jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanze Song (18358291) | Zhidong Pan (20471060) | Chengming Luo (11868936) | Yue Wang (65477) | Tao Zheng (146941) | Yuan Pan (309187) | Nabuqi Bu (18867313) | Ruiyang Xu (12882927) | Nengjie Huo (1650637)
Source: Figshare (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Ferroelectric α‑In<sub>2</sub>Se<sub>3</sub> Semi-floating Gate Transistors for MultilevelMemory andOptoelectronic Logic Gate Progress in artificial intelligence (AI) demands efficient data storage and high-speed processing. Traditional von Neumann architecture, with space separation of memory and computing units, struggles with increased data transmission, causing power inefficiency and date latency. To address this challenge, we designed a semi-floating gate transistor (SFGT) that integrates data storage and logical operation into a single device by employing a ferroelectric semiconductor α-In<sub>2</sub>Se<sub>3</sub> as a semi-floating gate layer. Leveraging the ferroelectric polarization of α-In<sub>2</sub>Se<sub>3</sub>, the device exhibits improved non-volatile memory performance with a high program/erase ratio of 1 × 10<sup>6</sup> and reliable durability over 1000 cycles. Through the dual-gate modulation, the SFGT achieves multilevel storage function with at least seven controllable programming states and performs three types of digital logic gate operations (“AND”, “NOR”, and “OR”) at an ultralow bias of 10 mV. Compared to traditional FGT architectures, the α-In<sub>2</sub>Se<sub>3</sub>-based semi-floating gate structure achieves multifunctional integration of data storage and logic computing, effectively addressing energy consumption and time delay issues in data transmission, making it highly significant for applications in data-intensive and low-power integrated circuits. Von Neumann architecture ; Computer data storage ; Logic gate ; Non-volatile memory ; Transistor ; Ferroelectricity ; AND gate ; XOR gate 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Sensor and Energy Harvesting Materials
Authors: LI XINCAI | LI JIAYU
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Infused Multimodal Intelligence Computing Architecture for Sustainable Green Material Discovery and Eco-Performance Prediction Field (mathematics) ; Sustainability ; Warning system ; Predictive modelling ; Quality (philosophy) ; Life-cycle assessment ; Sustainable development ; Sustainable design Machine Learning in Materials Science ; Environmental Impact and Sustainability ; Digital Transformation in Industry
Authors: LI  Yakang, LI  Jianfang, HU  Peng, CHEN  Juan, WANG  Shengxiang, QI  Fazhi, CHEN  Gang
Source: SHILAP Revista de lepidopterología (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

A Survey of Artificial Intelligence Applications Throughout the Full Lifecycle of Neutron Scattering Experiments This study explores the use of Artificial Intelligence (AI) technology throughout the neutron scattering experiments′ lifecycle to determine how AI technology can revolutionize key aspects such as experimental apparatus, data acquisition, and data processing. The study begins by introducing the fundamental principles and experimental procedures of neutron scattering technology before focusing on the multifaceted applications of AI technology in neutron scattering experiments. These applications include optimizing experimental infrastructure, data acquisition, and imaging preprocessing, as well as characterizing experimental samples in neutron diffraction, neutron reflection, and Inelastic Neutron Scattering (INS). This study demonstrates the importance of AI technology in increasing the intelligence level of experiments, accelerating data processing, and improving the accuracy and reliability of data analyses. In addition, an in-depth discussion is held on the future application of AI technology in neutron scattering experiments, indicating that with the continuous advancement of technologies such as multimodal learning, interpretable models, large language models, and AI-Ready databases, AI technology is poised to bring revolutionary changes to neutron scattering experiments, opening up new avenues for revealing the microstructure and properties of complex material systems. Neutron scattering ; Neutron ; Small-angle neutron scattering ; Scattering ; Experimental data ; Data acquisition ; Reliability (semiconductor) ; Key (lock) Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: R. Wang | Boxiao Wang | Kai Li | Yifan Zhang | Jian Cheng
Source: ArXiv.org (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience Symbolic regression is a fundamental tool for discovering interpretable mathematical expressions from data, with broad applications across scientific and engineering domains. Recently, large language models (LLMs) have demonstrated strong performance in this task, leveraging embedded scientific priors and reasoning capabilities to surpass traditional methods. However, existing LLM-based approaches, such as LLM-SR, often over-rely on internal priors, lacking explicit data understanding and systematic reflection during equation generation. To address these limitations, we propose DrSR (Dual Reasoning Symbolic Regression), a framework that combines data-driven insight with reflective learning to enhance both robustness and discovery capability. Specifically, DrSR guides LLMs to analyze structural relationships (e.g., monotonicity, nonlinearity, and correlation) within the data to generate structured descriptions. Simultaneously, it monitors equation performance and establishes a feedback loop to refine subsequent generations. By integrating data understanding and generation reflection in a closed loop, DrSR enables more efficient exploration of the symbolic expression space. Experiments across interdisciplinary datasets in physics, chemistry, biology, and materials science demonstrate that DrSR substantially improves the valid equation rate and consistently outperforms both classical and recent LLM-based methods in terms of accuracy, generalization, and search efficiency. These results underscore its potential for scientific equation discovery. Structural equation modeling ; Robustness (evolution) ; Reflection (computer programming) ; Scientific discovery ; Scientific reasoning ; Dual (grammatical number) ; Data modeling Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Source: Journal of Physics Conference Series (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Preface The 2024 5 th International Conference on Advanced Materials and Intelligent Manufacturing (ICAMIM 2024) has gracefully concluded in Guangzhou, China, from November 8 th to 10 th , 2024. This prestigious conference, held under the banner of Guangzhou’s “City of International Academic Conferences” initiative, brought together a diverse array of experts, scholars, researchers, entrepreneurs, and other professionals from across the globe. They converged to share their latest research findings, discuss cutting-edge ideas, and explore academic challenges within the realm of advanced materials and intelligent manufacturing. The development of advanced materials and their corresponding technologies and industries has been regarded as a “national development strategy” for the world’s leading countries and has gradually developed into a new “trump card” in the game among countries. Intelligent manufacturing, with the most technological innovation value, requires a high level of knowledge and technology in the equipment manufacturing industry and a high degree of integration and application of core technologies, new materials, and components. Therefore, ICAMIM 2024 was held to share and discuss the latest research findings in these two domains. The conference witnessed an impressive turnout, with participants hailing from diverse academic institutions, research centers, and industrial enterprises across the continents. Renowned experts and emerging talents congregated to share their insights and discoveries. The keynote addresses, delivered by eminent authorities in the fields, were a cornerstone of the event. These luminaries expounded on a spectrum of crucial topics, such as the revolutionary impact of nanomaterials on the performance of intelligent manufacturing systems, the synergy between materials science and artificial intelligence in product design and optimization, and the sustainable development strategies for advanced materials in the context of modern manufacturing. List of Committee Member is available in this pdf. Computer science Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Digital Transformation in Industry
Authors: Nidhisree C | Panchami Dinesh | Baishali Garai | Ananya Paul | Rajat Subhra Bhowmick
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Automating Knowledge Discovery in Material Science with RAG Framework The rapid growth of material science research has led to an unprecedented surge in the volume and complexity of scientific data, encompassing experimental results, computational simulations, theoretical models, and industrial applications. Traditional keyword-based search methods, while widely used, are increasingly insufficient for extracting meaningful insights from such heterogeneous and semantically rich datasets. Re-searchers often face challenges in locating relevant information hidden within vast repositories of publications, patents, and databases, slowing down the discovery of novel materials and the advancement of sustainable technologies. Recent developments in Artificial Intelligence (AI) and Natural Language Processing (NLP) offer promising avenues to overcome these challenges. In particular, Retrieval-Augmented Generation (RAG) frameworks combine the strengths of Large Language Models (LLMs) with powerful information retrieval mechanisms to deliver context-aware, semantically accurate responses. Unlike conventional retrieval systems that rely on surface-level keyword matches, RAG-based approaches leverage embeddings and source information to capture deeper semantic relationships across scientific literature and databases. To support this study, a domain-specific dataset was curated from unstructured PDF documents and preprocessed into structured text segments for training and evaluation. Experimental results show that the proposed RAG pipeline, powered by MatSciBERT embeddings, achieves strong performance with an overall accuracy of 0.784 and an F1-score of 0.858. These results demonstrate the effectiveness of the approach in automating semantic data retrieval in the materials science domain. Leverage (statistics) ; Knowledge extraction ; Scientific discovery ; Natural language ; Semantic technology ; Natural language understanding ; Question answering ; Face (sociological concept) Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Advanced Graph Neural Networks
