jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mirza Samad Ahmed Baig | Syeda Anshrah Gillani
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Symmetry-Constrained Language-Guided Program Synthesis for Discovering Governing Equations from Noisy and Partial Observations Discovering compact governing equations from experimental observations is one of the defining objectives of quantitative science, yet practical discovery pipelines routinely fail when measurements are noisy, relevant state variables are unobserved, or multiple symbolic structures explain the data equally well within statistical uncertainty. Here we introduce SymLang (Symmetry-constrained Language-guided equation discovery), a unified framework that brings together three previously separate ideas: (i) typed symmetry-constrained grammars that encode dimensional analysis, group-theoretic invariance, and parity constraints as hard production rules, eliminating on average 71.3% of candidate expression trees before any fitting; (ii) language-model-guided program synthesis in which a fine-tuned 7B-parameter proposer, conditioned on interpretable data descriptors, efficiently navigates the constrained search space; and (iii) MDL-regularized Bayesian model selection coupled with block-bootstrap stability analysis that quantifies structural uncertainty rather than committing to a single best equation. Across 133 dynamical systems spanning classical mechanics, electrodynamics, thermodynamics, population dynamics, and nonlinear oscillators, SymLang achieves an exact structural recovery rate of 83.7% under 10% observational noise - a 22.4 percentage-point improvement over the next-best baseline - while reducing out-of-distribution extrapolation error by 61% and near-eliminating conservation-law violations (3.1 x 10-3 vs. 187.3 x 10-3 physical drift for the closest competitor). In all tested regimes the framework correctly identifies structural degeneracy, reporting it explicitly rather than returning a confidently wrong single equation. The framework is fully open-source and reproducible, providing a principled pathway from raw data to interpretable, physically auditable symbolic laws. ENCODE ; Extrapolation ; Bayesian probability ; Stability (learning theory) ; Nonlinear system ; Population ; Probabilistic logic ; Noise (video) ; Ambiguity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Scientific Computing and Data Management
Authors: Kaiyu Wang | Daehyun Daniel Ahn | Nakul Rampal | Jackson Thomassian | Neda S. Sabeva | O.M. Kannan | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: Journal of the American Chemical Society (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Synthesis of Highly Crystalline Covalent Organic Frameworks Using Large Language Models Crystallizing covalent organic frameworks (COFs) remain a central challenge in reticular chemistry, as achieving long-range order typically requires extensive trial-and-error optimization over many months or years. Here, we demonstrate that by integrating a deep research agent within ChatGPT, this process can be markedly accelerated, reducing the crystallization timeline to less than one month. Our approach, termed the LLM For Accelerated Synthesis Technique (LFAST), operates through two interlinked cycles. In the first, we formulated a structured, multistep prompt to guide the deep research agent in mining, correlating, and validating synthesis parameters from the relevant chemical literature. This yielded an expanded and refined design space for reaction condition screening. In the second, these conditions were executed by using an automated synthesis platform coupled with high-throughput powder X-ray diffraction analysis. Using a widely reported β-ketoenamine-linked COF, TpPa-SO<sub>3</sub>H, as a benchmark, LFAST produced frameworks with diffraction peaks corresponding to a 350% increase in crystallinity index (CI) relative to prior reports. The same protocol enabled the synthesis of an unreported β-ketoenamine-linked COF-2000 with an even higher structural order. To ensure reproducibility and data accessibility, we further introduce a standardized metadata format encompassing synthesis and PXRD data sets. This data-driven methodology transforms the way that COFs are crystallized and significantly accelerates the pace of materials discovery. Crystallinity ; Crystallization ; Covalent bond ; Powder diffraction ; Organic synthesis ; Amorphous solid ; Protocol (science) Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Feifei Luo | Deyuan Li | Zhenyi Lin | Qilong Wang | Chunpeng Yang
Source: ACS Energy Letters (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

AutoSEE: An Artificial Intelligence Agent for Automated Solid Electrolyte Exploration To address the challenge of complex chemical spaces in solid-state battery research, we develop a research agent for automated solid electrolyte exploration, AutoSEE, which enables efficient end-to-end research, autonomously revealing design principles for high-performance solid electrolytes. By leveraging GPT-4o as a central controller, AutoSEE orchestrates specialized tools such as literature screening, text mining, and machine learning to construct specialized data sets and distill principles. Taking halide solid electrolytes as an example, AutoSEE processes 226 relevant articles with an average precision of 93.4% within 5 min and employs unsupervised clustering to autonomously distill a critical design principle that halide solid electrolytes featuring high charge density favor an amorphous phase and high ionic conductivity. Guided by this principle, we found an unexplored electrolyte LiTaB0.32O0.98Cl5, which possesses a high ionic conductivity (7.82 mS cm–1), experimentally validating the agent-distilled rule. AutoSEE showcases a paradigm shift from simple data retrieval to agentic knowledge exploration. Electrolyte ; Cluster analysis ; Fast ion conductor ; Construct (python library) ; Ionic conductivity ; Halide ; Battery (electricity) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Yuhang Zhou | Kai Zheng | Qiguang Chen | Mengkang Hu | Qingfeng Sun | Can Xu | Jingjing Chen
Source: Open MIND (preprint, 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: Shuoling Liu | Zhiquan Tan | Kun Yi | Hui Wu | Yihan Li | Jiangpeng Yan | Liyuan Chen | Kai Chen | Qiang Yang
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution. To bridge this gap, we formalize DRA behavior through the lens of category theory, modeling deep research workflow as a composition of structure-preserving maps (functors). Grounded in this theoretical framework, we introduce a novel mechanism-aware benchmark with 296 questions designed to stress-test agents along four interpretable axes: traversing sequential connectivity chains, verifying intersections within V-structure pullbacks, imposing topological ordering on retrieved substructures, and performing ontological falsification via the Yoneda Probe. Our rigorous evaluation of 11 leading models establishes a persistently low baseline, with the state-of-the-art achieving only a 19.9\% average accuracy, exposing the difficulty of formal structural stress-testing. Furthermore, our findings reveal a stark dichotomy in the current AI capabilities. While advanced deep research pipelines successfully redefine dynamic topological re-ordering and exhibit robust ontological verification -- matching pure reasoning models in falsifying hallucinated premises -- they almost universally collapse on multi-hop structural synthesis. Crucially, massive performance variance across tasks exposes a lingering reliance on brittle heuristics rather than a systemic understanding. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formidable open challenge.\footnote{Our implementation will be available at https://github.com/tzq1999/CDR. Heuristics ; Ambiguity ; Heuristic ; Workflow ; Matching (statistics) ; Categorical variable ; Liveness ; Benchmark (surveying) ; Bridge (graph theory) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Multimodal Machine Learning Applications
Authors: Shuoling Liu | Zhiquan Tan | Kun Yi | Hui Wu | Yihan Li | Jiangpeng Yan | Liyuan Chen | Kai Chen | Qiang Yang
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution. To bridge this gap, we formalize DRA behavior through the lens of category theory, modeling deep research workflow as a composition of structure-preserving maps (functors). Grounded in this theoretical framework, we introduce a novel mechanism-aware benchmark with 296 questions designed to stress-test agents along four interpretable axes: traversing sequential connectivity chains, verifying intersections within V-structure pullbacks, imposing topological ordering on retrieved substructures, and performing ontological falsification via the Yoneda Probe. Our rigorous evaluation of 11 leading models establishes a persistently low baseline, with the state-of-the-art achieving only a 19.9\% average accuracy, exposing the difficulty of formal structural stress-testing. Furthermore, our findings reveal a stark dichotomy in the current AI capabilities. While advanced deep research pipelines successfully redefine dynamic topological re-ordering and exhibit robust ontological verification -- matching pure reasoning models in falsifying hallucinated premises -- they almost universally collapse on multi-hop structural synthesis. Crucially, massive performance variance across tasks exposes a lingering reliance on brittle heuristics rather than a systemic understanding. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formidable open challenge.\footnote{Our implementation will be available at https://github.com/tzq1999/CDR. Heuristics ; Ambiguity ; Heuristic ; Workflow ; Matching (statistics) ; Categorical variable ; Liveness ; Benchmark (surveying) ; Bridge (graph theory) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Multimodal Machine Learning Applications
Authors: Debjit Paul | Daniel Murphy | Milan Gritta | Ronald Cardenas | Victor Prokhorov | Lena Sophia Bolliger | Aysim Toker | Roy Miles | Andreea-Maria Oncescu | J. Sivakumar | Philipp Borchert | Ismail Elezi | Meiru Zhang | Ka Yiu Lee | Guchun Zhang | Jun Wang | Gerasimos Lampouras
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

A Benchmark for Deep Information Synthesis Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis. However, current evaluation benchmarks do not adequately assess their ability to solve real-world tasks that require synthesizing information from multiple sources and inferring insights beyond simple fact retrieval. To address this, we introduce DEEPSYNTH, a novel benchmark designed to evaluate agents on realistic, time-consuming problems that combine information gathering, synthesis, and structured reasoning to produce insights. DEEPSYNTH contains 120 tasks collected across 7 domains and data sources covering 67 countries. DEEPSYNTH is constructed using a multi-stage data collection pipeline that requires annotators to collect official data sources, create hypotheses, perform manual analysis, and design tasks with verifiable answers. When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a maximum F1 score of 8.97 and 17.5 on the LLM-judge metric, underscoring the difficulty of the benchmark. Our analysis reveals that current agents struggle with hallucinations and reasoning over large information spaces, highlighting DEEPSYNTH as a crucial benchmark for guiding future research. Benchmark (surveying) ; Pipeline (software) ; Code (set theory) ; Source code ; Simple (philosophy) ; Verifiable secret sharing Machine Learning in Materials Science ; Topic Modeling ; Artificial Intelligence in Healthcare and Education
Authors: Yongchul G. Chung | Myoung Soo Lah
Source: arXiv (Cornell University) (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Hunting Structural Demons in Digital Reticular Chemistry Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise when disordered or incomplete structural models are incorrectly converted into fully specified simulation inputs. In hypothetical MOF database, structures are complete by construction but may encode chemically implausible oxidation states, coordination environments, or charge distributions. We term these erroneous structural models "structural demons." This mini-review asks three questions: where these errors enter, how we find them, and how we prevent them. On the prevention side, the key steps are keeping diffraction data and synthesis details together from the start, using consistent curation when structures enter a database, and filtering topology choices before structure generation. Connecting these steps can keep many bad structures out of downstream databases and reduce the need to fix them later. ENCODE ; Phaser ; Term (time) ; Reticular connective tissue ; Topology (electrical circuits) ; Key (lock) ; Crystal structure ; Computational model Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: David van Dijk | Ivan Vrkic
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

SciDesignBench: Benchmarking and Improving Language Models for Scientific Inverse Design Many of the most important problems in science and engineering are inverse problems: given a desired outcome, find a design that achieves it. Evaluating whether a candidate meets the spec is often routine; a binding energy can be computed, a reactor yield simulated, a pharmacokinetic profile predicted. But searching a combinatorial design space for inputs that satisfy those targets is fundamentally harder. We introduce SciDesignBench, a benchmark of 520 simulator-grounded tasks across 14 scientific domains and five settings spanning single-shot design, short-horizon feedback, long-horizon refinement, and seed-design optimization. On the 10-domain shared-core subset, the best zero-shot model reaches only 29.0% success despite substantially higher parse rates. Simulator feedback helps, but the leaderboard changes with horizon: Sonnet 4.5 is strongest in one-turn de novo design, whereas Opus 4.6 is strongest after 20 turns of simulator-grounded refinement. Providing a starting seed design reshuffles the leaderboard again, demonstrating that constrained modification requires a fundamentally different capability from unconstrained de novo generation. We then introduce RLSF, a simulator-feedback training recipe. An RLSF-tuned 8B model raises single-turn success rates by 8-17 percentage points across three domains. Together, these results position simulator-grounded inverse design as both a benchmark for scientific reasoning and a practical substrate for amortizing expensive test-time compute into model weights. Benchmark (surveying) ; Benchmarking ; Inverse ; Amortizing loan ; Language model ; Parsing ; Bottleneck Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Jiayi Zhu | Longhan Zhang | Yanxi Zhu | Xiaotian Lin | Yifan Wu | Shimin Di | Bang Liu | Yuyu Luo | Tongyi Zhang
Source:  (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

A survey of agentic materials science and engineering: Where are we and where are we going? Agents mostly built upon large language models (LLMs) with planning, tool-use, memory, and self-reﬂection capabilities are revolutionizing all aspects of materials science and engineering (MSE), from materials design, experiment execution, industrial manufacture, to deployment, thereby opening the age of agentic MSE. Rather than isolated AI predictive models, these agents coordinate multi-step scientiﬁc workﬂows by retrieving and structuring knowledge, proposing and reﬁning hypotheses, planning experiments, combining multi-modal simulations and characterizations, and, when integrated with AI materials laboratories, closing the loop toward autonomous discovery of materials. However, agentic systems exhibit varying degrees of autonomy, and their roles in materials research and development diﬀer accordingly. To systematically examine the landscape of agentic MSE, this survey proposes a six-level autonomy framework (Levels 0–5) that characterizes the progression from human-only workﬂows to fully autonomous scientiﬁc agents. The framework aligns with key task families across all steps in MSE, including information retrieval, property prediction, simulation, synthesis, and characterization. By reviewing recent advances in agentic MSE, we reveal uneven progress. Knowledge-centric capabilities often remain early-stage, while experimental orchestration and characterization are starting to explore higher-level agent behaviors. Importantly, mature autonomy requires coordinating multiple tasks rather than optimizing a single one. Collectively, these insights provide a structured roadmap for advancing agentic MSE toward higher autonomy. Autonomy ; Orchestration ; Task (project management) ; Property (philosophy) ; Structuring ; Key (lock) ; Agency (philosophy) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Lee Bin Choi | Ohyeon Lee | Sanghun Lee
Source: Journal of Chemical Information and Modeling (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Agentic Knowledge Graphs of the LiFePO <sub>4</sub> Cathode for Lithium Ion Battery: Balancing Discovery and Stability with LLMs Lithium iron phosphate (LiFePO<sub>4</sub>, LFP) has regained prominence as a cathode for lithium ion batteries thanks to its intrinsic safety, thermal stability, long cycle life, and cost advantages. We present an agentic knowledge-graph pipeline that converts titles/abstracts into directed, signed agent → property relations. Using a Scopus corpus of the 9500 most-cited LFP journal articles (2000-present), we benchmark three matched modes: A, rules with a closed vocabulary; B, LLM-only with an open vocabulary; and mixed LLM with a hybrid vocabulary. A yields a compact, high-precision core; B expands recall but increases label dispersion; C preserves much of B's breadth while maintaining schema alignment via canonicalization and role gating. Robustness tests with eight bootstrap passes show rapid convergence: requiring recurrence across ∼6 passes plus a modest publication-support threshold yields a compact, high-confidence backbone. The resulting network is predominantly positive and centers on transport and interfacial outcomes, with a small number of mixed and negative ties indicating condition dependence. Beyond LFP, the workflow can be adapted to other battery chemistries with modest retuning of vocabularies and projection rules alongside routine validation on held-out annotations, enabling a stability-aware, literature-scale synthesis of direction-of-effect relations. Cathode ; Robustness (evolution) ; Ion ; Lithium iron phosphate ; Workflow ; Binary number ; Thermal stability ; Lithium-ion battery Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Xiangsen Chen | Ruilong Wu | Yanyan Lan | Ting Ma | Yang Liu
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization Despite deep learning's success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigger drastic property shifts. Current representation learning, bound by the similarity principle, often fails to capture these structural-activity discontinuities. To address this, we introduce MolEvolve, an evolutionary framework that reformulates molecular discovery as an autonomous, look-ahead planning problem. Unlike traditional methods that depend on human-engineered features or rigid prior knowledge, MolEvolve leverages a Large Language Model (LLM) to actively explore and evolve a library of executable chemical symbolic operations. By utilizing the LLM to cold start and an Monte Carlo Tree Search (MCTS) engine for test-time planning with external tools (e.g. RDKit), the system self-discovers optimal trajectories autonomously. This process evolves transparent reasoning chains that translate complex structural transformations into actionable, human-readable chemical insights. Experimental results demonstrate that MolEvolve's autonomous search not only evolves transparent, human-readable chemical insights, but also outperforms baselines in both property prediction and molecule optimization tasks. Interpretability ; Executable ; Property (philosophy) ; Representation (politics) ; Monte Carlo tree search ; Process (computing) ; Evolutionary algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks