jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yiwen Song | Yale Song | Tomas Pfister | Jinsung Yoon
Source: arXiv (Cornell University) (preprint, 2026-04-06)
Field: Materials Science — Materials Chemistry

PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality. Suite ; Benchmark (surveying) ; Margin (machine learning) ; Automation ; Scientific literature Machine Learning in Materials Science ; Scientific Computing and Data Management ; Multimodal Machine Learning Applications
Authors: Yiwen Song | Yale Song | Tomas Pfister | Jinsung Yoon
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality. Suite ; Benchmark (surveying) ; Margin (machine learning) ; Automation ; Scientific literature Machine Learning in Materials Science ; Scientific Computing and Data Management ; Multimodal Machine Learning Applications
Authors: Jaewoong Lee | Junhee Woo | Younghun Kim | Sejin Kim | Cinthya Paulina | Hyunmin Park | Hee-Tak Kim | Steve Park | Jihan Kim
Source: ACS Central Science (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

LLMB: AI Agent for Lithium Metal Battery Research Using Large Language Model Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce LLMB, an AI agent for lithium metal battery research that integrates a large language model (LLM) for hierarchical text mining with an automatic graph mining tool, Material Graph Digitizer (MatGD). This agent enables state-of-the-art accurate extraction of battery material data and cyclability performance metrics from diverse sources. Through text mining, we extracted composition and operating condition information for 15,398 battery cells, and graph mining yielded cyclability data for 10,242 cells. By aligning and merging these, we constructed a comprehensive database of 8,074 cells, containing component specifics and capacity. Utilizing the comprehensive database constructed through the LLMB agent, we developed the first machine learning model to predict capacities of LMBs using material information from battery components. Furthermore, molecular simulations and material analyses were performed to elucidate how the identified predictive features influence the physicochemical properties governing the battery performance. Based on these models and material analysis, we experimentally validated that the weakly solvating electrolyte, induced from low EState VSA6 solvents, facilitates the formation of an anion-derived solid-electrolyte interphase (SEI) and promotes highly crystallized Li plating, thereby confirming the reliability of our models. Battery (electricity) ; Lithium (medication) ; Lithium metal ; Lithium battery ; Key (lock) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Software System Performance and Reliability
Authors: Yingjun Chen | Xinheng Guo | Weiwei Xue
Source: Drug Discovery Today (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Large language models for molecular design: bridging the gap between chemical syntax and biological semantics Bridging (networking) ; Syntax ; Semantics (computer science) ; Generative grammar ; Identification (biology) ; Abstract syntax ; Computational model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Yuanming Li | Jie Zhao | Xiangkun Elvis Cao | Shuangjun Li
Source: Separation and Purification Technology (article, 2026-03-04)
Field: Chemistry — Inorganic Chemistry

A large language model-based multi-agent methodology for intelligent materials screening: A case study on MOFs for CO2 capture Metal-organic frameworks (MOFs) hold significant potential for CO 2 capture owing to their tunable structures, large surface areas, and versatile chemistries. However, current screening strategies are often limited to specific application scenarios and overlook the intrinsic properties of materials, thereby limiting the transferability of findings. Here, an intelligent evaluation framework was proposed that leverages large language models (LLMs) to integrate semantic reasoning with numerical performance metrics. The approach combines molecular structure, elemental composition, and synthetic feasibility with molecular simulations and process modeling to optimize the design. Adsorption behavior is obtained from atomistic simulations, while cyclic performance is assessed through equilibrium-based process simulations. Notably, energy utilization efficiency is explicitly incorporated as a central performance indicator and assigned higher weighting, thereby emphasizing practical deployment considerations. These numerical indicators are further combined with semantic scores derived from an LLM-based multi-agent system, enabling a balanced, interpretable ranking of candidate MOFs. This dual-level strategy reconciles rigorous optimization with engineering feasibility, safety, and compliance. By aligning numerical robustness with semantic interpretability, the methodology addresses the scenario-dependence of conventional screening methods and provides a scalable pathway for intelligent material evaluation. Beyond CO 2 capture, the framework is readily extensible to diverse adsorption processes and evaluation metrics, highlighting its potential as a next-generation paradigm for material screening and decision support. • Introduces an LLM-based multi-agent framework for MOF screening in CO 2 capture. • Integrates molecular simulations with process modeling for robust evaluation. • Emphasizes energy efficiency as a core metric in material screening. • Dual-level ranking balances numerical rigor with semantic interpretability. • Provides a scalable and extensible pathway for intelligent materials discovery. Robustness (evolution) ; Scalability ; Ranking (information retrieval) ; Metric (unit) ; Process (computing) ; Extensibility ; Limiting ; Efficient energy use Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Zhenyuan Zhao | Yu Xing | Tianyang Xue | Lingxin Cao | Xin Yan | Lin Lü
Source: arXiv (Cornell University) (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This inverse design problem involves a vast, discontinuous search space where traditional topology optimization is computationally prohibitive, and deep generative models often suffer from "physical hallucinations," lacking the capability to ensure rigorous validity. To address this limitation, we introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evolutionary search. Unlike methods that treat LLMs merely as interfaces, AutoMS integrates them as "semantic navigators" to initialize search spaces and break local optima, while our novel Simulation-Aware Evolutionary Search (SAES) addresses the "blindness" of traditional evolutionary strategies. Specifically, SAES utilizes simulation feedback to perform local gradient approximation and directed parameter updates, effectively guiding the search toward physically valid Pareto frontiers. Orchestrating specialized agents (Manager, Parser, Generator, and Simulator), AutoMS achieves a state-of-the-art 83.8\% success rate on 17 diverse cross-physics tasks, nearly doubling the performance of traditional NSGA-II (43.7\%) and significantly outperforming ReAct-based LLM baselines (53.3\%). Furthermore, our hierarchical architecture reduces total execution time by 23.3\%. AutoMS demonstrates that autonomous agent systems can effectively navigate complex physical landscapes, bridging the gap between semantic design intent and rigorous physical validity. Evolutionary algorithm ; Bridging (networking) ; Multi-objective optimization ; Pareto principle ; Inverse ; Evolutionary computation ; Generative grammar ; Local search (optimization) Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Multi-Objective Optimization Algorithms
Authors: Subham Ghosh | Abhishek Tewari
Source: Computational Materials Science (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

LLM-based AI agents for automated extraction of material properties and structural features Workflow ; Set (abstract data type) ; Consistency (knowledge bases) ; Verifiable secret sharing ; Thermoelectric materials ; Orchestration ; Data set ; Analyser Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Topic Modeling
Authors: Yutian Zhuang | Xiaojin Yang | Chenyi Zhang | Xue Jia | Di Zhang | Mingzhe Li | Tongao Yao | Jiayu Peng | Zhengyang Gao | Weijie Yang | H. C. Li
Source: Precision Chemistry (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Materials Databases: Foundations of Modern Digital Materials Materials databases are increasingly the backbone of data-driven discovery for energy materials. In this Perspective, we map the ecosystem of computational and experimental databases, and argue that database architecture, which covers ingestion, curation, metadata, provenance, and access interfaces, strongly influences the performance and trustworthiness of modern AI models. We classify computational repositories into bulk-property and surface/interface resources, and summarize representative experimental databases spanning crystal structures, catalysis, energy storage, and characterization. Beyond single-modality repositories, we highlight integrated platforms that connect computed descriptors with context-rich experimental evidence and tool interfaces, enabling iterative hypothesis testing and closed-loop validation. Building on these examples, we propose a database–model–experiment roadmap for training and deploying graph neural networks, machine learning interatomic potentials, and large language model-based AI Agents. Finally, we outline key bottlenecks that must be addressed for reliable autonomous discovery, including FAIR (Findable, Accessible, Interoperable, Reusable)-aligned standardization, bias and missing negative results, and cross-code reproducibility. Trustworthiness ; Key (lock) ; Graph ; Energy (signal processing) ; Artificial neural network ; Deep learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Iman Peivaste | Nicolas D. Boscher | Ahmed Makradi | Salim Belouettar
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability--activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here we introduce Ara, a large-language-model (LLM) agent that leverages pretrained chemical knowledge, donor--acceptor theory, conjugation effects, and linkage stability hierarchies, to guide the search for photocatalytic COFs satisfying joint band-gap, band-edge, and hydrolytic-stability criteria. Evaluated against random search and Bayesian optimization (BO) over a space consisting of candidates with various nodes, linkers, linkages, and r-groups, screened with a GFN1-xTB fragment pipeline, Ara achieves a 52.7\% hit rate (11.5$\times$ random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO (p = 0.006). Inspection of the agent's reasoning traces reveals interpretable chemical logic: early convergence on vinylene and beta-ketoenamine linkages for stability, node selection informed by electron-withdrawing character, and systematic R-group optimization to center the band gap at 2.0 eV. Exhaustive evaluation of the full search space uncovers a complementary exploitation--exploration trade-off between the agent and BO, suggesting that hybrid strategies may combine the strengths of both approaches. These results demonstrate that LLM chemical priors can substantially accelerate multi-criteria materials discovery. Workflow ; Bayesian optimization ; Chemical space ; Inverse ; Node (physics) ; Space (punctuation) ; Convergence (economics) ; Hydrolysis Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Zhen Song | Yishuai Cai | Yadong Yu | Pan Xiang | Maggie Li | Zhe Liu | Mengyan Dai
Source: Materials Genome Engineering Advances (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models ABSTRACT The rapid advancement of large model technology in recent years has ushered in new opportunities for materials science. Leveraging their powerful feature learning capabilities, emergent properties, and flexible fine‐tuning mechanisms, large models are gradually being applied to all aspects of materials science research. However, we contend that the deep integration of artificial intelligence and materials science urgently requires a transition from “virtual intelligence assistance” to “embodied intelligence dominance”. Future materials discovery will be autonomously executed in closed‐loop operations within physical environments by Embodied AI Chemists. This review first introduces the blossom of large model technology and its multifaceted applications in various materials science‐related topics. Subsequently, this review specifically maps the developmental pathways of autonomous experimental platforms, underscoring the comparative advantages of large‐model‐based systems over traditional machine learning approaches. Furthermore, it discusses the potential of embodied large models in materials science and proposes potential applications by constructing diverse training datasets, enhancing embodied reasoning capabilities, establishing intelligent collaborative environments, and developing multi‐agent collaborative frameworks. Through this comprehensive analysis, we aim to pioneer new intelligent pathways for materials science research, motivating an end‐to‐end intelligent transformation from theoretical exploration to experimental realization. Embodied cognition ; Applications of artificial intelligence ; Intelligent decision support system ; Deep learning ; Feature (linguistics) ; Ambient intelligence Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Block Copolymer Self-Assembly
Authors: Zhengding Hu | Kuntal Talit | Zhen Wang | Haseeb Ahmad | Yichen Lin | Prabhleen Kaur | Christopher Lane | Elizabeth A. Peterson | Zhiting Hu | Elizabeth Nowadnick | Yufei Ding
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

TritonDFT: Automating DFT with a Multi-Agent Framework Density Functional Theory (DFT) is a cornerstone of materials science, yet executing DFT in practice requires coordinating a complex, multi-step workflow. Existing tools and LLM-based solutions automate parts of the steps, but lack support for full workflow automation, diverse task adaptation, and accuracy-cost trade-off optimization in DFT configuration. To this end, we present TritonDFT, a multi-agent framework that enables efficient and accurate DFT execution through an expert-curated, extensible workflow design, Pareto-aware parameter inference, and multi-source knowledge augmentation. We further introduce DFTBench, a benchmark for evaluating the agent's multi-dimensional capabilities, spanning science expertise, trade0off optimization, HPC knowledge, and cost efficiency. TritonDFT provides an open user interface for real-world usage. Our website is at https://www.tritondft.com. Our source code and benchmark suite are available at https://github.com/Leo9660/TritonDFT.git. Workflow ; Suite ; Benchmark (surveying) ; Task (project management) ; Code (set theory) ; Interface (matter) ; Extensibility Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Harshitha Menon | Charles F. Jekel | Kevin Korner | Brian Gunnarson | Nathan Brown | Michael Stees | M. Giselle Fernandez-Godino | Walter Nissen | Meir H. Shachar | Dane M. Sterbentz | William J. Schill | Yue Hao | Robert N. Rieben | William Roshan Quadros | Steve Owen | Scott Mitchell | Ismael D. Boureima | Jonathan L. Belof
Source: arXiv (Cornell University) (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems Today's scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-impact scientific discovery, we need to scale up our ability to test hypotheses, generate results, and learn from them rapidly. We present MADA (Multi-Agent Design Assistant), a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, a Geometry Agent (GA) generates meshes, and an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. While general purpose, we focus development and validation on Richtmyer--Meshkov Instability (RMI) suppression, a critical challenge in Inertial Confinement Fusion. We evaluate on two complementary settings: running a hydrodynamics simulations on HPC systems, and using a pre-trained machine learning surrogate for rapid design exploration. Our results demonstrate that the MADA system successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. More broadly, it demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery. Workflow ; Iterative design ; Focus (optics) ; Design tool ; Systems design ; Scale (ratio) ; Coupling (piping) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Modular Robots and Swarm Intelligence
Authors: Yewei Xiao | Xi Zeng | Zhaoxiang Yang | Junwei Gu | Y Wang | Huilin Wen | Mingdong Chen | Yanhong Lu | Zhipeng Huang | Junwei Hu | Jiaojiao Liu | Sha Chen | Jinhui Xie | Hui Li | Xiang Zhu | Shenchi Zheng | Weijie Zong | Ying Xu | Fei Li | Ziyi Yu
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Multi-agent reasoning enables predictive design of living materials Abstract Living materials derive function from tightly coupled cellular and material processes to deliver adaptive and therapeutic capabilities, yet their predictive design remains constrained by fragmented, cross-disciplinary knowledge and experience-driven iteration. Here we introduce LiveMat, a multi-agent reasoning framework that reconstructs living materials as a computable design space from unstructured literature. LiveMat curates and standardizes 34,738 living-material records, integrating 16,086 microorganism entries and 18,682 polymer entries into a domain-scale knowledge graph comprising tens of thousands of entities and relationships. Through constraint-driven multi-agent reasoning and expert-anchored evaluation, the system converts implicit design heuristics into explicit, auditable rules. Comparative benchmarking across leading large language models shows that limitations in living materials reasoning arise primarily from cross-domain feature integration rather than coarse-grained classification. In a prospective acute wound-healing task, LiveMat evaluates combinatorial four-component systems across six functional dimensions and identifies a top-ranked design whose in vivo performance matches state-of-the-art systems. LiveMat establishes a scalable reasoning infrastructure for cumulative, data-grounded living materials discovery. Heuristics ; Benchmarking ; Scalability ; Living systems ; Function (biology) ; Space (punctuation) ; Automated reasoning ; Qualitative reasoning Machine Learning in Materials Science ; Design Education and Practice ; Plant and Biological Electrophysiology Studies