jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyang Yu | Qifeng Yang | Yiming Mo
Source: ACS electrochemistry. (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Opportunities of Large Language Models in the Development of Organic Electrosynthesis Organic electrosynthesis is resurging as a green, versatile platform for building molecules, yet its development pipeline, spanning across literature mining, electrocatalyst design, reaction discovery and optimization, mechanistic elucidation, and reactor scale-up, remains fragmented and labor-intensive. This Perspective discusses the potential of recently emerging large language models (LLMs) serving as orchestrators that fuse chemistry knowledge with tool use to streamline the development process of electrosynthesis: multimodal LLMs for structured information extraction of electrosynthesis conditions from text and figures; agentic workflows that coordinate reaction predictors and iterative closed-loop optimization for reaction development; mechanism elucidation that couples electroanalytical signals with stepwise hypotheses and reasoning; fusing cross-domain knowledge for heterogeneous electrocatalyst selection; and engineering and multifaceted design for process scale-up. Considering the interdisciplinary nature of organic electrosynthesis, we envision LLMs will function as a transparent decision-maker to work with human chemists to accelerate the discovery and development of new electrosynthesis methods. Electrosynthesis ; Process (computing) ; Workflow ; Electrocatalyst ; Perspective (graphical) ; Function (biology) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Radical Photochemical Reactions
Authors: Adedire D. Adesiji | Jiashuo Wang | Chin-Cheng Kuo | Keith A. Brown
Source: ArXiv.org (preprint, 2025-08-08)
Field: Materials Science — Materials Chemistry

Benchmarking Self-Driving Labs A key goal of modern materials science is accelerating the pace of materials discovery. Self-driving labs, or systems that select experiments using machine learning and then execute them using automation, are designed to fulfil this promise by performing experiments faster, more intelligently, more reliably, and with richer metadata than conventional means. This review summarizes progress in understanding the degree to which SDLs accelerate learning by quantifying how much they reduce the number of experiments required for a given goal. The review begins by summarizing the theory underlying two key metrics, namely acceleration factor AF and enhancement factor EF, which quantify how much faster and better an algorithm is relative to a reference strategy. Next, we provide a comprehensive review of the literature, which reveals a wide range of AFs with a median of 6, and that tends to increase with the dimensionality of the space, reflecting an interesting blessing of dimensionality. In contrast, reported EF values vary by over two orders of magnitude, although they consistently peak at 10-20 experiments per dimension. To understand these results, we perform a series of simulated Bayesian optimization campaigns that reveal how EF depends upon the statistical properties of the parameter space while AF depends on its complexity. Collectively, these results reinforce the motivation for using SDLs by revealing their value across a wide range of material parameter spaces and provide a common language for quantifying and understanding this acceleration. Benchmarking ; Curse of dimensionality ; Range (aeronautics) ; Pace ; Key (lock) ; Bayesian probability ; Bayesian optimization ; Factor (programming language) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Modular Robots and Swarm Intelligence
Authors: Alexey V. Gulyuk | Nahed Abu Zaid | Rada Chirkova | Yaroslava G. Yingling
Source: APL Machine Learning (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Data integration and data fusion approaches in self-driving labs: A perspective Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials. Data integration ; Interoperability ; Sensor fusion ; Big data ; Perspective (graphical) ; Data sharing ; System integration ; Data-driven Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Scientific Computing and Data Management
Authors: Simon A. Mason | Megna Shah | Jeff Simmons | Dennis M. Dimiduk | Stephen R. Niezgoda
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration Accelerating materials development requires quantitative linkages between processing, microstructure, and properties. In this work, we introduce a framework for mapping microstructure onto a low-dimensional material manifold that is parametrized by processing conditions. A key innovation is treating microstructure as a stochastic process, defined as a distribution of microstructural instances rather than a single image, enabling the extraction of material state descriptors that capture the essential process-dependent features. We leverage the manifold hypothesis to assert that microstructural outcomes lie on a low-dimensional latent space controlled by only a few parameters. Using phase-field simulations of spinodal decomposition as a model material system, we compare multiple microstructure descriptors (two-point statistics, chord-length distributions, and persistent homology) in terms of two criteria: (1) intrinsic dimensionality of the latent space, and (2) invertibility of the processing-to-structure mapping. The results demonstrate that distribution-based descriptors can recover a two-dimensional latent structure aligned with the true processing parameters, yielding an invertible and physically interpretable mapping between processing and microstructure. In contrast, descriptors that do not account for microstructure variability either overestimate dimensionality or lose predictive fidelity. The constructed material manifold is shown to be locally continuous, wherein small changes in process variables correspond to smooth changes in microstructure descriptors. This data-driven manifold mapping approach provides a quantitative foundation for microstructure-informed process design and paves the way toward closed-loop optimization of processing--structure--property relationships in an integrated materials engineering context. Curse of dimensionality ; Granularity ; Manifold (fluid mechanics) ; Leverage (statistics) ; Microstructure ; Invertible matrix ; Nonlinear dimensionality reduction ; Process (computing) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Block Copolymer Self-Assembly
Authors: He Zhang | Houjun Qi | Lingtao Weng | Weiguo Gao | Y. B. Zhao | Yanling Tian | Hengning Hu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Dynamic Contact Angle Modeling and Optimization of Superhydrophobic Surfaces Contact angle ; Materials science ; Composite material Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Tribology and Lubrication Engineering
Authors: Yi Han
Source: Highlights in Business Economics and Management (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Scientific Research: Opportunities, Limits, and the Engineering Realization of the Data-Driven Paradigm This paper presents a publication-oriented blueprint for using artificial intelligence in scientific research, written strictly in the format requested and developed as sustained paragraphs rather than bullet points. The central claim is that AI augments, rather than replaces, the scientific method when its use follows a sequence that prioritizes domain structure, leverages learning for acceleration, quantifies and calibrates uncertainty, and secures results through reproducibility and auditability. It articulate four research questions: where AI most productively intervenes within mathematics, physics, chemistry, and cross-disciplinary settings; how order-of-magnitude gains can be realized without violating conservation laws, symmetries, or boundary and initial conditions; how unified evaluation practices, calibration, out-of-distribution (OOD) testing, negative controls, independent verification, distinguish operational value from deceptively high scores; and how to operationalize the entire process as a closed loop connecting data, models, decisions, and new evidence. The analysis section develops, for each discipline, a coherent account of problem formulations, representative methods such as neural operators and physics-informed neural networks, evaluation practices that emphasize physical validity alongside error, characteristic failure modes and mitigations, and the rationale for combining learning with structures. The solutions section translates these ideas into engineering practice, describing structure encoding at architectural and objective levels, probabilistic reporting and calibration, robust OOD testing, closed-loop optimization with laboratory or simulation interfaces, and the governance elements that make findings portable, auditable, and energy-aware. The conclusion integrates these strands into design principles for research programs, courses, and deployments. Throughout, the prose is organized into continuous paragraphs under numbered headings, in line with academic style, while in-text citations follow the bracketed numbering convention. Operationalization ; Blueprint ; Process (computing) ; Domain (mathematical analysis) ; Probabilistic logic ; Realization (probability) ; Applications of artificial intelligence ; Similarity (geometry) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Scientific Computing and Data Management
Authors: Jingran Yang | Jingshen Zhang | Yu Chen | Diyun Chen | Minhua Su
Source: Journal of environmental chemical engineering (article, 2025-11-08)
Field: Chemistry — Inorganic Chemistry

Optimizing uranium removal: A machine learning and NSGA-II approach for balancing adsorption efficiency and operational cost Uranium ; Hyperparameter ; Decision tree ; Bayesian optimization ; Adsorption ; Gradient boosting ; Boosting (machine learning) Radioactive element chemistry and processing ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Vivek Chawla | Stephen Puplampu | Haochen Zhu | Philip D. Rack | Dayakar Penumadu | Sergei Kalinin
Source: ArXiv.org (preprint, 2025-11-21)
Field: Materials Science — Materials Chemistry

Accelerated Materials Discovery through Cost-Aware Bayesian Optimization of Real-World Indentation Workflows Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteroskedastic Gaussian-process modeling with cost-aware Bayesian optimization to dynamically select indentation locations and hold times, minimizing total testing time while preserving measurement accuracy. A detailed emulator and cost model capture the intrinsic penalties associated with lateral motion, drift stabilization, and reconfiguration-factors often neglected in conventional active-learning approaches. To prevent kernel-length-scale collapse caused by disparate time scales, a hierarchical meta-testing workflow combining local grid and global exploration is introduced. Implementation of the workflow is shown on a experimental Ta-Ti-Hf-Zr thin-film library. The proposed framework achieves nearly a thirty-fold improvement in property-mapping efficiency relative to grid-based indentation, demonstrating that incorporating cost and drift models into probabilistic planning substantially improves performance. This study establishes a generalizable strategy for optimizing experimental workflows in autonomous materials characterization and can be extended to other high-precision, drift-limited instruments. Workflow ; Probabilistic logic ; Indentation ; Bayesian optimization ; Bayesian probability ; Grid ; Characterization (materials science) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Metal and Thin Film Mechanics
Authors: Jingyun Yang (600556) | Ruoyan Avery Yin (22360744) | Chi Jiang (3765622) | Yuepeng Hu (16821003) | Xiaokai Zhu (15461038) | Xingjian Hu (11544985) | Sutharsika Kumar (22360747) | Samantha K. Holmes (22360750) | Xiao Wang (19312) | Xiaohua Zhai (22360753) | Keran Rong (22360756) | Yunyue Zhu (17460772) | Tianyi Zhang (87298) | Zongyou Yin (1395061) | Yuan Cao (150846) | Haoning Tang (1788652) | Aaron D. Franklin (1535173) | Jing Kong (531041) | Neil Zhenqiang Gong (17433315) | Zhichu Ren (12175709) | Haozhe Wang (287184)
Source: Figshare (article, 2025-10-02)
Field: Materials Science — Materials Chemistry

Zero-Shot AutonomousMicroscopy for Scalable and IntelligentCharacterization of 2D Materials Characterization of atomic-scale materials traditionally requires human experts with months to years of specialized training. Even for trained human operators, accurate and reliable characterization remains challenging when examining newly discovered materials such as two-dimensional (2D) structures. This bottleneck drives demand for fully autonomous experimentation systems capable of comprehending research objectives without requiring large training data sets. In this work, we present ATOMIC (Autonomous Technology for Optical Microscopy & Intelligent Characterization), an end-to-end framework that integrates foundation models to enable fully autonomous, zero-shot characterization of 2D materials. Our system integrates the vision foundation model (i.e., Segment Anything Model), large language models (i.e., ChatGPT), unsupervised clustering, and topological analysis to automate microscope control, sample scanning, image segmentation, and intelligent analysis through prompt engineering, eliminating the need for additional training. When analyzing typical MoS<sub>2</sub> samples, our approach achieves 99.7% segmentation accuracy for single layer identification, which is equivalent to that of human experts. In addition, the integrated model is able to detect grain boundary slits that are challenging to identify with human eyes. Furthermore, the system retains robust accuracy despite variable conditions, including defocus, color-temperature fluctuations, and exposure variations. It is applicable to a broad spectrum of common 2D materialsincluding graphene, MoS<sub>2</sub>, WSe<sub>2</sub>, SnSeregardless of whether they were fabricated via top-down or bottom-up methods. This work represents the implementation of foundation models to achieve autonomous analysis, providing a scalable and data-efficient characterization paradigm that transforms the approach to nanoscale materials research. Characterization (materials science) ; Bottleneck ; Scalability ; Segmentation ; Robustness (evolution) ; Automation ; Foundation (evidence) ; Interfacing 2D Materials and Applications ; Advanced Electron Microscopy Techniques and Applications ; Graphene research and applications
Authors: Shawn Ray
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Generative Metascience: A Review of AI as the Next Scientific Instrument and the Emerging Paradigm of Algorithmic Discovery This paper introduces Generative Metascience, a comprehensive framework for understanding how artificial intelligence (AI) transforms scientific discovery. We synthesize historical milestones and case studies across genomics, astronomy, materials science, and social sciences to illustrate AI’s evolution from a research instrument into an autonomous co-investigator. Our analysis is structured around two core themes: AI-enabled data collection and analysis, and AI-driven hypothesis generation and testing. This dual focus highlights the iterative interplay between data-driven analytics and hypothesis-driven inquiry, showing how AI tools can simultaneously generate and evaluate scientific hypotheses. Key insights reveal AI-accelerated breakthroughs, such as automated protein-folding and materials design, and the rise of self-driving laboratories, which signal a shift from traditional inquiry toward an algorithmic discovery paradigm. By synthesizing data-driven pattern recognition with AI-facilitated hypothesis generation across disciplines, this paper addresses a critical research gap, showing that AI has begun to automate hypothesis formation and serves as a meta-technology that is redefining scientific epistemology. We highlight urgent implications for future research, including the development of hybrid AI-human workflows, robust metrics for machine-generated insights, and frameworks for addressing the societal consequences of AI-driven science, such as algorithmic bias and accountability. For policy, we articulate the need for greater transparency, open data standards, and interdisciplinary funding initiatives. For practice, we advocate for retraining researchers and updating curricula for AI-integrated labs. By articulating these developments and their contributions, this paper charts a roadmap for responsibly harnessing AI’s potential and guiding the scientific community as it navigates AI’s evolving role in a new era of discovery. Generative grammar ; Analytics ; Key (lock) ; Data collection ; Retraining ; Big data ; Generative model ; Focus (optics) ; Scientific discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Shawn Ray
Source: SHILAP Revista de lepidopterología (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Generative Metascience: A Review of AI as the Next Scientific Instrument and the Emerging Paradigm of Algorithmic Discovery This paper introduces Generative Metascience, a comprehensive framework for understanding how artificial intelligence (AI) transforms scientific discovery. We synthesize historical milestones and case studies across genomics, astronomy, materials science, and social sciences to illustrate AI’s evolution from a research instrument into an autonomous co-investigator. Our analysis is structured around two core themes: AI-enabled data collection and analysis, and AI-driven hypothesis generation and testing. This dual focus highlights the iterative interplay between data-driven analytics and hypothesis-driven inquiry, showing how AI tools can simultaneously generate and evaluate scientific hypotheses. Key insights reveal AI-accelerated breakthroughs, such as automated protein-folding and materials design, and the rise of self-driving laboratories, which signal a shift from traditional inquiry toward an algorithmic discovery paradigm. By synthesizing data-driven pattern recognition with AI-facilitated hypothesis generation across disciplines, this paper addresses a critical research gap, showing that AI has begun to automate hypothesis formation and serves as a meta-technology that is redefining scientific epistemology. We highlight urgent implications for future research, including the development of hybrid AI-human workflows, robust metrics for machine-generated insights, and frameworks for addressing the societal consequences of AI-driven science, such as algorithmic bias and accountability. For policy, we articulate the need for greater transparency, open data standards, and interdisciplinary funding initiatives. For practice, we advocate for retraining researchers and updating curricula for AI-integrated labs. By articulating these developments and their contributions, this paper charts a roadmap for responsibly harnessing AI’s potential and guiding the scientific community as it navigates AI’s evolving role in a new era of discovery. Generative grammar ; Analytics ; Key (lock) ; Data collection ; Retraining ; Big data ; Generative model ; Focus (optics) ; Scientific discovery Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Thomas M. Deucher | Juan C. Verduzco | Michael S. Titus | Alejandro Strachan
Source: ArXiv.org (preprint, 2025-06-24)
Field: Materials Science — Materials Chemistry

A collaborative digital twin built on FAIR data and compute infrastructure The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by findable, accessible, interoperable, and reusable (FAIR) data infrastructure, SDLs with overlapping interests can collaborate more effectively. This work presents a distributed SDL implementation built on nanoHUB services for online simulation and FAIR data management. In this framework, geographically dispersed collaborators conducting independent optimization tasks contribute raw experimental data to a shared central database. These researchers can then benefit from analysis tools and machine learning models that automatically update as additional data become available. New data points are submitted through a simple web interface and automatically processed using a nanoHUB Sim2L, which extracts derived quantities and indexes all inputs and outputs in a FAIR data repository called ResultsDB. A separate nanoHUB workflow enables sequential optimization using active learning, where researchers define the optimization objective, and machine learning models are trained on-the-fly with all existing data, guiding the selection of future experiments. Inspired by the concept of ``frugal twin", the optimization task seeks to find the optimal recipe to combine food dyes to achieve the desired target color. With easily accessible and inexpensive materials, researchers and students can set up their own experiments, share data with collaborators, and explore the combination of FAIR data, predictive ML models, and sequential optimization. The tools introduced are generally applicable and can easily be extended to other optimization problems. Workflow ; Task (project management) ; Set (abstract data type) ; Raw data ; Interface (matter) ; Active learning (machine learning) ; Data set ; Optimization problem ; Selection (genetic algorithm) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Experimental Learning in Engineering