jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rahul Chouhan | Dheeraj Parmar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

The Blind Discovery Challenge: Autonomous Physics Derivation in 1,000,000 Quantum Simulations "Autonomous scientific discovery requires agents that can derive fundamental physical laws from raw observation without human intervention or prior domain knowledge. We present the 'Blind Discovery Challenge,' a high-throughput simulation where a Scientific ASI (Prometheus 1T) was tasked with uncovering randomized quantum physical laws across 1,000,000 experiment cycles. Utilizing an Active Inference engine based on the Free Energy Principle, the system autonomously reconstructed the latent causal structure (A-matrix) of unknown quantum mechanisms—including Majorana Fermion States, Weyl Semimetals, and Topological Insulators—in under 0.1 seconds. The agent correctly identified high-correlation diagnostic signatures, such as Zero-Bias Conductance and Berry Curvature, with 100% accuracy. This demonstration showcases a $10^6\times$ acceleration over traditional experimental design, establishing a robust paradigm for 'Post-Prior Science' in accelerated materials discovery and quantum computing." Scientific discovery ; Quantum ; Domain (mathematical analysis) ; Physical law ; Quantum computer ; MAJORANA ; Physical system Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Guannan Tang | Noah H. Paulson
Source: Research Square (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

An LLM-Agentic Workflow for Data-Driven Modeling: From Image Reconstruction to Thermodynamic Modeling Workflow ; Identification (biology) ; Component (thermodynamics) ; Weighting ; Task (project management) ; Event (particle physics) ; CALPHAD Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Catalysis and Oxidation Reactions
Authors: Taolin Han | Shuang Wu | Jinghang Wang | Yuhao Zhou | Renquan Lv | Bing Zhao | Wei Hu
Source: arXiv (Cornell University) (preprint, 2026-03-26)
Field: Materials Science — Materials Chemistry

MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation Large language models (LLMs) hold considerable potential for advancing scientific discovery, yet systematic assessment of their dynamic reasoning in real-world research remains limited. Current scientific evaluation benchmarks predominantly rely on static, single-turn Question Answering (QA) formats, which are inadequate for measuring model performance in complex scientific tasks that require multi-step iteration and experimental interaction. To address this gap, we introduce MolQuest, a novel agent-based evaluation framework for molecular structure elucidation built upon authentic chemical experimental data. Unlike existing datasets, MolQuest formalizes molecular structure elucidation as a multi-turn interactive task, requiring models to proactively plan experimental steps, integrate heterogeneous spectral sources (e.g., NMR, MS), and iteratively refine structural hypotheses. This framework systematically evaluates LLMs' abductive reasoning and strategic decision-making abilities within a vast and complex chemical space. Empirical results reveal that contemporary frontier models exhibit significant limitations in authentic scientific scenarios: notably, even state-of-the-art (SOTA) models achieve an accuracy of only approximately 50%, while the performance of most other models remains below the 30% threshold. This work provides a reproducible and extensible framework for science-oriented LLM evaluation, our findings highlight the critical gap in current LLMs' strategic scientific reasoning, setting a clear direction for future research toward AI that can actively participate in the scientific process. Abductive reasoning ; Benchmark (surveying) ; Scientific modelling ; Plan (archaeology) ; Scientific reasoning ; Experimental data ; Proof of concept ; Flexibility (engineering) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Taolin Han | Shuang Wu | Jinghang Wang | Yuhao Zhou | Renquan Lv | Bing Zhao | Wei Hu
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation Large language models (LLMs) hold considerable potential for advancing scientific discovery, yet systematic assessment of their dynamic reasoning in real-world research remains limited. Current scientific evaluation benchmarks predominantly rely on static, single-turn Question Answering (QA) formats, which are inadequate for measuring model performance in complex scientific tasks that require multi-step iteration and experimental interaction. To address this gap, we introduce MolQuest, a novel agent-based evaluation framework for molecular structure elucidation built upon authentic chemical experimental data. Unlike existing datasets, MolQuest formalizes molecular structure elucidation as a multi-turn interactive task, requiring models to proactively plan experimental steps, integrate heterogeneous spectral sources (e.g., NMR, MS), and iteratively refine structural hypotheses. This framework systematically evaluates LLMs' abductive reasoning and strategic decision-making abilities within a vast and complex chemical space. Empirical results reveal that contemporary frontier models exhibit significant limitations in authentic scientific scenarios: notably, even state-of-the-art (SOTA) models achieve an accuracy of only approximately 50%, while the performance of most other models remains below the 30% threshold. This work provides a reproducible and extensible framework for science-oriented LLM evaluation, our findings highlight the critical gap in current LLMs' strategic scientific reasoning, setting a clear direction for future research toward AI that can actively participate in the scientific process. Abductive reasoning ; Benchmark (surveying) ; Scientific modelling ; Plan (archaeology) ; Scientific reasoning ; Experimental data ; Proof of concept ; Flexibility (engineering) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Hongliang Xin | John R Kitchin | Núria López | Neil M. Schweitzer | Milad Abolhasani | Nongnuch Artrith | Líney Árnadóttir | Kamal Choudhary | Rui Ding | Anatoly I. Frenkel | Joseph A. Gauthier | Bryan R Goldsmith | Amir Barati Farimani | Lars C. Grabow | G. T. Kasun Kalhara Gunasooriya | Guoxiang Hu | Tyler R. Josephson | Heather J Kulik | RA Kumar | Teodoro Laino | H. C. Li | Xinwei Li | Wanlu Li | Suljo Linic | Chong Liu | Cong Liu | Fudong Liu | Mingjie Liu | Piao Ma | Andrew J Medford | Sukrit Mukhopadhyay | Pengfei Ou | Christopher Paolucci | Jiayu Peng | C. B. Phillips | Marc D. Porosoff | Long Qi | Andrew S Rosen | Shijing Sun | Tibor Szilvási | Johannes Voss | Siwen Wang | X. L. Wang | Kirsten Winther | Qin Wu | Di Zhang | Zheng Zhang
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence (TRACE-AI): Community Guidelines and A Publication Checklist Artificial intelligence (AI) is increasingly integrated into catalysis science, enabling agentic workflows in which AI systems perceive inputs, reason under constraints, plan, and autonomously execute in silico or physical experiments with minimal human intervention. While these closed-loop capabilities hold promise to accelerate knowledge generation and technological innovation, they inevitably introduce new sources of variability in data lineage, model specification, and agent policies that can undermine FAIRness, rigor, and reproducibility. These risks are particularly pronounced in heterogeneous catalysis, where subtleties in catalyst synthesis and pretreatment, dynamic restructuring under operating conditions, and transport-mediated local environments can largely determine catalytic outcomes. To address these challenges, we introduce TRACE-AI (Transparent Reporting for Agentic Catalysis Enabled by Artificial Intelligence) as a set of community guidelines paired with a publication checklist. TRACE-AI emphasizes end-to-end traceability across the full lifecycle of an agentic catalysis campaign, linking research objectives to data and models, agent reasoning and action, and the knowledge acquired. By promoting standardized and accountable reporting, TRACE-AI aims to cultivate a shared foundation for accelerating scientific discovery while reinforcing safety and trust as autonomous catalysis laboratories continue to emerge. Workflow ; Traceability ; Restructuring ; Set (abstract data type) ; Checklist Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Mohammed Muttayem Mahi Khan
Source: International Journal of Science and Research (IJSR) (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning in Physical Sciences Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming research methodologies in the physical sciences by enabling data-driven discovery, predictive modelling, and accelerated simulations. The increasing availability of high-dimensional experimental and computational data has created significant challenges for traditional analytical and numerical approaches. Machine learning techniques, including supervised learning, deep learning, and physics-informed neural networks, provide powerful alternatives by learning complex nonlinear relationships directly from data while complementing established physical theories. This paper presents a comprehensive review of AI and ML applications across major domains of physical sciences, including physics, chemistry, materials science, astronomy, and earth sciences. Particular emphasis is placed on physics-informed machine learning approaches that integrate governing equations and physical constraints into data-driven models to enhance accuracy, interpretability, and generalization. Key challenges such as data quality, model explainability, and computational cost are discussed. The study highlights emerging trends including autonomous scientific discovery and hybrid theory?data approaches, underscoring the growing role of AI as a foundational tool for advancing modern physical science research. Artificial neural network ; Deep learning ; Key (lock) ; Big data ; Supervised learning ; Support vector machine ; Physical system ; Computational model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Physics and Python Applications
Authors: Yutang Ge | Yaning Cui | Hanzheng Li | Jun-Jie Wang | Fanjie Xu | Jinhan Dong | Yongqi Jin | Dongxu Cui | Peng Jin | Guojiang Zhao | Hengxing Cai | Rong Zhu | Linfeng Zhang | Xiaohong Ji | Zhifeng Gao
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

SpecXMaster Technical Report Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence. However, conventional expert-dependent spectral interpretation encounters substantial hurdles, including susceptibility to human bias and error, dependence on limited specialized expertise, and variability across interpreters. To address these challenges, we propose SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation. SpecXMaster enables automated extraction of multiplicity information from both 1H and 13C spectra directly from raw FID (free induction decay) data. This end-to-end pipeline enables fully automated interpretation of NMR spectra into chemical structures. It demonstrates superior performance across multiple public NMR interpretation benchmarks and has been refined through iterative evaluations by professional chemical spectroscopists. We believe that SpecXMaster, as a novel methodological paradigm for spectral interpretation, will have a profound impact on the organic chemistry community. Pipeline (software) ; Raw data ; Interpretation (philosophy) ; Bridge (graph theory) ; Nuclear magnetic resonance spectroscopy ; Replicate Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Zhe Zhao | Hai‐Hu Wen | Peng Wang | Nianping Liu | Zhiheng | Pengkun Wang | Bo An | Yang Wang
Source: Research Square (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

CogSynth: From Data Diagnosis to Production-Ready Scientific Software Executable ; Cognitive reframing ; Raw data ; Software ; Coding (social sciences) ; Security token ; Reduction (mathematics) ; Cognition Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Multi-Objective Optimization Algorithms
Authors: Vani Nigam | Chandrasekhar, Achuth | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

Polymer-Agent: Large Language Model Agent for Polymer Design On-demand Polymer discovery is essential for various industries, ranging from biomedical to reinforcement materials. Experiments with polymers have a long trial-and-error process, leading to use of extensive resources. For these processes, machine learning has accelerated scientific discovery at the property prediction and latent space search fronts. However, laboratory researchers cannot readily access codes and these models to extract individual structures and properties due to infrastructure limitations. We present a closed-loop polymer structure-property predictor integrated in a terminal for early-stage polymer discovery. The framework is powered by LLM reasoning to provide users with property prediction, property-guided polymer structure generation, and structure modification capabilities. The SMILES sequences are guided by the synthetic accessibility score and the synthetic complexity score (SC Score) to ensure that polymer generation is as close as possible to synthetically accessible monomer-level structures. This framework addresses the challenge of generating novel polymer structures for laboratory researchers, thereby providing computational insights into polymer research. Property (philosophy) ; Chemical space ; Reinforcement learning ; Space (punctuation) ; Polymer ; Structured prediction Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Polymer Synthesis and Characterization
Authors: Vani Nigam | Chandrasekhar, Achuth | Amir Barati Farimani
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Polymer-Agent: Large Language Model Agent for Polymer Design On-demand Polymer discovery is essential for various industries, ranging from biomedical to reinforcement materials. Experiments with polymers have a long trial-and-error process, leading to use of extensive resources. For these processes, machine learning has accelerated scientific discovery at the property prediction and latent space search fronts. However, laboratory researchers cannot readily access codes and these models to extract individual structures and properties due to infrastructure limitations. We present a closed-loop polymer structure-property predictor integrated in a terminal for early-stage polymer discovery. The framework is powered by LLM reasoning to provide users with property prediction, property-guided polymer structure generation, and structure modification capabilities. The SMILES sequences are guided by the synthetic accessibility score and the synthetic complexity score (SC Score) to ensure that polymer generation is as close as possible to synthetically accessible monomer-level structures. This framework addresses the challenge of generating novel polymer structures for laboratory researchers, thereby providing computational insights into polymer research. Property (philosophy) ; Chemical space ; Reinforcement learning ; Space (punctuation) ; Polymer ; Structured prediction Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Polymer Synthesis and Characterization
Authors: Ryan Shamim
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-03)
Field: Materials Science — Materials Chemistry

Autonomous Discovery of Low-Dimensional Physical Invariants from Real-World Data Modern machine learning systems excel at prediction but rarely explain why complex systems behave as they do. Scientific progress, however, depends on identifying low-dimensional organizing principles that compress messy observations into interpretable structure. This work presents a general computational architecture for autonomous discovery of physical invariants from real-world data. Rather than fitting target variables directly, the system searches for derived quantities that collapse heterogeneous system behaviors onto shared functional relationships. The objective isn't accuracy, but abstraction. The approach is validated across multiple settings, including synthetic physical systems with hidden dynamics and a real NASA lithium-ion battery aging dataset. In the battery domain, the system autonomously identifies a candidate thermodynamic efficiency invariant, constructed from delivered electrical energy and thermal rise, which collapses degradation trajectories across distinct cells without referencing capacity directly. The results demonstrate that autonomous abstraction and theory formation are tractable computational problems, distinct from regression, curve fitting, or black-box prediction. This repository serves as a methods-level proof of concept for machine-assisted scientific discovery, emphasizing explanation as compression rather than performance on predefined targets. The work is intentionally conservative in scope. It doesn't claim a complete theory of any physical domain, but instead illustrates how machines can discover organizing variables that humans traditionally identify through intuition, dimensional analysis, and long empirical study. Abstraction ; Physical system ; Scientific discovery ; Complex system ; Work (physics) ; Autonomous system (mathematics) ; Energy (signal processing) ; Abstraction layer ; Domain (mathematical analysis) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Gaussian Processes and Bayesian Inference
Authors: Jiawei Gong | Danqing Ma | Ralph Bulanadi | Moore, Robert Kennon;Mayo, Charles | Rama K. Vasudevan | Lei Zhao | Yongtao Liu
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting dataset diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with a dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral datasets. Dual-VAE embeds local structure and spectroscopic responses into a shared latent manifold that serves as a structure-property relationship map. We applied this framework for the investigation of halide perovskite films using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials. Representation (politics) ; Grain boundary ; Microscopy ; Nanoscopic scale ; Perovskite (structure) ; Kernel (algebra) ; Hysteresis ; Autoencoder Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Mengxue Sun | Yuhang Song | Zhiqi Chen | Lanze Xiao | Hairui Zhou | Jia Lin
Source: Small Methods (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning‐Driven R&amp;D of Perovskites and Spinels: From Traditional Models to Deep Learning The development of strategic materials such as spinels and perovskites is hampered by the long cycles and low efficiency of traditional trial-and-error methods, for which machine learning (ML) offers a disruptive data-driven paradigm. This review dissects the field's evolution from traditional machine learning (TML), reliant on manual feature engineering, to deep learning (DL) models capable of end-to-end autonomous feature extraction, examining their applications within forward screening and inverse design frameworks. We critically assess how DL overcomes TML's limitations by mapping atomic-level structures to macroscopic properties with greater precision, while also elucidating cutting-edge approaches to address the core challenge of high-quality data scarcity. However, we argue that fully realizing ML's potential requires shifting focus beyond predictive accuracy toward model reliability and robustness. In materials discovery and inverse design, the credibility of predictions depends on accurate uncertainty calibration. Integrating Bayesian learning and confidence-aware modeling can yield statistically reliable guidance for experiments and design decisions, enhancing the stability and trust of AI-assisted research. Future breakthroughs must pivot toward building standardized multi-modal databases, developing interpretable models that can unveil underlying physical mechanisms to overcome the "black-box" problem, and deeply integrating AI with automated experimental platforms to establish a closed-loop research ecosystem that truly accelerates scientific discovery. Credibility ; Feature (linguistics) ; Stability (learning theory) ; Reliability (semiconductor) ; Deep learning ; Focus (optics) ; Bayesian probability ; Core (optical fiber) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Catalysis and Oxidation Reactions