jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. L. Simpson
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

URM3 — Generative Extensions and Material Findings From Admissibility Grammar to Scientific Discovery URM³ V2.2 — Generative Extensions and Material Findings extends the Unified Resonance Model (URM) from a unification and placement grammar into a disciplined framework for constrained scientific discovery. URM³ V2.1 established a subject-free, non-agentive grammatical structure for foundational description, capable of placing classical mechanics, quantum mechanics, relativity, thermodynamics, and information theory within a single admissibility landscape without modifying their internal formalisms. The present work treats that grammar as canonical and operationally binding, and asks what follows if its constraints are enforced rather than merely descriptive. This document develops derived results, material findings, conditional claims, and explicit proof obligations that emerge from the URM grammar. Central to this extension is a verb-first methodology: admissible processes (verbs) are invariant across scale, while entities and constructs (nouns) are closures of those processes and may be substituted across domains only when closure, record, and return-to-potential conditions are preserved. Using this framework, the work re-examines several long-standing foundational issues, including: the quantum–classical boundary as a closure and record threshold rather than an ontological divide, the measurement problem as interaction with record-bearing closure rather than observer privilege, and the status of “exotic” constructs (e.g. dark matter, dark energy) as potentially mis-typed nouns that must either introduce genuinely new closure behaviour or be reclassified as stability or admissibility phenomena. The document explicitly distinguishes between established grammatical consequences and results that require quantitative derivation or experimental validation, and enumerates the conditions under which URM could be falsified. No new ontological primitives are introduced, and no replacement of existing empirical models is claimed without meeting declared quantitative and observational obligations. URM³ V2.2 is intended as a foundational research framework rather than a completed physical theory. It provides a structured methodology for evaluating, extending, or rejecting theoretical proposals based on admissibility, closure, and cross-scale consistency, and is offered for open scholarly scrutiny and challenge. Generative grammar ; Unification ; Grammar ; Categorization ; Noun ; Observer (physics) ; Closure (psychology) ; Hierarchy ; Extension (predicate logic) Machine Learning in Materials Science ; Philosophy and History of Science ; Quantum Mechanics and Applications
Authors: Shiyun Zhang | Yibo Yao | Haoquan Long | Dingwen Tao | Guangming Tan | Wei-Hua Wang | Yuan-Chao Hu
Source: ArXiv.org (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials science so far. It has served as a protocol to guide experiments, modeling, and theory to uncover hidden relationships between various aspects of a certain material. This substantially facilitates knowledge accumulation and material discovery with desired functionalities to realize versatile applications. In recent years, with the advent of artificial intelligence (AI) techniques, the attention of AI towards scientific research is soaring. The trials of implementing AI in various disciplines are endless, with great potential to revolutionize the research diagram. Despite the success in natural language processing and computer vision, how to effectively integrate AI with natural science is still a grand challenge, bearing in mind their fundamental differences. Inspired by these observations and limitations, we delve into the current research paradigm dictated by the classical material tetrahedron and propose two new paradigms to stimulate data-driven and AI-augmented research. One tetrahedron focuses on AI for materials science by considering the Matter-Data-Model-Potential-Agent diagram. The other demonstrates AI research by discussing Data-Architecture-Encoding-Optimization-Inference relationships. The crucial ingredients of these frameworks and their connections are discussed, which will likely motivate both scientific thinking refinement and technology advancement. Despite the widespread enthusiasm for chasing AI for science, we must analyze issues rationally to come up with well-defined, resolvable scientific problems in order to better master the power of AI. Tetrahedron ; Enthusiasm ; Natural (archaeology) ; Artificial general intelligence ; Order (exchange) ; Paradigm shift ; Scientific discovery Machine Learning in Materials Science ; Material Selection and Properties ; Catalysis and Oxidation Reactions
Authors: Ludi Wang | Hexing Han | Xin Li | Zhiyuan Ning | Yujie Ma | Haidan Wang | Jinling Xu | Quanzhen Huang | Wenjuan Cui | Y. Zhou | Yang Gao | Bin Wang | Yi Du
Source: Advanced Science (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

MicrobeDiscover: A Knowledge Graph–Enabled AI Framework for Identifying Microbes for Inorganic Nanomaterial Biosynthesis As an environmentally friendly, mild, sustainable preparation method for nanomaterials (NMs) and the foundation of microbe-material hybrid systems, microbial synthesis of NMs hold great promise in the sustainable future. However, reported instances cover only approximately 400 microbes and 90 NMs, merely scratching the surface of the theoretical potential of enormous microbe-NMs combinations. Research methods predominantly based on empirical approaches and trial-and-error are significantly challenged in terms of screening efficiency. Here, we introduce an AI-based framework, MicrobeDiscover, which identifies potential microbes for NM synthesis within a vast search space by integrating and representing microorganisms, NMs, and their interactions. A central component of MicrobeDiscover is a knowledge graph guided by expert insights into microbial synthesis, bridging microbiological and materials science domains to provide the AI model with robust data for screening and predictive modeling. Among the top 20 microorganisms predicted by MicrobeDiscover, the recommendation success efficiency reached 80.77%. Leveraging the framework's predictions, we successfully synthesized several kinds of trimetallic NMs using Shewanella oneidensis MR-1 as suggested by MicrobeDiscover, in the context of non-trimetallic NMs were reported producing in a biosynthetic way. This approach is anticipated to significantly advance the development of effective microorganisms and enhance the controllable synthesis of NMs. Shewanella oneidensis ; Context (archaeology) ; Metagenomics ; Chemical space ; Bridging (networking) ; Synthetic biology ; Component (thermodynamics) Nanoparticles: synthesis and applications ; Microbial Fuel Cells and Bioremediation ; Graphene and Nanomaterials Applications
Authors: Yifeng Guan | Chuyi Liu | Dongzhan Zhou | Lei Bai | Wan‐Jian Yin | Jingyuan Li | Mao Su
Source: ArXiv.org (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression Discovering interpretable physical laws from high-dimensional data is a fundamental challenge in scientific research. Traditional methods, such as symbolic regression, often produce complex, unphysical formulas when searching a vast space of possible forms. We introduce a framework that guides the search process by leveraging the embedded scientific knowledge of large language models, enabling efficient identification of physical laws in the data. We validate our approach by modeling key properties of perovskite materials. Our method mitigates the combinatorial explosion commonly encountered in traditional symbolic regression, reducing the effective search space by a factor of approximately $10^5$. A set of novel formulas for bulk modulus, band gap, and oxygen evolution reaction activity are identified, which not only provide meaningful physical insights but also outperform previous formulas in accuracy and simplicity. Physical law ; Identification (biology) ; Process (computing) ; Set (abstract data type) ; Combinatorial explosion ; Space (punctuation) ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Catalysis and Oxidation Reactions
Authors: Abdulla Al Tareq | Abrarul Haque
Source: Scientia. Technology, science and society. (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Zinc Oxide (ZnO) Thin Film Semiconductor Applications The main objective of this paper is to review the main applications of ZnO application in semiconductor devices. The study elucidates the varied terrain of ZnO thin films by an in-depth examination of real-world examples, highlighting their versatility and revolutionary potential across multiple disciplines. The case study illustrates the importance of ZnO thin films in defining modern technologies by evaluating its integration in electronic devices, optoelectronic systems, and sensors. Furthermore, the research digs into the complexities of manufacturing techniques spanning from physical vapor deposition to chemical approaches, demonstrating the precise methodologies that permit customized thin film qualities. The research also examines issues such as interface engineering and material compatibility, offering light on solutions for addressing these constraints. As the study ends, it provides insights into the ongoing evolution of ZnO-based thin film semiconductor applications, which are situated at the crossroads of innovation and practical implementation, paving the way for future advancements and breakthroughs. The case study forecasts the trajectory that ZnO-based thin film semiconductor applications will take. The study uses lessons learned from historical history and present utilization to highlight the dynamic nature of this issue, where innovation always meets with practical application. The case studies forecast future breakthroughs as technology and demand evolve, presumably driven by synergies between ZnO thin films and emerging technologies such as nanotechnology, quantum computing, and flexible electronics. This case study, in essence, serves as a thorough entryway into the domain of ZnO-based thin film semiconductor applications, exposing their astonishing diversity and defining a course for their ongoing significance in molding our technological landscape. Thin film ; Semiconductor ; Physical vapor deposition ; Chemical vapor deposition ; Emerging technologies ; Interface (matter) ; Situated ; Semiconductor device ZnO doping and properties ; Chemical and Physical Properties of Materials ; Thin-Film Transistor Technologies
Authors: Abhinaba Basu | Pavan Chakraborty
