jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrei Dăescu | Alexandru Guzu | Georgian Nicolae | Claudius Dan
Source: Electronics Letters (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Evaluating the Impact of Circuit Representation on LLM‐Based Functional Block Recognition in Analogue Circuits ABSTRACT Large language models (LLMs) have shown increasing potential in analogue circuit analysis automation, yet their ability to understand and identify functional blocks can vary significantly depending on the representation format of the circuit. This paper investigates how circuit representation, flat SPICE Netlists versus structured PySpice code, influences LLM performance in functional block recognition tasks. Using a benchmark of ten analogue comparator circuits derived from a standard educational collection, we evaluate five state‐of‐the‐art LLMs across both representations. Each circuit is annotated with ground‐truth sub‐topologies, and models are prompted to extract these blocks in a standardized JSON format. Our results reveal that DeepSeek R1 achieves the highest average accuracy on Netlist inputs, while GPT 5 provides the most balanced performance across both formats. LLaMA 4 shows a slight advantage on PySpice compared to Netlist, indicating that semantic cues in structured code can benefit certain models. Overall, most models still perform better on Netlist than on PySpice, demonstrating that code‐structured representations do not generically improve performance and highlighting the importance of representation format selection in LLM‐driven electronic design automation. Netlist ; Representation (politics) ; Benchmark (surveying) ; Block (permutation group theory) ; Spice ; Electronic circuit ; Code (set theory) ; Pooling Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Neural Network Applications
Authors: Srikanth Basety | Renuka Gudepu | Aditya Velidandi
Source: Pharmaceutics (article, 2026-02-02)
Field: Materials Science — Biomaterials

Smart Nanoformulations for Oncology: A Review on Overcoming Biological Barriers with Active Targeting, Stimuli-Responsive, and Controlled Release for Effective Drug Delivery Effective drug delivery in oncology is challenged by a hierarchy of biological barriers-from abnormal vasculature and dense stroma to cellular immunosuppression and specialized interfaces like the blood-brain barrier. This review provides a contemporary analysis of smart nanoformulations through the lens of a rational, stage-gated design pipeline. We first deconstruct the solid tumor microenvironment as a multi-tiered obstacle (systemic, stromal, cellular), establishing a barrier-specific foundation for nanocarrier design. The core of the review articulates an architectural toolkit, detailing how intrinsic nanoparticle properties precondition in vivo identity via the protein corona, which in turn informs the selection of advanced ligands for cellular targeting and programmed intracellular trafficking. This integrated framework sets the stage for exploring sophisticated applications, including endogenous and externally triggered responsive systems, bio-orthogonal activation, immuno-nanoformulations, and combination strategies aimed at overcoming multidrug resistance. By synthesizing these components into a cohesive design philosophy, this review moves beyond a catalog of advances to offer a blueprint for engineering next-generation nanotherapeutics. We critically assess the translational landscape and contend that this hierarchical design approach is essential for developing more effective, personalized, and clinically viable cancer treatments. Nanocarriers ; Drug delivery ; Controlled release ; Tumor microenvironment ; Cancer ; Blueprint Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Z. Merrick Li | Fang Gao | Dongdong Liu
Source: Computation (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Reinforcement Learning-Based Inverse Design of Multilayer Particles Multilayered particles possess exceptional optical properties and hold significant potential for applications in chemical analysis, life sciences, optical sensing, and photonic integration. In practical applications, however, it is often necessary to perform inverse design of multilayered particles with given optical characteristics to meet specific requirements, a process that remains time-consuming. To overcome this challenge, we propose a reinforcement learning-based method for the automated design of multilayered particles. Leveraging the self-learning capacity of reinforcement learning models in combination with an optical characteristics calculation model, the method iteratively determines particle parameters that fulfill the desired optical responses. This method effectively addresses the many-to-one parameter mapping problem in inverse design, eliminates the need for extensive pre-computations, and provides an innovative approach to the automated design of complex nanostructures. Inverse ; Process (computing) ; Reinforcement ; Inverse problem ; Reinforcement learning ; Photonics ; Particle (ecology) Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Block Copolymer Self-Assembly
Authors: Yuhang Zhou | Kai Zheng | Qiguang Chen | Mengkang Hu | Qingfeng Sun | Can Xu | Jingjing Chen
Source: arXiv (Cornell University) (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents Deep research agents have shown remarkable potential in handling long-horizon tasks. However, state-of-the-art performance typically relies on online reinforcement learning (RL), which is financially expensive due to extensive API calls. While offline training offers a more efficient alternative, its progress is hindered by the scarcity of high-quality research trajectories. In this paper, we demonstrate that expensive online reinforcement learning is not all you need to build powerful research agents. To bridge this gap, we introduce a fully open-source suite designed for effective offline training. Our core contributions include DeepForge, a ready-to-use task synthesis framework that generates large-scale research queries without heavy preprocessing; and a curated collection of 66k QA pairs, 33k SFT trajectories, and 21k DPO pairs. Leveraging these resources, we train OffSeeker (8B), a model developed entirely offline. Extensive evaluations across six benchmarks show that OffSeeker not only leads among similar-sized agents but also remains competitive with 30B-parameter systems trained via heavy online RL. Reinforcement learning ; Suite ; Task (project management) ; Scarcity ; Bridge (graph theory) ; Deep learning ; Key (lock) Machine Learning in Materials Science ; Machine Learning and Data Classification ; Multimodal Machine Learning Applications
Authors: Andrés Alonso-Robisco | Carlos Esparcia | Francisco Sánchez Jareño
Source: arXiv (Cornell University) (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

On the Carbon Footprint of Economic Research in the Age of Generative AI Generative artificial intelligence (AI) is increasingly used to write and refactor research code, expanding computational workflows. At the same time, Green AI research has largely measured the footprint of models rather than the downstream workflows in which GenAI is a tool. We shift the unit of analysis from models to workflows and treat prompts as decision policies that allocate discretion between researcher and system, governing what is executed and when iteration stops. We contribute in two ways. First, we map the recent Green AI literature into seven themes: training footprint is the largest cluster, while inference efficiency and system level optimisation are growing rapidly, alongside measurement protocols, green algorithms, governance, and security and efficiency trade-offs. Second, we benchmark a modern economic survey workflow, an LDA-based literature mapping implemented with GenAI assisted coding and executed in a fixed cloud notebook, measuring runtime and estimated CO2e with CodeCarbon. Injecting generic green language into prompts has no reliable effect, whereas operational constraints and decision rule prompts deliver large and stable footprint reductions while preserving decision equivalent topic outputs. The results identify human in the loop governance as a practical lever to align GenAI productivity with environmental efficiency. Carbon footprint ; Footprint ; Workflow ; Corporate governance ; Cloud computing ; Discretion ; Productivity ; Benchmark (surveying) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Green IT and Sustainability
Authors: Mrinmoy Chakraborty
Source: ChemRxiv (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

N 12 as a Zero-Carbon Energy Carrier: A CL5D Framework Analysis of Polynitrogen High-Energy-Density Materials Polynitrogen compounds represent the frontier of zero-carbon high-energy-density materials (HEDMs), releasing energy exclusively as molecular nitrogen (N 2) with no carbon emissions.Building on our prior computational study of the N 12 cage [1] and the CL5D spectroscopic hysteresis framework [2-4], we analyse N 12 as a candidate zero-carbon energy carrier in the context of computationally predicted neon-nitrogen compounds (NeN 10 , NeN 22 , t-N 20) reported by Lin et al. [5].The N 12 cage, with a computed thermodynamic energy density of 67.3 kJ/g (16.1TNT, 5.6 Lin et al.'s best compound), sits at the apex of the polynitrogen energy ladder.The CL5D convergence score C n (P) is applied to identify the pressure window most favourable for N 12 synthesis and storage, and the hysteresis coefficient # N12 = 8.28 10 #6 is proposed as a regulatory parameter for fuel cell energy conversion.A conceptual N 12-based fuel cell cycle is outlined, yielding an estimated useful energy output of 40.4 kJ/g at 60% conversion efficiency-approximately 2 the output of a hydrogen fuel cell per gram of fuel.All results are computational; experimental synthesis of N 12 remains the critical enabling step. Energy (signal processing) ; Field (mathematics) ; Raw material ; Component (thermodynamics) ; Energy consumption ; Work (physics) Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Boron and Carbon Nanomaterials Research
Authors: Ranil Vikraman Kumarasamy | Prabhu Manickam Natarajan | Isai Mathivanan | M. Balasubramaniam | Suresh SN | Dhamodharan Prabhu | Kamaraj Raju | Monica Mironescu | Ion Dan Mironescu
Source: Frontiers in Nanotechnology (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Sustainable biogenic synthesis of silver nanoparticles for oral cancer: a comprehensive review The increasing incidence of oral malignancies, coupled with the limitations of conventional treatments such as toxicity and drug resistance, has driven the exploration of novel therapeutic approaches. Silver nanoparticles (AgNPs) have emerged as promising anticancer agents due to their distinctive physicochemical attributes, which facilitate antimicrobial, anti-inflammatory, and tumor-suppressive activities. Unlike traditional chemical or physical synthesis methods, plant-mediated green synthesis offers a sustainable and ecologically sound alternative, leveraging the natural reducing and stabilizing compounds inherent in botanical extracts. This review provides a detailed analysis of contemporary advancements in the eco-conscious production of AgNPs using diverse plant sources and their potential role in addressing oral cancer. Furthermore, the article evaluates the cytotoxic impact of these biogenic nanoparticles on oral cancer cell models, elucidating molecular pathways such as oxidative stress induction, apoptosis activation, and inhibition of proliferative signaling. Clinical implications are explored, emphasizing the balance between therapeutic efficacy and biocompatibility in normal cells. While plant-derived AgNPs present a groundbreaking avenue for targeted oral cancer therapy, challenges such as scalability, standardization, and long-term safety require resolution for successful clinical translation. This synthesis of current knowledge aims to inspire innovative, nature-driven strategies to enhance oral oncology outcomes. Silver nanoparticle ; Biocompatibility ; Cancer therapy ; Cancer ; Cancer treatment ; Drug discovery Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Diverse Scientific Research Studies
Authors: Yulin Huang
Source: Preprints.org (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

A Self-Reflective Multi-Agent Collaboration Framework for Dynamic Software Engineering Tasks Large Language Model (LLM) based multi-agent systems have demonstrated remarkable potential in automating complex software engineering tasks. However, existing frameworks such as MetaGPT and AutoGen suffer from critical limitations including static role assignments, cascading hallucinations in long-horizon tasks, and the absence of experience accumulation mechanisms. We propose Eco-Evolve, a self-reflective multi-agent collaboration framework that addresses these challenges through three key innovations: (1) a dynamic topology generation mechanism that adaptively constructs agent communication graphs based on task complexity, (2) a system reflection module featuring a dedicated Critic Agent for deliberate verification at critical checkpoints, and (3) an error-driven self-evolution mechanism inspired by Hindsight Experience Replay (HER) that enables prompt optimization through experiential learning. We evaluate Eco-Evolve on SWE-bench Verified and DevBench, achieving62.3% and 73.5% respectively, representing improvements of26.6% and 14.7% over the strongest baseline. Comprehensive ablation studies validate the contribution of each component, demonstrating that integrating dynamic collaboration, deliberate reflection, and continuous evolution significantly advances the state of the art in automated software engineering. Hindsight bias ; Task (project management) ; Key (lock) ; Software ; Mechanism (biology) ; Reflection (computer programming) ; Software system ; State (computer science) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Yuhang Liu | Heyan Huang | Yizhe Yang | Hongyan Zhao | Zhizhuo Zeng | Yang Gao
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear. Mathematical modeling competitions provide a stringent testbed for evaluating such end-to-end problem-solving capability. We propose a problem-oriented, stage-wise evaluation framework that assesses LLM performance across modeling stages using expert-verified criteria. We validate the framework's reliability by comparing automatic scores with independent human expert judgments on problems from the China Postgraduate Mathematical Contest in Modeling, demonstrating substantially stronger alignment than existing evaluation schemes. Using this framework, we reveal a comprehension-execution gap in state-of-the-art LLMs: while they perform well in early stages such as problem identification and formulation, they exhibit persistent deficiencies in execution-oriented stages including model solving, code implementation, and result analysis. These gaps persist even with increased model scale. We further trace these failures to insufficient specification, missing verification, and lack of validation, with errors propagating across stages without correction. Our findings suggest that bridging this gap requires approaches beyond model scaling, offering insights for applying LLMs to complex real-world problem solving. CONTEST ; Bridging (networking) ; Workflow ; Identification (biology) ; Testbed ; Reliability (semiconductor) Machine Learning in Materials Science ; Model-Driven Software Engineering Techniques ; Topic Modeling
Authors: Shuyin Ouyang | Dong HUANG | Jingwen Guo | Zeyu Sun | Qihao Zhu | Jie M. Zhang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

DSCodeBench: A Realistic Benchmark for Data Science Code Generation We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of 1,000 carefully constructed problems sourced from realistic problems from GitHub across ten widely used Python data science libraries. DSCodeBench offers a more challenging and representative testbed, more complex code solutions, more comprehensive data science libraries, clearer and better structured problem descriptions, and stronger test suites. To construct the DSCodeBench, we develop a robust pipeline that combines task scope selection, code construction, test case generation, and problem description synthesis. The process is paired with rigorous manual editing to ensure alignment and enhance the reliability of the evaluation. Experimental result shows that DSCodeBench exhibits robust scaling behavior, where larger models systematically outperform smaller ones, validating its ability to distinguish model capabilities. The best LLM we test, GPT-4o, has a pass@1 of 0.392, indicating that LLMs still have a large room to improve for realistic data science code generation tasks. We believe DSCodeBench will serve as a rigorous and trustworthy foundation for advancing LLM-based data science programming. Python (programming language) ; Benchmark (surveying) ; Code (set theory) ; Construct (python library) ; Process (computing) ; Pipeline (software) ; Code generation ; Debugging ; Unit testing Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Physics and Python Applications
Authors: Milad Masrouri | Zhao Qin
Source: MRS Communications (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Generative–predictive AI framework for joint design and mechanical prediction of bicontinuous composite microstructures Abstract Designing high-performance composites requires exploring vast microstructural design spaces, which is computationally expensive for bicontinuous architecture using traditional simulations. We present an end-to-end artificial intelligence framework combining a denoising diffusion probabilistic model (DDPM) and a multimodal surrogate predictor to jointly generate and evaluate composite microstructures. Trained on 2000 phase-field-generated binary composites (0.55:0.45 stiff-to-soft ratio) with simulated stress fields, the DDPM co-generates realistic configurations and von Mises responses. From each stress map, 28 features and image embeddings feed a multimodal neural network that predicts Young’s modulus and ultimate tensile strength ( R 2 = 0.95 and 0.75). This generative–predictive pipeline accelerates composite design and inverse discovery. Graphical abstract Composite number ; Ultimate tensile strength ; Joint (building) ; Inverse ; Pipeline (software) ; Modulus ; Artificial neural network ; Probabilistic logic ; Microstructure Machine Learning in Materials Science ; Composite Material Mechanics ; Epoxy Resin Curing Processes
Authors: Yi Huang | Bowen Zheng | Yunxi Dong | Hong Tang | Huan Zhao | S. M. Rakibul Hasan Shawon | Hualiang Zhang
Source: ArXiv.org (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Self-Evolving Agentic Framework for Metasurface Inverse Design Metasurface inverse design has become central to realizing complex optical functionality, yet translating target responses into executable, solver-compatible workflows still demands specialized expertise in computational electromagnetics and solver-specific software engineering. Recent large language models (LLMs) offer a complementary route to reducing this workflow-construction burden, but existing language-driven systems remain largely session-bounded and do not preserve reusable workflow knowledge across inverse-design tasks. We present an agentic framework for metasurface inverse design that addresses this limitation through context-level skill evolution. The framework couples a coding agent, evolving skill artifacts, and a deterministic evaluator grounded in physical simulation so that solver-specific strategies can be iteratively refined across tasks without modifying model weights or the underlying physics solver. We evaluate the framework on a benchmark spanning multiple metasurface inverse-design task types, with separate training-aligned and held-out task families. Evolved skills raise in-distribution task success from 38% to 74%, increase criteria pass fraction from 0.510 to 0.870, and reduce average attempts from 4.10 to 2.30. On held-out task families, binary success changes only marginally, but improvements in best margin together with shifts in error composition and agent behavior indicate partial transfer of workflow knowledge. These results suggest that the main value of skill evolution lies in accumulating reusable solver-specific expertise around reliable computational engines, thereby offering a practical path toward more autonomous and accessible metasurface inverse-design workflows. Workflow ; Benchmark (surveying) ; Task (project management) ; Coding (social sciences) ; Software ; Inverse ; Margin (machine learning) ; Component (thermodynamics) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Orbital Angular Momentum in Optics
Authors: Zonglin Yang | Runze Mao | Tianhao Wu | Han Li | Qingguo Zhou | Zhi X. Chen
Source: arXiv (Cornell University) (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

A unified foundational framework for knowledge injection and evaluation of Large Language Models in Combustion Science To advance foundation Large Language Models (LLMs) for combustion science, this study presents the first end-to-end framework for developing domain-specialized models for the combustion community. The framework comprises an AI-ready multimodal knowledge base at the 3.5 billion-token scale, extracted from over 200,000 peer-reviewed articles, 8,000 theses and dissertations, and approximately 400,000 lines of combustion CFD code; a rigorous and largely automated evaluation benchmark (CombustionQA, 436 questions across eight subfields); and a three-stage knowledge-injection pathway that progresses from lightweight retrieval-augmented generation (RAG) to knowledge-graph-enhanced retrieval and continued pretraining. We first quantitatively validate Stage 1 (naive RAG) and find a hard ceiling: standard RAG accuracy peaks at 60%, far surpassing zero-shot performance (23%) yet well below the theoretical upper bound (87%). We further demonstrate that this stage's performance is severely constrained by context contamination. Consequently, building a domain foundation model requires structured knowledge graphs and continued pretraining (Stages 2 and 3). Context (archaeology) ; Benchmark (surveying) ; Knowledge base ; Domain (mathematical analysis) ; Combustion ; Foundation (evidence) ; Domain knowledge Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Combustion Engine Technologies