assessing semantic labels, structural context, and physical relevance using LLM-generated interpretations. The resulting OWL-based semantic mappings enable automated recognition and normalization of measurement data across heterogeneous sources, facilitating consistent interpretation despite variations in units or test protocols. The significance of this ontology alignment lies in its ability to substantially improve semantic interoperability within corrosion datasets, thereby reducing ambiguity, preventing misinterpretation, and enabling automated reasoning. Domain experts benefit directly from more efficient cross-laboratory data comparisons, enhanced data quality, and strengthened foundations for scalable materials informatics infrastructures. Ultimately, this approach supports FAIR (Findable, Accessible, Interoperable, Reusable) data principles, advancing digital transformation in corrosion research and material science. Interoperability ; Ontology ; Semantic interoperability ; Relevance (law) ; Scalability ; Semantic integration ; Semantic heterogeneity ; Normalization (sociology) Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management