Source: arXiv (Cornell University) (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials Machine-learned interatomic potentials (MLIPs) are deployed for high-throughput materials screening without formal reliability guarantees. We show that a single MLIP used as a stability filter misses 93% of density functional theory (DFT)-stable materials (recall 0.07) on a 25,000-material benchmark. Proof-Carrying Materials (PCM) closes this gap through three stages: adversarial falsification across compositional space, bootstrap envelope refinement with 95% confidence intervals, and Lean 4 formal certification. Auditing CHGNet, TensorNet and MACE reveals architecture-specific blind spots with near-zero pairwise error correlations (r <= 0.13; n = 5,000), confirmed by independent Quantum ESPRESSO validation (20/20 converged; median DFT/CHGNet force ratio 12x). A risk model trained on PCM-discovered features predicts failures on unseen materials (AUC-ROC = 0.938 +/- 0.004) and transfers across architectures (cross-MLIP AUC-ROC ~ 0.70; feature importance r = 0.877). In a thermoelectric screening case study, PCM-audited protocols discover 62 additional stable materials missed by single-MLIP screening - a 25% improvement in discovery yield. Reliability (semiconductor) ; Density functional theory ; Pairwise comparison ; Feature (linguistics) ; Stability (learning theory) ; Envelope (radar) ; Margin (machine learning) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Inorganic Chemistry and Materials
Authors: Xinwu Ye | Yicheng Mao | Jia Zhang | Yimeng Liu | Li Hao | Fang Wu | Zhiwei Li | Yuxuan Liao | Zehong Wang | Wu, Yingcheng | Liu, Zhiyuan | Li Yuan | Yuan, Li | Torr, Philip | Sun, Huan | Zeng, Xiangxiang | Wang, Mengdi | Cong, Le | Gao, Shenghua | Xiangru Tang
Source: ArXiv.org (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning Chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) in natural language to perform complex reasoning. However, chemical reasoning is inherently continuous and structural, and forcing it into discrete linguistic tokens introduces a fundamental representation mismatch that constrains both efficiency and performance. We introduce LatentChem, a latent reasoning interface that decouples chemical computation from textual generation, enabling models to perform multi-step reasoning directly in continuous latent space while emitting language only for final outputs. Remarkably, we observe a consistent emergent behavior: when optimized solely for task success, models spontaneously internalize reasoning, progressively abandoning verbose textual derivations in favor of implicit latent computation. This shift is not merely stylistic but computationally advantageous. Across diverse chemical reasoning benchmarks, LatentChem achieves a 59.88\% non-tie win rate over strong CoT-based baselines on ChemCoTBench, while delivering a 10.84$\times$ average reduction in reasoning overhead. Our results provide empirical evidence that chemical reasoning is more naturally and effectively realized as continuous latent dynamics rather than discretized linguistic trajectories. Inference ; Representation (politics) ; Forcing (mathematics) ; Natural language ; Computation ; Knowledge representation and reasoning ; Task (project management) ; Language model Machine Learning in Materials Science ; Topic Modeling ; Multimodal Machine Learning Applications
Authors: Bowen Han | Jiarui Miao
Source: ChemRxiv (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

ChemCognitoGuard: A Human-AI Collaborative Framework for Traceable and Cognitively Sustainable Verification of Chemical Scientific Literature The rapid integration of Artificial Intelligence (AI) in chemical science has introduced a "capability-understandability gap," where advanced AI performance outpaces human ability to interpret and verify results. This phenomenon exacerbates existing challenges in scientific literature, such as prevalent miscitations and the difficulty in precisely identifying and localizing complex chemical errors, which traditional AI tools often fail to address with sufficient interpretability and traceability. To counter these issues, we propose ChemCognitoGuard (CCG), a human-AI collaborative framework specifically engineered to enhance the accuracy, traceability, and cognitive sustainability of scientific literature review in chemistry. CCG comprises three synergistic modules: the Cognitive Integrity Verification Engine (CIVE) for human cognitive autonomy; the Chem-Relational Traceability Tracker (CRTT) for multigranularity evidence aggregation and contextual modeling of intricate chemical claims, even for restricted access literature; and the ReactSeq-Enhanced Error Localization & Correction (RELC) module, leveraging chemistry-specific knowledge for precise error detection and actionable correction suggestions. We introduce SciChemVerifyBench, a new benchmark dataset tailored for chemical literature verification, featuring expert-inserted errors and simulated restricted access scenarios. Comprehensive evaluations against state-of-the-art Large Language Models reveal CCG’s superior performance across all metrics, achieving significantly higher error detection accuracy, error localization accuracy, chemical claim traceability, and explanation quality. These results, especially its robust traceability for restricted-access claims and enhanced interpretability, demonstrate CCG’s effectiveness in bridging the capabilityunderstandability gap, fostering greater trust, efficiency, and scientific rigor in chemistry research through symbiotic human-AI collaboration. Interpretability ; Traceability ; Cognition ; Bridging (networking) ; Workflow ; Scientific literature Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Bogdan Zagribelnyy | Ivan Ilin | Maksim Kuznetsov | Nikita Bondarev | Roman Schutski | Thomas MacDougall | Rim Shayakhmetov | Zulfat Miftakhutdinov | Mikolaj Mizera | Vladimir Aladinskiy | Alex Aliper | Alex Zhavoronkov
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evaluation of retrosynthesis performance remains limited. Existing benchmarks and metrics typically rely on published synthetic procedures and Top-K accuracy based on single ground-truth, which does not capture the open-ended nature of real-world synthesis planning. We propose a new benchmarking framework for single-step retrosynthesis that evaluates both general-purpose and chemistry-specialized LLMs using ChemCensor, a novel metric for chemical plausibility. By emphasizing plausibility over exact match, this approach better aligns with human synthesis planning practices. We also introduce CREED, a novel dataset comprising millions of ChemCensor-validated reaction records for LLM training, and use it to train a model that improves over the LLM baselines under this benchmark. Benchmarking ; Retrosynthetic analysis ; Metric (unit) ; Compensation (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Mingjian Lu | Pawan K. Tripathi | Mark Shteyn | Debargha Ganguly | Roger H. French | Vipin Chaudhary | Yinghui Wu
Source: arXiv (Cornell University) (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Context Determines Optimal Architecture in Materials Segmentation Segmentation architectures are typically benchmarked on single imaging modalities, obscuring deployment-relevant performance variations: an architecture optimal for one modality may underperform on another. We present a cross-modal evaluation framework for materials image segmentation spanning SEM, AFM, XCT, and optical microscopy. Our evaluation of six encoder-decoder combinations across seven datasets reveals that optimal architectures vary systematically by context: UNet excels for high-contrast 2D imaging while DeepLabv3+ is preferred for the hardest cases. The framework also provides deployment feedback via out-of-distribution detection and counterfactual explanations that reveal which microstructural features drive predictions. Together, the architecture guidance, reliability signals, and interpretability tools address a practical gap in materials characterization, where researchers lack tools to select architectures for their specific imaging setup or assess when models can be trusted on new samples. Interpretability ; Segmentation ; Context (archaeology) ; Architecture ; Reliability (semiconductor) ; Image segmentation ; Modality (human–computer interaction) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuting Zheng | Zijian Chen | Qi Jia
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Benchmarking Cross-Scale Perception Ability of Large Multimodal Models in Material Science Unraveling the hierarchical structure-property relationships is the central challenge of materials science, necessitating the interpretation of data across vast physical scales from micro to macro. Despite the rapid integration of Large Multimodal Models (LMMs) into scientific workflows, existing scientific benchmarks primarily focus on general chart interpretation or isolated common-sense reasoning, failing to capture reasoning ability across intricate physical dimensions. To address this, we introduce CSMBench, a dataset comprising 1,041 high-quality figures curated from premier journals up to September 2025. CSMBench categorizes data into four scientifically distinct regimes: atomic, micro, meso, and macro scales, strictly aligning with the focus and definitions in materials study. Through open-ended figure description and multiple-choice caption matching tasks, we evaluate state-of-the-art open-source and closed-source models. Our analysis identifies that performance varies significantly across physical scales due to the distinct visual characteristics, highlighting the limitations of current generalist models and identifying critical directions for achieving hierarchical and accurate understanding in materials research. The CSMBench is publicly released at: https://huggingface.co/datasets/lututu/CSMBench. Benchmarking ; Focus (optics) ; Interpretation (philosophy) ; Matching (statistics) ; Macro ; Perception ; Term (time) ; Visual reasoning Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Advanced Graph Neural Networks
Authors: Kenan Li | Yijian Zhang | Jin Wang | Haipeng Gan | Zeying Sun | Xiaoguang Lei | Hao Dong
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. While LLMs have been applied to molecular simplified molecular input line entry system (SMILES) in computer-aided synthesis planning (CASP), existing methodologies typically address single tasks, such as precursor prediction. We introduce ChemBART, a SMILES-based LLM pre-trained on chemical reactions, which enables a unified model for multiple downstream chemical tasks--achieving the paradigm of "one model, one pre-training, multiple tasks." By leveraging outputs from a mask-filling pre-training task on reaction expressions, ChemBART effectively solves a variety of chemical problems, including precursor/reagent generation, temperature-yield regression, molecular property classification, and optimizing the policy and value functions within a reinforcement learning framework, integrated with Monte Carlo tree search for multi-step synthesis route design. Unlike single-molecule pre-trained LLMs constrained to specific applications, ChemBART addresses broader chemical challenges and integrates them for comprehensive synthesis planning. Crucially, ChemBART-designed multi-step synthesis routes and reaction conditions directly inspired wet-lab validation, which confirmed shorter pathways with ~30% yield improvement over literature benchmarks. Our work validates the power of reaction-focused pre-training and showcases the broad utility of ChemBART in advancing the complete synthesis planning cycle. Variety (cybernetics) ; Tree (set theory) ; Task (project management) ; Yield (engineering) ; Reinforcement learning ; Transformative learning Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods