jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan A Meissner | Philipp Kuboth | Jan Meisner
Source: ChemRxiv (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Evolving transition-state search with agentic large language models Accurate identification of transition states (TSs) is fundamental to computational chemistry. Modern reaction-discovery efforts increasingly rely on refining large reaction datasets, where even a small fraction of TS-search failures can leave key pathways unresolved and bias the resulting reaction network. Here, we introduce an adaptive evolutionary framework for TS search workflows that prioritizes completion of a target dataset over global algorithmic robustness. The framework iteratively focuses on reactions for which no validated TS has been found and dynamically modifies the workflow to search the remaining TSs. Rather than converging toward a single universally optimal TS search workflow, the framework generates a sequence of specialized workflow variants that collectively solve the dataset. Applied to the Transition1X benchmark (over 10,000 reactions), this adaptive evolution refines a state-of-the-art TS-search workflow to achieve a 99.96% TS localization success rate. More broadly, these results suggest that adaptive, LLM-driven evolutionary workflow optimization provides a transferable strategy for achieving near-complete coverage in large-scale, failure-prone scientific workflows. Workflow ; Benchmark (surveying) ; Identification (biology) ; Key (lock) ; Evolutionary algorithm ; Sequence (biology) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Timur Yunusov
Source: ChemRxiv (article, 2026-03-16)
Field: Chemistry — Organic Chemistry

Artificial Intelligence for Surfactant Science and Applications: A Review This paper reviews recent advances in applications of artificial intelligence methods to solve surfactants-related tasks. As variety of surfactant species and annual production are constantly growing, a new ways to characterize them are necessary. Artificial intelligence (AI) technologies in the field of chemistry are promising instrument to perform such tasks. This review summarizes recent advances in the application of AI and machine learning (ML) to surfactant-related problems. After a brief overview of ML algorithms and typical workflows used in reviewed papers, the main applications of ML for quantitative structure-property relationship of intrinsic and industrial properties is discussed. Critical micelle concentration and surface tension are considered separately since they represent the most extensively studied properties. The review also discusses new rapidly evolving directions, including de novo generation of surfactant structures, duided experimental planning and developing of surfactant-based formulations. Finally, current challenges and future prospects of AI applications in surfactant science and applications are discussed. Overall, this review may provide solid foundation for future studies in the field and may become a good starting point for all interested researchers. Field (mathematics) ; Variety (cybernetics) ; Applications of artificial intelligence ; Workflow ; Emerging technologies Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization
Authors: Peiyao Xiao | Xiaogang Li | Chengliang Xu | Jiayi Wang | Ben Wang | Zichao Chen | Zeyu Wang | Kejun Yu | Yueqian Chen | Xulin Liu | Wende Xiao | Bing Zhao | Hu Wei
Source: ArXiv.org (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

SPM-Bench: Benchmarking Large Language Models for Scanning Probe Microscopy As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchmarks due to data contamination, insufficient complexity, and prohibitive human labor costs. Here we present SPM-Bench, an original, PhD-level multimodal benchmark specifically designed for scanning probe microscopy (SPM). We propose a fully automated data synthesis pipeline that ensures both high authority and low-cost. By employing Anchor-Gated Sieve (AGS) technology, we efficiently extract high-value image-text pairs from arXiv and journal papers published between 2023 and 2025. Through a hybrid cloud-local architecture where VLMs return only spatial coordinates "llbox" for local high-fidelity cropping, our pipeline achieves extreme token savings while maintaining high dataset purity. To accurately and objectively evaluate the performance of the LLMs, we introduce the Strict Imperfection Penalty F1 (SIP-F1) score. This metric not only establishes a rigorous capability hierarchy but also, for the first time, quantifies model "personalities" (Conservative, Aggressive, Gambler, or Wise). By correlating these results with model-reported confidence and perceived difficulty, we expose the true reasoning boundaries of current AI in complex physical scenarios. These insights establish SPM-Bench as a generalizable paradigm for automated scientific data synthesis. Benchmarking ; Pipeline (software) ; Benchmark (surveying) ; Hierarchy ; Metric (unit) ; Security token ; Performance measurement Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Andrei Voinea | Anna C.M. Thöni | Elija Veenman | Wilhelm T.S. Huck | Tal Kachman | Mathijs F.J. Mabesoone
Source: ChemRxiv (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

BigMixSolDB: Extraction of a solubility database in solvent mixtures with an uncertainty-quantified large language model-based pipeline The rapidly expanding body of chemical literature contains physical data scattered across unstructured text and complex tables in thousands of publications. Translating this information into machinereadable formats is essential for training data-driven chemical models. The lack of uncertainty quantification in computationally extracted datasets make manual extraction and curation still necessary. We here address this challenge and present an uncertainty-quantified data extraction pipeline. Our fully automated pipeline utilizes large language models (LLMs) to extract complex tabular and textual chemical data directly from scientific PDFs. We systematically assess several modern LLMs and document-to-text conversion frameworks. We benchmark the uncertainty in extracted data by comparison of our computationally extracted databases with large, existing databases on solubility that are manually curated. Our pipeline achieve between 93 and 79% of the literature contained data, with extraction errors being on par with aleatoric limits that exist in the manually curated databases. We showcase the utility of our pipeline by deploying the optimized pipeline across a diverse corpus of 2,793 articles to generate BigMixSolDB, a comprehensive database on solubility in complex mixtures. BigMixSolDB comprises over 325,000 filtered solubility entries spanning single, binary, and ternary solvent systems. Our results demonstrate how integrated LLM-based pipelines can be used for literature data extraction that is comparable with manual curation. We envision these frameworks can be used for large-scale, accurate extraction of literature data into datasets for machine learning applications. Pipeline (software) ; Benchmark (surveying) ; Extraction (chemistry) ; Information extraction ; Solubility ; Pipeline transport ; Data extraction ; Binary number Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Chang Su | Zhongkai Hao | Zhizhou Zhang | Zeyu Xia | Youjia Wu | Hang Su | Jun Zhu
Source: arXiv (Cornell University) (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving Large language models (LLMs) with reasoning abilities have demonstrated growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unbounded and open-ended, demanding exploration across vast and flexible solution spaces. Existing approaches, whether purely learning-based or reliant on carefully designed workflows, often suffer from limited exploration efficiency and poor generalization. To overcome these challenges, we present HELIX -- a Hierarchical Evolutionary reinforcement Learning framework with In-context eXperiences. HELIX introduces two key novelties: (i) a diverse yet high-quality pool of candidate solutions that broadens exploration through in-context learning, and (ii) reinforcement learning for iterative policy refinement that progressively elevates solution quality. This synergy enables the discovery of more advanced solutions. On the circle packing task, HELIX achieves state-of-the-art result with a sum of radii of 2.63598308 using only a 14B model. Across standard machine learning benchmarks, HELIX further surpasses GPT-4o with a carefully engineered pipeline, delivering an average F1 improvement of 5.95 points on the Adult and Bank Marketing datasets. Reinforcement learning ; Key (lock) ; Variety (cybernetics) ; Evolutionary algorithm ; Helix (gastropod) ; Reinforcement Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Artificial Intelligence in Healthcare and Education
Authors: Nianjun Zhou | Dhaval Patel | Anamitra Bhattacharyya
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Deployed AI Agents for Industrial Asset Management: CodeReAct Framework for Event Analysis and Work Order Automation Maintenance of mission-critical industrial assets is frequently hindered by fragmented data, inconsistent record-keeping, and limited access to analytical expertise, resulting in reactive rather than predictive practices. We present \textit{CodeReAct}, an AI-powered agentic framework deployed in large-scale facilities to automate event analysis and work order (WO) management.CodeReAct extends the ReAct paradigm by embedding executable Python code within the Thought--Action--Observation (TAO) loop, enabling natural language interaction, grounding heterogeneous alerts and work orders into structured Business Objects (BOs), and dynamically invoking analytic functions for forecasting, anomaly correlation, and maintenance recommendations. This architecture reduces manual data science intervention, improves adaptability, and supports reuse across asset types. Deployed in a mission-critical data center and productionized in Maximo, CodeReAct manages pumps, chillers, AHUs, compressors, cooling towers, and other mechanical and electrical systems. Evaluation with 36 representative maintenance utterances showed that outer-loop reflection and adaptive temperature improved task completion by up to 20%, while ablation studies confirmed the importance of reasoning in addition to code execution. Business validation revealed seasonal failure patterns, bundling opportunities, and predictive accuracy trends. In production, site engineers reported 25--40% faster diagnostics, fewer unplanned downtime events, and reduced reliance on specialized analysts. Lessons learned highlight the importance of structured BOs for grounding analytics, runtime safeguards to mitigate hallucinations, and adaptive model control for consistent execution. These results demonstrate how deployed agentic AI can deliver measurable business value in predictive and strategic maintenance planning. Executable ; Automation ; Downtime ; Work order ; Python (programming language) ; Leverage (statistics) ; Event (particle physics) ; Data integrity ; Scripting language Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Artificial Intelligence in Healthcare and Education
Authors: Zen Han Cho | Matthew Osvaldo | Sayan Doloi | Maloy Das | Jun Ci Goh | Bo Sheng Tan | Jiali Wang | Yujia Li | Xingchi Xiao | Amrita Joshi | Leonard W. T. Ng
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Large language models in materials science: assessing RAG evaluation frameworks through graphene synthesis Retrieval-Augmented Generation (RAG) systems increasingly support scientific research, yet evaluating their performance in specialized domains remains challenging due to the technical complexity and precision requirements of scientific knowledge. This study presents the first systematic analysis of automated evaluation frameworks for scientific RAG systems, using graphene synthesis in materials science as a representative case study. We develop a comprehensive evaluation protocol comparing four assessment approaches: RAGAS (an automated RAG evaluation framework), BERTScore, LLM-as-a-Judge, and expert human evaluation across 20 domain-specific questions. Our analysis of automated evaluators reveals that BERTScore lacks the interpretability and score sensitivity required to distinguish meaningful performance difference, while LLM-as-a-Judge failed to capture retrieval augmentation benefits. In contrast, RAGAS successfully captured relative performance improvements from retrieval augmentation, identifying performance gains in RAG-augmented systems (0.52-point improvement for Gemini, 1.03-point for Qwen on a 10-point scale), and demonstrating particular sensitivity to retrieval benefits in smaller, open-source models. However, it still exhibits fundamental limitations in absolute score interpretation for scientific content. These findings establish methodological guidelines for scientific RAG evaluation and highlight critical considerations for researchers deploying AI systems in specialized domains. Interpretability ; Protocol (science) ; Interpretation (philosophy) ; Sensitivity (control systems) ; Scientific literature Machine Learning in Materials Science ; Graphene research and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Yunfei Wang | Raja Vyshnavi Sriramoju | Saroj Upreti | K.T. Chan | Marcus M. Noack | Eric Schaible | Damon English | Alexander Hexemer | Wiebke Koepp | Chenhui Zhu | Xiaodan Gu
Source: ChemRxiv (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Autonomous Optimization of Block Copolymer Blend Morphology via Synchrotron X-ray Scattering The utility of block copolymers (BCPs) in next-generation nanofabrication and optoelectronics is strictly governed by the long-range ordering and precise periodicity of their microphase-separated domains. However, navigating their multidimensional processing space to maximize this structural fidelity is notoriously difficult; conventional optimization relies on Edisonian trial-and-error, which is resource-intensive, slow, and often fails to identify the global optimum. Here, we overcome this bottleneck with an autonomous morphology-discovery workflow that integrates synchrotron grazing-incidence small-angle X-ray scattering (GISAXS), automated scattering data analysis, and Gaussian process-driven Bayesian optimization. Applied to a solvent additive swelling-induced assembly of poly(styrene-b-2-vinylpyridine) (S2VP) copolymer blends, the system autonomously navigates the complex two-dimensional space of blending and solvent uptake to achieve desired polymer ordering. By jointly optimizing for both domain spacing and ordering (via scattering peak sharpness), the autonomous workflow converges on accurate targets using only 20–30 samples—a reduction of over two orders of magnitude compared to the ~3500 samples required for a traditional grid scan of various polymer blends. This work establishes a transferable, high-efficiency autonomous morphology optimization method for accelerating the discovery of highly ordered soft materials. Scattering ; Synchrotron ; Copolymer ; Block (permutation group theory) ; Bayesian optimization ; Bottleneck ; Polymer ; Nanolithography ; Lithography Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Namkyung Yoon | Sanghong Kim | Hwangnam Kim
Source: ArXiv.org (article, 2026-01-29)
Field: Chemistry — Analytical Chemistry

Conditional Generative Framework with Peak-Aware Attention for Robust Chemical Detection under Interferences Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical method for chemical substance detection, but measurement reliability tends to deteriorate in the presence of interfering substances. In particular, interfering substances cause nonspecific peaks, residence time shifts, and increased background noise, resulting in reduced sensitivity and false alarms. To overcome these challenges, in this paper, we propose an artificial intelligence discrimination framework based on a peak-aware conditional generative model to improve the reliability of GC-MS measurements under interference conditions. The framework is learned with a novel peak-aware mechanism that highlights the characteristic peaks of GC-MS data, allowing it to generate important spectral features more faithfully. In addition, chemical and solvent information is encoded in a latent vector embedded with it, allowing a conditional generative adversarial neural network (CGAN) to generate a synthetic GC-MS signal consistent with the experimental conditions. This generates an experimental dataset that assumes indirect substance situations in chemical substance data, where acquisition is limited without conducting real experiments. These data are used for the learning of AI-based GC-MS discrimination models to help in accurate chemical substance discrimination. We conduct various quantitative and qualitative evaluations of the generated simulated data to verify the validity of the proposed framework. We also verify how the generative model improves the performance of the AI discrimination framework. Representatively, the proposed method is shown to consistently achieve cosine similarity and Pearson correlation coefficient values above 0.9 while preserving peak number diversity and reducing false alarms in the discrimination model. Pattern recognition (psychology) ; Reliability (semiconductor) ; Similarity (geometry) ; Artificial neural network ; Cosine similarity ; Sensitivity (control systems) ; Generative model ; Generative grammar ; SIGNAL (programming language) Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Pavel Dytrych
Source: ChemRxiv (article, 2026-03-12)
Field: Chemistry — Organic Chemistry

EOSH-Closure: Wrong-by-Design Discovery of Ion and Radical Candidates in Binary A x B y Space via Shell-Hierarchy Arithmetic The quest for novel chemical species often relies on modifying known structural motifs or highthroughput screening of massive substituent libraries. However, a significant portion of chemical space remains unexplored because it is a priori dismissed as "electronically inadmissible" under classical valence rules. In this third installment of the EOSH-Closure series, we present a systematic "Wrongby-Design" discovery workflow that explicitly mines these perceived failures. By transitioning the Electron Ownership via Shell Hierarchy (EOSH) framework from descriptive bookkeeping to predictive mining, we scan over 28,000 charge variants of binary A x B y combinations that are inadmissible as neutral molecules. We identify that 92.6% of these theoretical "failures" can be salvaged into electronically coherent closed-shell formulations through constrained charge adjustments (±1, ±2). We report a priority list of 15 high-confidence salvage candidates, validated against a massive high-throughput PySCF screening pipeline of over 2,100 complexes. The pipeline achieves a 99.7% in-regime validation rate, successfully isolating exotic relativistic and electride-like edge cases for manual review. This work establishes EOSH as an ultra-efficient, parameter-free front-end filter for computational discovery, bridging the gap between simple integer arithmetic and intensive ab initio quantum chemistry. Binary number ; Space (punctuation) ; Ion Supramolecular Chemistry and Complexes ; Quantum Computing Algorithms and Architecture ; DNA and Biological Computing
Authors: Badri N. Patro | Vijay S. Agneeswaran
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems approaching human-level performance. We present LLMOrbit, a comprehensive circular taxonomy navigating the landscape of large language models spanning 2019-2025. This survey examines over 50 models across 15 organizations through eight interconnected orbital dimensions, documenting architectural innovations, training methodologies, and efficiency patterns defining modern LLMs, generative AI, and agentic systems. We identify three critical crises: (1) data scarcity (9-27T tokens depleted by 2026-2028), (2) exponential cost growth ($3M to $300M+ in 5 years), and (3) unsustainable energy consumption (22x increase), establishing the scaling wall limiting brute-force approaches. Our analysis reveals six paradigms breaking this wall: (1) test-time compute (o1, DeepSeek-R1 achieve GPT-4 performance with 10x inference compute), (2) quantization (4-8x compression), (3) distributed edge computing (10x cost reduction), (4) model merging, (5) efficient training (ORPO reduces memory 50%), and (6) small specialized models (Phi-4 14B matches larger models). Three paradigm shifts emerge: (1) post-training gains (RLHF, GRPO, pure RL contribute substantially, DeepSeek-R1 achieving 79.8% MATH), (2) efficiency revolution (MoE routing 18x efficiency, Multi-head Latent Attention 8x KV cache compression enables GPT-4-level performance at <$0.30/M tokens), and (3) democratization (open-source Llama 3 88.6% MMLU surpasses GPT-4 86.4%). We provide insights into techniques (RLHF, PPO, DPO, GRPO, ORPO), trace evolution from passive generation to tool-using agents (ReAct, RAG, multi-agent systems), and analyze post-training innovations. Generative grammar ; Language model ; Inference ; Taxonomy (biology) ; Scaling ; Generative model ; Embedding ; Transformer ; Efficient energy use Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Topic Modeling