Authors: Shih-Hung Cheng | Yun-Siang Syu | Er-Feng Hsieh | Wen–Jeng Hsueh
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-performance gate-tunable spin valves based on transition metal dichalcogenides in the presence of phonon scattering Condensed matter physics ; Phonon ; Scattering ; Materials science ; Transition metal ; Phonon scattering ; Spin (aerodynamics) ; Optoelectronics ; Physics ; Chemistry ; Optics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Pedro Gabriel C. de Lucena | Flávia L. R. Lessa | Jhonattas de Carvalho Carregosa | Marcos N. Eberlin | Alberto Wisniewski | Jandyson M. Santos
Source: Energy & Fuels (article, 2025-10-30)
Field: Chemistry — Analytical Chemistry

Molecular Patterns of Crude Oils with Varying Naphthenic Acidity Revealed by ESI(−)-FT-ICR MS and Unsupervised Learning Acidic crude oils are becoming more prominent in global markets due to technological advances that enable the exploitation of unconventional reserves. Understanding their chemical nature is essential for refining strategies, especially as acidity poses processing challenges. Unsupervised pattern recognition offers a way to uncover meaningful relationships between molecular composition and macroscopic acidity, reducing the manual interpretation effort. This study proposes an unsupervised learning framework to reveal such patterns efficiently. Crude oil samples from the Sergipe-Alagoas Basin (northeast Brazil) were analyzed using negative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(−)-FT-ICR MS), focusing on polar compounds. Bulk acidity was assessed through total acid number (TAN), naphthenic acidity index (NAI), and percentage of naphthenic acids (%NA). K-means clustering applied to these parameters identified four acidity-level groups. Clusters I and II contained low-TAN oils (<1 mg KOH g–1), differing in %NA, while Clusters III and IV grouped high-TAN oils (>1 mg KOH g–1), also separated by %NA. Multidimensional scaling (MDS) was applied to the autoscaled abundance of globally shared molecular formulas from the O1–O4 oxygen classes. While individual classes had limited discriminatory power, combined O1–O3 formulas enabled clear differentiation among acidity levels. This framework demonstrates that the data-driven analysis of high-resolution mass spectrometry data can be automated to enhance the molecular understanding of acidic oils, providing valuable insights for refining decisions and linking molecular fingerprints to macroscopic acidity behavior. Naphthenic acid ; Refining (metallurgy) ; Electrospray ionization ; Fourier transform ion cyclotron resonance ; Mass spectrometry ; Cluster analysis ; Compositional data ; Unsupervised learning Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Lubricants and Their Additives
Authors: Renquan Lv | Weiwei Han | Zixu Zeng | Yi He | Lecheng Lei | Ping Li | Xingwang Zhang
Source: ACS Applied Nano Materials (article, 2025-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning for Controlling the Particle Size Distribution of TiO<sub><b>2</b></sub> Nanoparticles in Optical Nanocomposites To achieve precise control over the particle size distribution (PSD) of titanium oxide (TiO2) nanoparticles (NPs) in optical nanocomposites, this study introduces a GBDT-RR (Gradient Boosted Decision Tree-Rosin-Rammler) framework to guide their synthesis. We first constructed a TiO2 synthesis-PSD data set based on hydrodynamic diameter by combining Syn-ext Llama with manual collaboration. Based on this data set, we integrated a GBDT model for high-precision prediction with an RR model for concise PSD representation. Moreover, this framework was embedded into a Bayesian optimization (BO) algorithm (GBDT-RR with BO) to develop an efficient synthesis reverse design platform. Experimental validation confirmed the platform’s efficiency, with the computational process to guide the synthesis of 15 nm TiO2 NPs taking only 5 min. Feature importance analysis offered mechanistic insights into the nucleation process. Additionally, its versatility was proven by successful extension to ZrO2 and CeO2 NPs. In summary, the GBDT-RR framework and its associated synthesis reverse design platform represent a powerful, data-driven tool for the precise and efficient optimization of TiO2 NP PSDs, facilitating their applications in optical nanocomposites. Nanocomposite ; Nanoparticle ; Materials science ; Particle-size distribution ; Particle size ; Distribution (mathematics) ; Particle (ecology) ; Nanotechnology ; Chemical engineering ; Mathematics ; Engineering ; Mathematical analysis Gold and Silver Nanoparticles Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Quantum Dots Synthesis And Properties
Authors: Zhengyu Zhang | Jun Li | Zegeng Chen | Xinqi Wang | Zhongxiang Zhou
Source:  (article, 2025-11-04)
Field: Chemistry — Organic Chemistry

Enhanced Dielectric Loss in NiS/Graghene for Optimizing Microwave Absorption The advancement of the artificial intelligence has greatly promoted the application of related electronic devices, which inevitably leads to increasingly serious electromagnetic pollution problems. Microwave absorption (MA) materials have received widespread attention since they can eliminate electromagnetic pollution. Metal sulfides absorbers can be easily combined with other carbon materials to flexibly adjust their permittivity, thereby optimizing their attenuation characteristics. However, Monometallic sulfides are frustrating due to their low electrical conductivity and single polarization behavior when confronted with increasingly complex electromagnetic environments. Herein, assisted by the solvothermal method, NiS/graphene oxide composites (NiS/G), in which the dielectric loss and the impedance matching was optimized by regulating the proportion of graphene oxide, were prepared. The experimented results demonstrate that granular NiS is loaded on graphene oxide, and the interfacial polarization behavior occurs at the NiS/graphene oxide interface under the electromagnetic fields. Meanwhile, the increase in the content of graphene oxide enhances their conduction loss behavior. As a result, this strategy has optimized their microwave absorption performances, exhibiting an improved reflection loss <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(R L)$</tex> of −41.72 dB and an effective absorption bandwidth (EAB) of 3.28 GHz. Therefore, this research provides an idea for solving other chalcogenides with low electrical conductivity or poor polarization loss. Graphene ; Microwave ; Reflection loss ; Dielectric loss ; Oxide ; Dielectric ; Attenuation ; Electromagnetic radiation ; Impedance matching Microwave-Assisted Synthesis and Applications ; Electromagnetic wave absorption materials ; Microwave Dielectric Ceramics Synthesis
Authors: Dwarasala Lakshmi kumari | Divya kandavalli | K. Padmalatha
Source: Trends in Pharmaceuticals and Nanotechnology (article, 2025-01-01)
Field: Materials Science — Biomaterials

Advancing Nanocarrier-Based Drug Delivery: Precision Nanocarrier‐based drug delivery systems have emerged as a transformative approach in modern therapeutics by enabling precise, site‐specific drug release, enhanced bioavailability, and improved stability of therapeutic agents. These nanoscale platforms overcome many limitations of conventional formulations, including poor aqueous solubility, rapid clearance, and off‐target toxicity. In this review, we provide a comprehensive examination of various nanocarrier platforms including lipid‐based carriers, polymeric vehicles, dendrimers, inorganic nanoparticles, and hybrid assemblies. We discuss each system's fundamental properties that permit effective drug protection, controlled release kinetics, and targeted delivery. Additionally, three principal targeting strategies passive, active, and stimuli‐responsive are evaluated alongside key pharmacokinetic and biodistribution parameters. Recent innovations, such as artificial intelligence-assisted design, CRISPR-based delivery systems, and personalized nanomedicine, are explored. Challenges related to biocompatibility, manufacturing complexities, immune clearance, and regulatory hurdles are also addressed. Finally, we propose future directions that, through interdisciplinary collaboration, will advance nanocarrier‐based systems toward next‐generation precision therapeutics with improved patient outcomes and minimize adverse effects. Nanocarriers ; Drug delivery ; Drug ; Nanotechnology ; Computer science ; Targeted drug delivery ; Medicine ; Pharmacology ; Materials science Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems
Authors: Shuo Liu | Yuan Cheng | Wen Xiao | Yaru Li | Yidi Li | Jiajin Li | Lixia Bao
Source: ACS Applied Nano Materials (article, 2025-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tuning Oxygen Vacancy Concentration in Defect-Engineered Black TiO <sub>2</sub> Nanomaterials for Synergistically Enhanced Microwave Absorption Performance With the rapid advancement of 5G communication and artificial intelligence technologies, the ubiquitous deployment of electronic apparatuses has raised pressing issues relating to signal interference and electromagnetic pollution, driving intensive research into microwave-absorbing materials. This article proposed a defect engineering-driven strategy to endow nanostructured TiO2 with excellent microwave absorption performance through the synergistic optimization of oxygen vacancies and core–shell nanostructure design. By precisely controlling the hydrogenation temperature (300–450 °C) and TiO2/NaBH4 ratio (1–4), we achieved tunable microwave absorption performance. Under optimal conditions, the nanomaterial exhibited a RLmin of −32.95 dB and an effective absorption bandwidth of 2.794 GHz, showing great promise for electromagnetic interference shielding in next-generation communication devices and radar systems. The concentration of oxygen vacancies was the core factor affecting the absorption performance. Incorporating oxygen vacancies not only enhanced defect-induced polarization and interface polarization effects but also strengthened conductivity loss by increasing carrier concentration. The collaborative interplay of these three mechanisms endows the nanostructured material with a superior microwave attenuation capability. The findings provide theoretical and practical guidance for developing lightweight, broadband, and highly efficient black TiO2-derived microwave absorbers. Our findings offer practical guidance for developing advanced microwave absorbers for next-generation aerospace radar and communication systems. Microwave ; Nanomaterials ; Attenuation ; Absorption (acoustics) ; Radar ; Nanoparticle ; Nanostructure ; Polarization (electrochemistry) Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; MXene and MAX Phase Materials