jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenyang Jia | Ziwei Yao | Tassos Koidis | Yunhe Hong | Di Wu | Honggang Huang
Source: ChemRxiv (article, 2026-03-05)
Field: Chemistry — Analytical Chemistry

Grand Banquet: A next-generation chemometrics framework for classification analysis on data matrix The advancement of high-throughput analytical instrumentation has placed analytical science into the "Fourth Paradigm" of data-intensive discovery. In this new era, the limitation of scientific insight is not merely data acquisition but the objective, reproducible interpretation of high-dimensional data matrices. Conventional chemometric workflows remain fundamentally reliant on "try-and-trust" methodologies. These expert-assisted paradigms require analysts to manually select preprocessing filters, feature selection methods, and classification models based on intuition or limited resources. Such subjectivity introduces analyst bias, raising concerns about reproducibility when applied to biological or chemical signals. Chemometrics ; Preprocessor ; Intuition ; Feature selection ; Workflow ; Data pre-processing ; Selection (genetic algorithm) ; Data acquisition Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Bowen Mou | Ruotong Lou
Source: ChemRxiv (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Enhanced Chemical Reasoning Agent: Boosting Trustworthy LLM Performance in Chemistry via Multi-Stage Reasoning and Calibrated Confidence Large language models (LLMs) show immense promise in chemistry but struggle with deep knowledge comprehension, complex multi-step reasoning, and output trustworthiness, particularly overconfident predictions. Inspired by the ChemBench evaluation framework, we propose the Enhanced Chemical Reasoning Agent (ECRA). ECRA is an intelligent agent framework built upon existing powerful LLMs, integrating modules for structured problemsolving, leveraging external computational resources, and dynamically estimating and selfcalibrating confidence to address overconfidence. Fabricated experimental results on the ChemBench-Mini subset demonstrate ECRA's superior performance, achieving higher overall accuracy than state-of-the-art LLMs. Critically, ECRA significantly improves reasoning and intuition capabilities and dramatically reduces expected calibration error, indicating a groundbreaking enhancement in output trustworthiness. Ablation studies confirm each module's vital role. Human evaluations and detailed error analyses further underscore ECRA's ability to deliver more accurate, explainable, and reliably calibrated chemical insights, paving the way for safer and more effective LLM deployment in scientific applications. Boosting (machine learning) ; Trustworthiness ; Intuition ; Software deployment ; SAFER Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Aaditya Baranwal | Shruti Vyas
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

ChemPro: A Progressive Chemistry Benchmark for Large Language Models We introduce ChemPro a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in general scientific reasoning and understanding and point towards understudied dimensions of difficulty, emphasizing the need for more robust methodologies to improve LLMs. Benchmark (surveying) ; Point (geometry) ; Broad spectrum ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Text Readability and Simplification ; Topic Modeling
Authors: Aaditya Baranwal | Shruti Vyas
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

ChemPro: A Progressive Chemistry Benchmark for Large Language Models We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in general scientific reasoning and understanding and point towards understudied dimensions of difficulty, emphasizing the need for more robust methodologies to improve LLMs. Benchmark (surveying) ; Point (geometry) ; Broad spectrum ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Text Readability and Simplification ; Topic Modeling
Authors: Aaditya Baranwal | Shruti Vyas
Source:  (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

ChemPro: A Progressive Chemistry Benchmark for Large Language Models We introduce ChemPro, a progressive benchmark with 4100 natural language question-answer pairs in Chemistry, across 4 coherent sections of difficulty designed to assess the proficiency of Large Language Models (LLMs) in a broad spectrum of general chemistry topics. We include Multiple Choice Questions and Numerical Questions spread across fine-grained information recall, long-horizon reasoning, multi-concept questions, problem-solving with nuanced articulation, and straightforward questions in a balanced ratio, effectively covering Bio-Chemistry, Inorganic-Chemistry, Organic-Chemistry and Physical-Chemistry. ChemPro is carefully designed analogous to a student's academic evaluation for basic to high-school chemistry. A gradual increase in the question difficulty rigorously tests the ability of LLMs to progress from solving basic problems to solving more sophisticated challenges. We evaluate 45+7 state-of-the-art LLMs, spanning both open-source and proprietary variants, and our analysis reveals that while LLMs perform well on basic chemistry questions, their accuracy declines with different types and levels of complexity. These findings highlight the critical limitations of LLMs in general scientific reasoning and understanding and point towards understudied dimensions of difficulty, emphasizing the need for more robust methodologies to improve LLMs. Benchmark (surveying) ; Point (geometry) ; Broad spectrum ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Text Readability and Simplification ; Topic Modeling
Authors: Durbek Usmanov | Priyanka Yadav | Gerardo M. Casanola-Martin | Akshat Satyanarayan Mallya | Seyedehelham Shirvanihosseini | Allison Hubel | Bakhtiyor Rasulev
Source: Green Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Framework to Predict Glass Transition Temperature in Natural Deep Eutectic Solvents: A Step toward Green Functional Materials Natural Deep Eutectic Solvents (NADES) are a promising class of sustainable and environmentally-safe solvents with highly tunable physicochemical properties, including the glass transition temperature, which is critical for their functional... Eutectic system ; Glass transition ; Class (philosophy) ; Deep learning ; Natural (archaeology) Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications ; Ionic liquids properties and applications
Authors: Cong Liu (66219) | Chengyue Gong | Zhenyu Liu | Jiale Zhao | Yuxuan Zhang
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models Generative models hold great promise for accelerating material discovery but are often limited by their inflexible single-stage generative process in designing valid and diverse materials. To address this, we propose a two-stage generative framework, Lang2Str, that combines the strengths of large language models (LLMs) and flow-based models for flexible and precise material generation. Our method frames the generative process as a conditional generative task, where an LLM provides high-level conditions by generating descriptions of material unit cells' geometric layouts and properties. These descriptions, informed by the LLM's extensive background knowledge, ensure reasonable structure designs. A conditioned flow model then decodes these textual conditions into precise continuous coordinates and unit cell parameters. This staged approach combines the structured reasoning of LLMs and the distribution modeling capabilities of flow models. Experimental results show that our method achieves competitive performance on \textit{ab initio} material generation and crystal structure prediction tasks, with generated structures exhibiting closer alignment to ground truth in both geometry and energy levels, surpassing state-of-the-art models. The flexibility and modularity of our framework further enable fine-grained control over the generation process, potentially leading to more efficient and customizable material design. Generative grammar ; Generative Design ; Modularity (biology) ; Flexibility (engineering) ; Generative model ; Process (computing) ; Flow (mathematics) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Material Selection and Properties
Authors: Paweł Mateusz Nowak
Source: Analytical Chemistry (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Analytical Chemistry: Towards Yet Undiscovered Opportunities Artificial intelligence (AI) has become deeply embedded in analytical chemistry, supporting data processing, chemometric modeling, and experimental design. Yet, its potential can be extended far beyond these familiar applications. This Perspective outlines several emerging directions in which AI may reshape scientific writing, method evaluation, and organization of analytical knowledge. It discusses opportunities for AI-assisted clarity in scholarly communication, new evaluation frameworks including i-metrics and AI-Delphi, and the role of negative results, knowledge bases, and implementation-oriented research in building a more circular information ecosystem. It is also proposed that analytical chemistry may even help establish an emerging "Analytics of Intelligent Systems" by extending its frameworks for method evaluation and uncertainty analysis to the systematic characterization of AI models. This Perspective is not a manual but a vision of how analytical chemistry should evolve in the future. Ultimately, it is written to inspire every reader, including intelligent machines. If AI continues to learn from us, we must ensure that it also understands our long-term goals and what truly matters in analytical science. CLARITY ; Perspective (graphical) ; Applications of artificial intelligence ; Characterization (materials science) ; Big data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Carolina L. Recio-Colmenares | Roxana Recio-Colmenares | Francisco E. Castillo-Barrera | César García-García
Source: Applied Sciences (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

An Ontology-Based Framework for Semantic Integration and Interoperable Assessment of Green-Synthesized Nanomaterials for Environmental Remediation Green-synthesized nanomaterials have emerged as promising candidates for environmentally sustainable remediation; however, experimental evidence describing their synthesis routes, physicochemical properties, remediation performance, and sustainability-relevant attributes remains fragmented, inconsistently reported, and difficult to integrate across studies. This work addresses this challenge by proposing an ontology-based semantic framework for the interoperable integration of green-synthesized nanomaterials, contaminants, and remediation processes, incorporating explicit provenance metadata and structured sustainability descriptors. The ontology was developed using the Linked Open Terms (LOT) methodology and implemented in OWL 2 DL, with selective alignment to established vocabularies including eNanoMapper, ChEBI, ENVO, and PROV-O. Adsorption and photocatalysis were instantiated as representative remediation mechanisms to evaluate the framework’s capacity to accommodate structurally distinct processes. Logical reasoning and SHACL-based validation were applied to assess semantic consistency, provenance traceability, and data completeness. The results demonstrate that the proposed ontology effectively integrates heterogeneous experimental data within a unified, FAIR-compliant semantic framework, supports conservative and provenance-aware inference, and enables comparative analysis across mechanistically diverse remediation systems without structural modification. This ontology-based approach provides a robust foundation for sustainability-aware knowledge integration in environmental nanotechnology and establishes the basis for future extensions involving data quality assessment and explainable AI-driven analysis. Interoperability ; Ontology ; Semantic integration ; Semantic interoperability ; Metadata ; Semantic Web ; Data integration ; Semantic heterogeneity Machine Learning in Materials Science ; Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials
Authors: M. Busch | M. Tacke | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Christian J. Cyron | Christian Feiler | Roland C. Aydin
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts The capabilities of large language models (LLMs) have expanded beyond natural language processing to scientific prediction tasks, including molecular property prediction. However, their effectiveness in in-context learning remains ambiguous, particularly given the potential for training data contamination in widely used benchmarks. This paper investigates whether LLMs perform genuine in-context regression on molecular properties or rely primarily on memorized values. Furthermore, we analyze the interplay between pre-trained knowledge and in-context information through a series of progressively blinded experiments. We evaluate nine LLM variants across three families (GPT-4.1, GPT-5, Gemini 2.5) on three MoleculeNet datasets (Delaney solubility, Lipophilicity, QM7 atomization energy) using a systematic blinding approach that iteratively reduces available information. Complementing this, we utilize varying in-context sample sizes (0-, 60-, and 1000-shot) as an additional control for information access. This work provides a principled framework for evaluating molecular property prediction under controlled information access, addressing concerns regarding memorization and exposing conflicts between pre-trained knowledge and in-context information. Blinding ; Memorization ; Property (philosophy) ; Causal chain ; Control (management) ; Sample (material) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ravi Varala | Pramod S. Kulkarni | Ramesh B. Gawade
Source: Organic Communications (article, 2026-02-20)
Field: Chemistry — Organic Chemistry

DBUHI3-catalyzed efficient synthesis of 1,4-dihydropyridines A process-optimized, one-pot multicomponent reaction catalyzed by DBUHI 3 was developed, enabling the synthesis of diverse 1,4-dihydropyridines (1,4-DHPs), a class of bioactive nitrogen heterocycles from substituted benzaldehydes, ethyl acetoacetate, and ammonium acetate under mild conditions in DMSO.The methodology afforded good to excellent yields for a variety of aryl aldehyde substrates bearing both electron-donating and electronwithdrawing groups.Structural confirmation was achieved through IR, 1 H NMR, 13 C NMR, and HRMS analyses.This work not only summarizes key synthetic strategies but also provides a practical, sustainable route to the preparation of 1,4-DHPs, supporting further medicinal and synthetic exploration. Yield (engineering) ; Set (abstract data type) ; Process (computing) ; Identification (biology) ; Product (mathematics) Multicomponent Synthesis of Heterocycles ; Catalytic Cross-Coupling Reactions ; Synthesis and pharmacology of benzodiazepine derivatives
Authors: Alex Morehead | Miruna Cretu | Antonia Panescu | Rishabh Anand | Maurice Weiler | Tynan J. Perez | Samuel Blau | Steven Farrell | W. Bhimji | Anubhav Jain | Hrushikesh P. Sahasrabuddhe | Pietro Lio | Tommi Jaakkola | Rafael Gomez-Bombarelli | Rex Ying | N. Benjamin Erichson | Michael W. Mahoney
Source: arXiv (Cornell University) (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimized for a single domain (molecules or materials) and a single task (generation or prediction), which limits representation sharing and transfer. We introduce Zatom-1, the first end-to-end, fully open-source foundation model that unifies generative and predictive learning of 3D molecules and materials. Zatom-1 is a Transformer trained with a multimodal flow matching objective that jointly models discrete atom types and continuous 3D geometries. This approach supports scalable pretraining with predictable gains as model capacity increases, while enabling fast and stable sampling. We use joint generative pretraining as a universal initialization for downstream multi-task prediction of properties, energies, and forces. Empirically, Zatom-1 matches or outperforms specialized baselines on both generative and predictive benchmarks, while reducing the generative inference time by more than an order of magnitude. Our experiments demonstrate positive predictive transfer between chemical domains from joint generative pretraining: modeling materials during pretraining improves molecular property prediction accuracy. Open-source code: https://github.com/Zatom-AI/zatom Generative grammar ; Initialization ; Generative model ; Inference ; Generative Design ; Scalability ; Matching (statistics) ; Equivalence (formal languages) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zhenzhong Wang | Yuhan Hou | Chenggong Huang | Yuxuan Du | Dacheng Tao | Min Jiang
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational chemistry and materials science. Accurate prediction is highly reliant on extensive training data, including precise molecular geometries and the Hamiltonian matrices, which are expensive to acquire via either experimental or computational methods. Towards a fast yet accurate method for Hamiltonian prediction, we first introduce a geometry information-aware molecular language model to bypass the use of expensive molecular geometries by only using the readily available molecular language -- simplified molecular input line entry system (SMILES). Our method employs multimodal alignment to bridge the relationship between SMILES strings and their corresponding molecular geometries. Recognizing that the molecular language inherently lacks explicit geometric information, we propose a geometry modality compensation strategy to imbue molecular language representations with essential geometric features, thereby enabling accurate predictions using SMILES. In addition, given the high cost of acquiring Hamiltonian data, we devise a weakly supervised strategy to fine-tune the molecular language model, thus improving the data efficiency. Theoretically, we prove that the prediction generalization error without explicit molecular geometry can be bounded through our modality compensation scheme. Empirically, our method achieves superior computational efficiency, providing up to 100x speedup over conventional quantum mechanical methods while maintaining comparable prediction accuracy. We further demonstrate the practical case study of our approach in the screening of electrolyte formulations. Hamiltonian (control theory) ; Quantum ; Bounded function ; Hamiltonian mechanics ; Generalization ; Geometric modeling ; Hamiltonian system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xuhui Shi
Source: Academic Journal of Science and Technology (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Collaborative Integration of Large Language Models and Computer Algebra Systems for Simulation Verification and Code Generation This paper explores the transformative role of Large Language Models (LLMs) in scientific computing and research. As scientific datasets increasingly contain unstructured or semi-structured text, preprocessing and structuring this data are crucial challenges. LLMs have shown great potential in automating data extraction, transforming raw text into structured formats for computational models. By utilizing techniques such as Named Entity Recognition (NER), LLMs can extract critical information like chemical names, experimental conditions, and research findings, significantly reducing manual efforts. In addition to data preprocessing, LLMs facilitate literature reviews by rapidly scanning vast amounts of research papers, summarizing key insights, and constructing knowledge graphs to visualize relationships across complex datasets. Furthermore, LLMs contribute to problem-solving by generating theoretical insights and assisting in mathematical and computational tasks. Finally, LLMs can support scientific programming by automating code generation for data analysis and simulations. These capabilities enhance efficiency, foster faster discoveries, and lower the barrier to managing complex scientific data, ultimately accelerating research in multiple scientific domains. Transformative learning ; Preprocessor ; Structuring ; Key (lock) ; Code (set theory) ; Raw data ; Computational model ; Data modeling ; Code generation Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling