jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nuan Zheng | Hyuntai Kim
Source: Asia-pacific Journal of Convergent Research Interchange (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

The Transformative Role of Generative AI in Music Production and Creation: A CiteSpace-Based Bibliometric Analysis The rapid advancement of generative artificial intelligence (Generative AI) is reshaping the landscape of music production, transforming creative methodologies, technological processes, and aesthetic value systems.Based on 630 publications retrieved from the Web of Science Core Collection, this study employs CiteSpace (version 6.4.R1) to perform a systematic bibliometric and visualization analysis of Generative AI in music.The overall network shows a modularity Q value of 0.7326 and a mean silhouette coefficient of 0.9144, indicating high structural and clustering reliability.The field exhibits a progressive evolution through three stages: early explorations in algorithmic composition and model design, mid-term expansion driven by deep learning and multimodal generation, and recent advances in human-AI co-creation and interactive composition.The United States and China form a dual-core collaboration structure, while Queen Mary University of London and Tsinghua University emerge as leading institutions.The keyword clustering results indicate that the research field is structured around three interrelated thematic groups, collectively illustrating a paradigm shift from technology-driven exploration toward a more integrated trajectory that encompasses artistic expression and ethical reflection.This study not only maps the dynamic knowledge structure of Generative AI music research but also proposes a "Technology-Creation-Value-Feedback" conceptual framework, offering quantitative insights and strategic implications for artistic practice, industrial innovation, and copyright governance. Transformative learning ; Production (economics) ; Generative grammar ; Knowledge production ; Perspective (graphical) ; Reflexivity Machine Learning in Materials Science ; Music Technology and Sound Studies ; Artificial Intelligence in Healthcare and Education
Authors: Si-Yuan Wang | Qi-yang Liu | Ailing Tan | Linan Liu
Source: Processes (article, 2026-01-22)
Field: Chemistry — Organic Chemistry

Edible Oil Adulteration Analysis via QPCA and PSO-LSSVR Based on 3D-FS A method utilizing quaternion principal component analysis (QPCA) for three-dimensional fluorescence spectral (3D FS) feature extraction is employed to identify frying oil in edible oil. Particle swarm optimization partial least squares support vector machine (PSO-LSSVR) is utilized for detecting frying oil concentration. The study includes rapeseed oil, soybean oil, peanut oil, blending oil, and corn oil samples. Adulteration involves adding frying oil to these edible oils at concentrations of 0%, 5%, 10%, 30%, 50%, 70%, and 100%. Firstly, the F7000 fluorescence spectrometer is employed to measure the 3D FS of the adulterated edible oil samples, resulting in the generation of contour maps and 3D FS projections. The excitation wavelengths utilized in these measurements are 360 nm, 380 nm, and 400 nm, while the emission wavelengths span from 220 nm to 900 nm. Secondly, leveraging the automatic peak-finding function of the spectrometer, a quaternion parallel representation model of the 3D FS data for frying oil in edible oil is established using the emission spectra data corresponding to the aforementioned excitation wavelengths. Subsequently, in conjunction with the K-nearest neighbor classification (KNN), three feature extraction methods—summation, modulus, and multiplication quaternion feature extraction—are compared to identify the optimal approach. Thirdly, the extracted features are input into KNN, particle swarm optimization support vector machine (PSO-SVM), and genetic algorithm support vector machine (GA-SVM) classifiers to ascertain the most effective discriminant model for adulterated edible oil. Ultimately, a quantitative model for adulterated edible oil is developed based on partial least squares regression, PSO-SVR and PSO-LSSVR. The results indicate that the classification accuracy of QPCA features combined with PSO-SVM achieved 100%. Furthermore, the PSO-LSSVR quantitative model exhibited the best performance. Linear discriminant analysis ; Principal component analysis ; Pattern recognition (psychology) ; Edible oil ; Support vector machine ; Particle swarm optimization ; Partial least squares regression Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Oil Spill Detection and Mitigation
Authors: Erin G. Teich | Jason Z. Kim | Dani S. Bassett
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Linear control theory for jammed particle systems Amorphous particulate matter constitutes a wide range of natural and synthetic materials. Despite this ubiquity, the way in which these systems' disordered microstructure couples to their often subtle and complex dynamical behavior is not yet fully understood, with profound consequences for phenomena ranging from landscape evolution to cellular unjamming during tumor metastasis. With this paper, we introduce tools from linear control theory that quantify system response to external input, and demonstrate their utility in elucidating the dynamics of jammed amorphous materials under stress. Our results indicate that average controllability, the response of a system to perturbation, strongly correlates with particle rearrangement in systems subject to quasistatic shear, implying that average controllability is an accurate predictor of rearrangement dynamics in certain contexts. Moreover, we show that the time scale over which average controllability is calculated can be tuned to optimize its predictive capacity for particle rearrangement. Values of the optimal time scale provide physical insight into the system; namely, that multiple rearranging particles participate on average in vibrational eigenmodes of lower and lower energy as the system is sheared until the rearrangement event. Broadly, our study demonstrates that linear control theory is a promising mathematical framework for predicting and designing mechanical response in disordered media. Quasistatic process ; Controllability ; Scale (ratio) ; Particle (ecology) ; Particle system ; Range (aeronautics) ; Linear system ; Amorphous solid ; Control theory (sociology) ; Particle dynamics Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Micro and Nano Robotics
Authors: Hadi M. Dolatabadi | Thalaiyasingam Ajanthan | Sameera Ramasinghe | Chamin P Hewa Koneputugodage | Shamane Siriwardhana | Violetta Shevchenko | Karol Pająk | James Snewin | Gil Avraham | Alexander Long
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

NuMuon: Nuclear-Norm-Constrained Muon for Compressible LLM Training The rapid progress of large language models (LLMs) is increasingly constrained by memory and deployment costs, motivating compression methods for practical deployment. Many state-of-the-art compression pipelines leverage the low-rank structure of trained weight matrices, a phenomenon often associated with the properties of popular optimizers such as Adam. In this context, Muon is a recently proposed optimizer that improves LLM pretraining via full-rank update steps, but its induced weight-space structure has not been characterized yet. In this work, we report a surprising empirical finding: despite imposing full-rank updates, Muon-trained models exhibit pronounced low-rank structure in their weight matrices and are readily compressible under standard pipelines. Motivated by this insight, we propose NuMuon, which augments Muon with a nuclear-norm constraint on the update direction, further constraining the learned weights toward low-rank structure. Across billion-parameter-scale models, we show that NuMuon increases weight compressibility and improves post-compression model quality under state-of-the-art LLM compression pipelines while retaining Muon's favorable convergence behavior. Compressibility ; Compression (physics) ; Leverage (statistics) ; Muon ; Convergence (economics) ; Pipeline transport Machine Learning in Materials Science ; Big Data and Digital Economy ; Stochastic Gradient Optimization Techniques
Authors: Yanchang Liang | Xiaowei Zhao
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

SimuAgent: An LLM-Based Simulink Modeling Assistant Enhanced with Reinforcement Learning Large language models (LLMs) have revolutionized text-based code automation, but their potential in graph-oriented engineering workflows remains under-explored. We introduce SimuAgent, an LLM-powered modeling and simulation agent tailored for Simulink. SimuAgent replaces verbose XML with a concise, dictionary-style Python representation, dramatically cutting token counts, improving interpretability, and enabling fast, in-process simulation. A lightweight plan-execute architecture, trained in two stages, equips the agent with both low-level tool skills and high-level design reasoning. To tackle sparse rewards in long-horizon tasks, we propose Reflection-GRPO (ReGRPO), which augments Group Relative Policy Optimization (GRPO) with self-reflection traces that supply rich intermediate feedback, accelerating convergence and boosting robustness. Experiments on SimuBench, our newly released benchmark comprising 5300 multi-domain modeling tasks, show that a Qwen2.5-7B model fine-tuned with SimuAgent converges faster and achieves higher modeling accuracy than standard RL baselines, and even surpasses GPT-4o when evaluated with few-shot prompting on the same benchmark. Ablations confirm that the two-stage curriculum and abstract-reconstruct data augmentation further enhance generalization. SimuAgent trains and runs entirely on-premise with modest hardware, delivering a privacy-preserving, cost-effective solution for industrial model-driven engineering. SimuAgent bridges the gap between LLMs and graphical modeling environments, offering a practical solution for AI-assisted engineering design in industrial settings. Python (programming language) ; Workflow ; Boosting (machine learning) ; Bridging (networking) ; Reinforcement learning ; Benchmark (surveying) ; XML ; Systems Modeling Language Machine Learning in Materials Science ; Topic Modeling ; Model-Driven Software Engineering Techniques
Authors: Xingze Zou | Jing Wang | Yuhua Zheng | Xueyi Chen | Haolei Bai | Lingcheng Kong | Syed A. R. Abu-Bakar | Zhaode Wang | Chengfei Lv | Haoji Hu | Huan Wang
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices? Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile devices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices? To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated pipeline that bridges the host-device gap for on-device verification. Leveraging this framework, we conduct extensive evaluation on the CPU backend of Mobile Neural Network (MNN), revealing that current LLMs struggle with the engineering complexity and data scarcity inherent to mobile frameworks; standard models and even fine-tuned variants exhibit high compilation failure rates (over 54%) and negligible performance gains due to hallucinations and a lack of domain-specific grounding. To overcome these limitations, we propose the Mobile Kernel Agent (MoKA), a multi-agent system equipped with repository-aware reasoning and a plan-and-execute paradigm. Validated on MobileKernelBench, MoKA achieves state-of-the-art performance, boosting compilation success to 93.7% and enabling 27.4% of generated kernels to deliver measurable speedups over native libraries. Pipeline (software) ; Boosting (machine learning) ; Benchmark (surveying) ; Mobile device ; Mobile computing ; Cellular network ; Kernel (algebra) ; Domain (mathematical analysis) Machine Learning in Materials Science ; Topic Modeling ; Software System Performance and Reliability
Authors: ABDESSAMAD BOURKIBATE
Source: Research Square (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

Entropy-Driven Gradient Stability in Large Language Models: A Non-Equilibrium Thermodynamic Framework for Reinforcement Learning Optimization Reinforcement learning ; Gradient descent ; Maximization ; Optimization problem ; Stability (learning theory) ; Balanced flow ; Helmholtz free energy ; Heuristic Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Model Reduction and Neural Networks
Authors: Lei Guo
Source: Textile & Leather Review (article, 2026-03-31)
Field: Materials Science — Polymers and Plastics

Parameter Optimization of Functional Textile Materials for Building Sunshade Based on Mul-ti-Objective Genetic Algorithm Existing functional textile materials for building shading lack effective methods for parameter coordination and optimization across multiple performance objectives, making it difficult to simultaneously meet comprehensive requirements for shading, light transmission, and energy savings. To address this issue, this paper proposes a pa-rameter optimization framework based on a multi-objective genetic algorithm (MOGA). It constructs a perfor-mance model with fabric density, coating thickness, and fiber thermal conductivity as variables, and shading effi-ciency, visible light transmittance, and thermal resistance as objective functions. MOGA is employed to optimize these parameters and obtain an optimal solution set that achieves coordinated multi-performance. Experimental results show that changes in coating thickness and thermal conductivity significantly affect visible light transmit-tance and thermal resistance, verifying the effectiveness of the multi-objective genetic algorithm for optimizing building shading materials. This has important engineering application value. Coating ; Genetic algorithm ; Shading ; Textile ; Thermal conductivity ; Thermal ; Set (abstract data type) ; Structured light Textile materials and evaluations ; Architecture and Computational Design ; Building Energy and Comfort Optimization
Authors: Xue Fan | Wei Zou | M. K. Moghimi | Wei Yi
Source: Journal of Cloud Computing Advances Systems and Applications (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Integrating AI and cloud-edge technologies for music creation in educational and performance domains This paper provides a systematic exploration of Artificial Intelligence (AI) for music generation within a cloud-edge synergy architecture (i.e. a coordinated cloud–edge computing paradigm where the cloud handles compute-intensive training and high-fidelity generation, while the edge supports latency-sensitive and privacy-critical inference and interaction), focusing on its deep integration and practical applications across educational and performance ecosystems. It begins by reviewing the evolution of key technologies, from early Recurrent Neural Networks (RNNs/LSTMs) and Generative Adversarial Networks (GANs) to the current mainstream Transformer architectures and Diffusion Models, highlighting breakthroughs in handling long-range dependencies, fidelity, and controllability. The core of the paper delves into cloud-edge collaborative system architectures designed for scenarios involving music generation, real-time interaction, and multimodal alignment. It provides an in-depth analysis of four primary collaboration models—Edge-first, Cloud-first, Split Inference/Learning, and Federated Personalization—detailing their characteristics, suitable application scenarios, and inherent trade-offs. Furthermore, the paper comprehensively examines how this architecture empowers the educational ecosystem by enabling resource democratization, personalized learning, and pedagogical innovation. Finally, the paper identifies prevailing challenges at the system, model, ethical/copyright, and privacy/security levels, and outlines future research directions, including model-system co-design, multi-modal and XR integration, trustworthy copyright mechanisms, and standardized open-source ecosystems. This work argues that cloud-edge synergy is the key enabling technology for the scalable and deep application of AI-driven music creation in latency-sensitive and privacy-critical domains such as education and performance. Scalability ; Key (lock) ; Architecture ; Inference ; Deep learning ; Cloud computing ; Mainstream Machine Learning in Materials Science ; Music Technology and Sound Studies ; Scientific Computing and Data Management
Authors: Bowen Fang | Wen Ye | Su, Yunyue | Jinghao Zhang | Qiang Liu | Yesheng Liu | Xin Sun | Shu Wu | Jiabing Yang | Baole Wei | Liang Wang
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

ToolWeaver: Weaving Collaborative Semantics for Scalable Tool Use in Large Language Models Prevalent retrieval-based tool-use pipelines struggle with a dual semantic challenge: their retrievers often employ encoders that fail to capture complex semantics, while the Large Language Model (LLM) itself lacks intrinsic tool knowledge from its natural language pretraining. Generative methods offer a powerful alternative by unifying selection and execution, tasking the LLM to directly learn and generate tool identifiers. However, the common practice of mapping each tool to a unique new token introduces substantial limitations: it creates a scalability and generalization crisis, as the vocabulary size explodes and each tool is assigned a semantically isolated token. This approach also creates a semantic bottleneck that hinders the learning of collaborative tool relationships, as the model must infer them from sparse co-occurrences of monolithic tool IDs within a vast library. To address these limitations, we propose ToolWeaver, a novel generative tool learning framework that encodes tools into hierarchical sequences. This approach makes vocabulary expansion logarithmic to the number of tools. Crucially, it enables the model to learn collaborative patterns from the dense co-occurrence of shared codes, rather than the sparse co-occurrence of monolithic tool IDs. We generate these structured codes through a novel tokenization process designed to weave together a tool's intrinsic semantics with its extrinsic co-usage patterns. These structured codes are then integrated into the LLM through a generative alignment stage, where the model is fine-tuned to produce the hierarchical code sequences. Evaluation results with nearly 47,000 tools show that ToolWeaver significantly outperforms state-of-the-art methods, establishing a more scalable, generalizable, and semantically-aware foundation for advanced tool-augmented agents. Semantics (computer science) ; Scalability ; Vocabulary ; Generative grammar ; Automatic summarization ; Bottleneck ; Process (computing) ; Natural language Machine Learning in Materials Science ; Digital Humanities and Scholarship ; Software Engineering Research
Authors: Jackie Teddy Yik
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Self-Driving Lab for Battery Electrolyte Design As the transition to renewable energy accelerates, development of efficient, safe, and cost-effective energy storage systems become increasingly critical. Lithium-ion batteries dominate the current market, but their technological maturity motivates exploration of alternative chemistries, like aqueous zinc batteries, which offer safety and cost advantages. However, significant challenges remain before such systems can reach practical, industrial deployment. At the research level, battery testing is often time- and resource-intensive, with long testing protocols alongside process variability from factors such as environmental conditions, component quality, and assembly precision. Self-driving labs (coupling automation and data-driven experimentation) offer a pathway to improved reproducibility, higher throughput, and deeper insight into these electrochemical systems. This thesis presents ODACell, a self-driving lab designed for automated coin cell assembly, in-line battery cycling, and closed-loop electrolyte optimization. The initial ODACell prototype is described and evaluated using an aqueous lithium battery model system, revealing poor reproducibility that motivated hardware and workflow improvements: integration of computer vision, environmental enclosure, and increased space efficiency. The upgraded ODACell was applied to multiple electrolyte systems using Bayesian optimization as the experiment planner. Co-solvent effects (from acetonitrile, dimethyl sulfoxide, trimethyl phosphate, and water) on Coulombic efficiency (CE) were explored with ODACell, confirming the detrimental impact of water. In another study with conventional carbonate-based electrolytes, formation cycle CE was mapped for varying additive concentrations (of 1,3,2-dioxathiolane 2,2-dioxide, prop-1-ene-1,3-sultone, tris(trimethylsilyl) phosphite, and vinylene carbonate); Bayesian optimization combined with statistical analysis identified first-cycle benefits from specific additives while confirming known resistance increases from vinylene carbonate. Finally, ODACell was demonstrated on a zincsystem, highlighting its adaptability across chemistries. Optimization of zinc salts (ZnSO4 and ZnCl2) and additives (thiourea, ammonium phosphate monobasic, and sodium dodecyl sulfate) was supported by a separate advanced characterization technique to provide physiochemical explanations to observed CE effects. Overall, this thesis demonstrates the capabilities and challenges of ODACell as a self-driving lab for liquid electrolyte optimization in coin cell batteries. Battery (electricity) ; Workflow ; Automation ; Energy storage ; Flexibility (engineering) ; Electrolyte ; Adaptability ; Process (computing) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced battery technologies research