jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zaid Abulawi | Zavier Ndum Ndum | Eric Cervi | Rui Hu | Yang Liu
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

AutoSAM: an Agentic Framework for Automating Input File Generation for the SAM Code with Multi-Modal Retrieval-Augmented Generation In the design and safety analysis of advanced reactor systems, constructing input files for system-level thermal-hydraulics codes such as the System Analysis Module (SAM) remains a labor-intensive task. Analysts must extract and reconcile design data from heterogeneous engineering documents and manually translate it into solver-specific syntax. In this paper, we present AutoSAM, an agentic framework that automates SAM input file generation. The framework combines a large language model agent with retrieval-augmented generation over the solver's user guide and theory manual, together with specialized tools for analyzing PDFs, images, spreadsheets, and text files. AutoSAM ingests unstructured engineering documents, including system diagrams, design reports, and data tables, extracts simulation-relevant parameters into a human-auditable intermediate representation, and synthesizes validated, solver-compatible input decks. Its multimodal retrieval pipeline integrates scientific text extraction, vision-based figure interpretation, semantic embedding, and query answering. We evaluate AutoSAM on four case studies of increasing complexity: a single-pipe steady-state model, a solid-fuel channel with temperature reactivity feedback, the Advanced Burner Test Reactor core, and the Molten Salt Reactor Experiment primary loop. Across all cases, the agent produces runnable SAM models consistent with expected thermal-hydraulic behavior while explicitly identifying missing data and labeling assumed values. The framework achieves 100% utilization of structured inputs, about 88% extraction from PDF text, and 100% completeness in vision-based geometric extraction. These results demonstrate a practical path toward prompt-driven reactor modeling, in which analysts provide system descriptions and supporting documentation while the agent translates them into transparent, and executable, SAM simulations. Completeness (order theory) ; Code (set theory) ; Pipeline (software) ; Documentation ; File system ; Data file ; Source code ; Code generation Machine Learning in Materials Science ; Topic Modeling ; Multi-Agent Systems and Negotiation
Authors: Zhuo Diao | Kouma Matsumoto | Linfeng Hou | Hayato Yamashita | Masayuki Abe
Source: arXiv (Cornell University) (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Autonomous Laboratory Agent via Customized Domain-Specific Language Model and Modular AI Interface We introduce a system architecture that addresses a fundamental challenge in deploying language-model agents for autonomous control of scientific instrumentation: ensuring reliability in safety-critical environments. The framework integrates probabilistic reasoning by domain-specialized language models with deterministic execution layers that enforce constraints through structured validation and modular orchestration. By separating intent interpretation, experimental planning, and command verification, the architecture translates high-level scientific goals into verifiable experimental actions. We demonstrate this approach in real-time atomic-resolution scanning probe microscopy experiments operated at room temperature, where the system autonomously generates control strategies, invokes corrective modules, and maintains stable operation under experimentally challenging conditions. Quantitative evaluations show that domain-adapted small language models achieve high routing robustness and command accuracy while operating on consumer-grade hardware. Beyond a specific instrument, the framework establishes a general computational principle for deploying language-model agents in safety-critical experimental workflows, providing a pathway toward scalable autonomous laboratories. Modular design ; Verifiable secret sharing ; Robustness (evolution) ; Scalability ; Probabilistic logic ; Interface (matter) ; Architecture ; Language model Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Scientific Computing and Data Management
Authors: Yuyang Liu | Jingya Wang | Liuzhenghao Lv | Yonghong Tian
Source: ArXiv.org (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these irreversible environments, probabilistic hallucinations are not merely incorrect, but also cause equipment damage or experimental failure. To address this, we propose \textbf{BioProAgent}, a neuro-symbolic framework that anchors probabilistic planning in a deterministic Finite State Machine (FSM). We introduce a State-Augmented Planning mechanism that enforces a rigorous \textit{Design-Verify-Rectify} workflow, ensuring hardware compliance before execution. Furthermore, we address the context bottleneck inherent in complex device schemas by \textit{Semantic Symbol Grounding}, reducing token consumption by $\sim$6$\times$ through symbolic abstraction. In the extended BioProBench benchmark, BioProAgent achieves 95.6\% physical compliance (compared to 21.0\% for ReAct), demonstrating that neuro-symbolic constraints are essential for reliable autonomy in irreversible physical environments. \footnote{Code at https://github.com/YuyangSunshine/bioproagent and project at https://yuyangsunshine.github.io/BioPro-Project/} Probabilistic logic ; Bridge (graph theory) ; Context (archaeology) ; Bottleneck ; Ground ; Security token ; Key (lock) ; Symbol (formal) Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Adversarial Robustness in Machine Learning
Authors: Giuseppe Sancataldo
Source: Applied Sciences (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

ChatMicroscopy: A Perspective Review of Large Language Models for Next-Generation Optical Microscopy Optical microscopy is a fundamental tool in the physical, chemical, and life sciences, enabling direct investigation of structure, dynamics, and function across multiple spatial and temporal scales. Advances in optical design, detectors, and computational techniques have greatly enhanced performance, but have also increased the complexity of modern microscopes, which are now software-driven and embedded in data-intensive workflows. Artificial intelligence has become an important component of this landscape, particularly through task-specific machine learning approaches for image analysis, optimization, and limited instrument control. While effective, these solutions are often fragmented and lack the ability to integrate experimental intent, contextual knowledge, and multi-step reasoning. Recent progress in large language models (LLMs) offers a new paradigm for intelligent microscopy. As foundation models trained on large-scale text and code, LLMs exhibit emergent capabilities in reasoning, abstraction, and tool coordination, allowing them to act as natural interfaces between users and complex experimental systems. This perspective highlights how LLMs can function as cognitive and orchestration layers that connect experiment design, instrument control, data analysis, and knowledge integration. Emerging applications include conversational microscope control, workflow supervision, and scientific assistance for data exploration and hypothesis generation, alongside important technical, ethical, and governance challenges. Perspective (graphical) ; Orchestration ; Function (biology) ; Workflow ; Component (thermodynamics) ; Computational model Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Electron Microscopy Techniques and Applications
Authors: Yanjin Xiang | Yihan Nie | Yunzhi Gao | Haidi Wang | Wei Hu
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials Universal machine learning interatomic potentials (UMLIPs) offer accuracy close to first-principles calculations at a fraction of the cost, showing significant potential for large-scale material simulations. However, the fragmented UMLIPs ecosystem lacks unified interface standards and integration frameworks, hindering their automated deployment in high-throughput workflows. To address this, we present UniMatSim, a modular Python framework. It systematically integrates various UMLIPs (e.g., CHGNet, M3GNet, MACE) and automates workflows from structural optimization to stability verification. The framework enables seamless model switching via abstracted interfaces, incorporates task orchestration, and provides standardized modules for key properties (elasticity, phonons, molecular dynamics), including automated handling for low-dimensional materials. As a test case, using the 2D Lieb lattice system, we constructed a multi-stage high-throughput screening workflow covering structural optimization, elastic stability, and phonon spectrum calculations. Starting from 1,176 candidate compositions, a four-model consensus pipeline yields 393 stable structures. These are refined by magnetic-state screening and DFT band-structure calculations to 59 Lieb-lattice candidates with staggered-magnetic-band characteristics. Results show UniMatSim significantly improves computational efficiency and reproducibility, providing a reliable infrastructure for data-driven materials discovery and design. Workflow ; Python (programming language) ; Automation ; Modular design ; Interface (matter) ; Key (lock) ; Software ; Software deployment Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Shenshen Li | Shufang Chen | Juanjuan Bai
Source: International Journal of Reasoning-based Intelligent Systems (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Agent-driven multi-scale simulation for predicting the catalytic activity of complexes This paper presents an agent-driven multi-scale simulation framework for efficiently and accurately predicting the catalytic activity of complexes.This framework constructs the reaction path search as a Markov decision process, adopts hierarchical reinforcement learning agents to actively explore the potential energy surface, and combines the equivariant graph neural network potential function to ensure quantum accuracy.Experiments on the open catalyst project (OC20) dataset show that the average absolute error of this framework in adsorption energy prediction is significantly reduced to 0.291 eV, the force prediction error is 0.072 eV/, and it can converge to a stable configuration in an average of only 18.3 steps.It is superior to the existing mainstream methods in both accuracy and efficiency.This research provides a new paradigm of intelligent computing for catalyst design and promotes the development of multi-scale simulation towards autonomous decision making and efficient exploration. Catalysis ; Yield (engineering) ; Work (physics) ; Component (thermodynamics) ; Process (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Theoretical and Computational Physics
Authors: Sergey Gusarov | Svetlana Sapelnikova | Julio J. Valdés | Anguang Hu | Stanislav R. Stoyanov
Source: Preprints.org (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Integrating the Theory of Inventive Problem Solving with Large Language Models: Enhancing Reasoning for Innovation in Materials Science at the Molecular Scale The Theory of Inventive Problem Solving (TRIZ) has long been a cornerstone for systematic innovation in engineering domains, including chemical and materials science. This paper proposes a novel framework that integrates TRIZ principles with large language models (LLMs) to emulate researcher-like reasoning in atomistic materials science. By structuring LLM prompts around TRIZ tools—such as patterns of evolution, contradiction matrices, and inventive principles—we enable models to identify problems, frame contradictions, and generate inventive solutions for challenges like data scarcity, poor interpretability, and unphysical predictions in quantum-chemical simulations and machine learning (ML) models. Drawing on recent artificial intelligence-TRIZ hybrids, like AutoTRIZ and TRIZ-GPT (generative pre-trained transformer), we demonstrate applications in molecular design, such as resolving contradictions in shape-memory polymers. This approach not only amplifies current trends in physics-informed ML and generative design but also democratizes advanced problem-solving, accelerating discoveries toward ideality TRIZ ; Cornerstone ; Structuring ; Frame (networking) ; Generative grammar Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Jiaxuan Lu | Ziyu Kong | Yemin Wang | Rong Fu | Haiyuan Wan | Cheng Yang | Wenjie Lou | Haoran Sun | Lilong Wang | Yankai Jiang | Xiaosong Wang | Xiao Sun | Dongzhan Zhou
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on static, pre-defined tool libraries, a paradigm that fundamentally fails in scientific domains where tools are sparse, heterogeneous, and intrinsically incomplete. In this paper, we propose Test-Time Tool Evolution (TTE), a new paradigm that enables agents to synthesize, verify, and evolve executable tools during inference. By transforming tools from fixed resources into problem-driven artifacts, TTE overcomes the rigidity and long-tail limitations of static tool libraries. To facilitate rigorous evaluation, we introduce SciEvo, a benchmark comprising 1,590 scientific reasoning tasks supported by 925 automatically evolved tools. Extensive experiments show that TTE achieves state-of-the-art performance in both accuracy and tool efficiency, while enabling effective cross-domain adaptation of computational tools. The code and benchmark have been released at https://github.com/lujiaxuan0520/Test-Time-Tool-Evol. Executable ; Benchmark (surveying) ; Scientific discovery ; Scientific reasoning ; Automated reasoning ; Adaptation (eye) ; Computational model Machine Learning in Materials Science ; Topic Modeling ; Scientific Computing and Data Management
Authors: Guannan Tang | Noah H. Paulson
Source: Research Square (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

An LLM-Agentic Workflow for Data-Driven Modeling: From Toy Image Reconstruction to Thermodynamic Modeling Workflow ; Identification (biology) ; Weighting ; Task (project management) ; Event (particle physics) ; CALPHAD Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Solidification and crystal growth phenomena
Authors: Vineeth Venugopal | Mahjoubi, Soroush | Elsa A. Olivetti
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions Large language models are increasingly applied to materials science, yet fundamental questions remain about their reliability and knowledge encoding. Evaluating 25 LLMs across four materials science tasks -- over 200 base and fine-tuned configurations -- we find that output modality fundamentally determines model behavior. For symbolic tasks, fine-tuning converges to consistent, verifiable answers with reduced response entropy, while for numerical tasks, fine-tuning improves prediction accuracy but models remain inconsistent across repeated inference runs, limiting their reliability as quantitative predictors. For numerical regression, we find that better performance can be obtained by extracting embeddings directly from intermediate transformer layers than from model text output, revealing an ``LLM head bottleneck,'' though this effect is property- and dataset-dependent. Finally, we present a longitudinal study of GPT model performance in materials science, tracking four models over 18 months and observing 9--43\% performance variation that poses reproducibility challenges for scientific applications. Inference ; Reliability (semiconductor) ; Limiting ; Knowledge base ; Verifiable secret sharing ; Base (topology) ; Generality ; Uncertainty quantification Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Text Readability and Simplification
Authors: Yinpeng Liu | Congrui Wang | Jiawei Liu | Xiang Shi | Yong Huang | Qimin Cheng | Lingfei Wei
Source: Scientific Data (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

A multimodal dataset of causal mechanisms in materials science literature Understanding how processing, structure, properties, and performance interact is essential for guiding materials design and discovery. Yet, causal mechanisms linking these elements are typically scattered across text, figures, and references in the literature, and efforts to systematically mine and organize such knowledge remain limited. In this work, we leverage an LLM-based mechanism deduction framework to construct a dataset of 207,200 fine-grained mechanisms with 1,113,940 multimodal evidences from 61,766 materials science research articles. Each mechanism is linked to a specific causal relation among the tetrahedral elements and is supported by evidence from experiment information, characterization results, and external knowledge, with its accuracy verified by materials science researchers. This dataset provides a large-scale, cross-validated collection of multimodal mechanism knowledge in materials science, serving as a resource for data-driven research and intelligent analysis. Mechanism (biology) ; Leverage (statistics) ; Construct (python library) ; Resource (disambiguation) ; Relation (database) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Haowei Hua | Chen Liang | Ding Pan | King, Irwin | Liu, Shengchao | Koji Tsuda | Wanyu Lin
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $α_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials. Equivariant map ; Dielectric ; Scalability ; Artificial neural network ; Nonlinear system ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Junkai Zhang | Wenye Xu | Shiwei Yang | Yong Cui
Source: Chinese Journal of Chemistry (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Development of Machine Learning in Organic Photovoltaic Cells <sup>†</sup> Comprehensive Summary Organic photovoltaic (OPV) cells are a key part of next‐generation flexible optoelectronic technologies, offering lightweight, environmentally friendly, and printable energy solutions based on renewable resources. With the development of new donor–acceptor materials and advances in device design, the power conversion efficiency of OPV cells has now surpassed 21% (the highest certified value of 20.80%). However, optimizing these complex, multi‐component active layers using traditional experimental approaches is slow and inefficient due to the enormous chemical space and the need to balance both morphological and electronic properties. In this context, machine learning (ML) has emerged as a powerful tool for handling complex data and modeling nonlinear relationships in OPV research. This review provides a concise overview of recent ML applications in the OPV field. We focus on its role in accelerating material discovery by rapidly screening large material libraries and identifying promising donor–acceptor combinations with suitable energy level alignment. We also highlight ML‐based performance prediction, which enables the estimation of device efficiency and stability before synthesis and fabrication. In addition, the integration of ML with automated experimental platforms is discussed, enabling high‐throughput optimization of processing conditions and supporting future large‐scale production. Although challenges such as limited data and model interpretability remain, the continued integration of machine learning with advanced experimental techniques is expected to significantly accelerate OPV development and promote the transition of organic photovoltaics from laboratory research to practical applications. Key Scientists Interpretability ; Photovoltaic system ; Organic solar cell ; Photovoltaics ; Renewable energy ; Key (lock) ; Efficient energy use ; Energy transformation Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications