jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ocean Cheung | Shun Tokuda | Damian Jędrzejowski | Evelyn Ploetz | Bettina Baumgartner | Marzena Pander | Faqing Yang | Jack D. Evans | Romy Ettlinger | Stefan Wuttke | Dariusz Matoga
Source: ChemRxiv (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Material Preparation Information File (MPIF): A Community-Driven Standard for Reporting MOF Syntheses The rapid growth of research on metal–organic frameworks (MOFs) and related porous materials has highlighted a critical need for standardized reporting of synthetic procedures and material properties. Reproducibility is often compromised by incomplete protocols and inconsistent nomenclature, with even well-established MOFs exhibiting substantial variability depending on subtle and frequently unreported factors. To address this critical challenge, we introduce the Material Preparation Information File (MPIF), a modular, machine- and human-readable format for documenting synthesis protocols alongside key characterization data. Built on the (Self-defining Text Archive and Retrieval (STAR) file architecture, MPIF consolidates essential informations—including reagents, conditions, equipment, and handling procedures—into a structured format that supports both digital processing and practical laboratory use. To streamline MPIF creation and editing a web-based interface has been developed (mpif.jackdevans.com). By aligning with existing standards such as CIF and AIF, MPIF enables integration with databases, promotes FAIR data principles, and provides a foundation for data-driven materials discovery, informatics, and automation. We envision MPIF becoming a community-wide standard for transparent and reproducible reporting of MOF and related material syntheses. Key (lock) ; Interface (matter) ; File format ; Characterization (materials science) ; Information Dissemination ; Materials processing ; Data collection Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Machine Learning in Materials Science
Authors: Dilshod Nematov | Iskandar Raufov | Saidjaafar Murodzoda | Anushervon Ashurov | Sakhidod Sattorzoda
Source: Journal of Modern Nanotechnology (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

The Bright Future of Materials Science with AI: Self-Driving Laboratories and Closed-Loop Discovery Next-generation autonomous laboratories that combine machine learning (ML), robotic synthesis, and in situ characterization is rapidly emerging as one of the key directions in modern materials science. By integrating active and deep learning algorithms with multi-level datasets ranging from quantum mechanical simulations to high-throughput flow-based experiments, these platforms establish closed-loop frameworks that guide complex nanofabrication processes in real time. Such AI-driven systems lower material and energy consumption, enable the precise design of nanostructures with tailored optical, electronic, and mechanical properties, and create research opportunities that were previously difficult to realize. This review provides a comprehensive overview of self-driving laboratories (SDLs), including their architectures, algorithmic strategies, and practical demonstrations such as the optimization of perovskite nanostructures, the development of nanoparticles for targeted drug delivery, and the synthesis of quantum dots with controlled emission. It also discusses existing methodological limitations, the need for standardized data practices, and challenges related to the integration of in situ techniques, while highlighting the prospects for creating fully digital pipelines for material production, as evidenced by recent autonomous laboratory demonstrations that progressed from initial hypotheses to functional prototypes within remarkably short timeframes. Furthermore, the self-driving lab paradigm is beginning to transition from academic laboratories into industrial research and development. This emerging trend suggests that SDL techniques will soon accelerate discovery and development in sectors such as pharmaceutical research and chemical manufacturing. Key (lock) ; Characterization (materials science) ; Open research ; Energy (signal processing) ; Workflow ; Field (mathematics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nanowire Synthesis and Applications
Authors: W.D. Jung | Keuntae Cho
Source: Actuators (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Deep Learning and NLP-Based Trend Analysis in Actuators and Power Electronics Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies. Artificial intelligence ; Deep learning ; Natural language processing ; Computer science ; Electronics ; Power electronics ; Power (physics) ; Engineering ; Electrical engineering ; Physics Machine Learning in Materials Science ; Robot Manipulation and Learning ; Advanced Sensor and Energy Harvesting Materials
Authors: Nicholas T. Runcie | Charlotte M. Deane | Fergus Imrie
Source: ArXiv.org (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Assessing the Chemical Intelligence of Large Language Models Large Language Models are versatile, general-purpose tools with a wide range of applications. Recently, the advent of "reasoning models" has led to substantial improvements in their abilities in advanced problem-solving domains such as mathematics and software engineering. In this work, we assessed the ability of reasoning models to perform chemistry tasks directly, without any assistance from external tools. We created a novel benchmark, called ChemIQ, consisting of 816 questions assessing core concepts in organic chemistry, focused on molecular comprehension and chemical reasoning. Unlike previous benchmarks, which primarily use multiple choice formats, our approach requires models to construct short-answer responses, more closely reflecting real-world applications. The reasoning models, OpenAI's o3-mini, Google's Gemini Pro 2.5, and DeepSeek R1, answered 50%-57% of questions correctly in the highest reasoning modes, with higher reasoning levels significantly increasing performance on all tasks. These models substantially outperformed the non-reasoning models which achieved only 3%-7% accuracy. We found that Large Language Models can now convert SMILES strings to IUPAC names, a task earlier models were unable to perform. Additionally, we show that the latest reasoning models can elucidate structures from 1D and 2D 1H and 13C NMR data, with Gemini Pro 2.5 correctly generating SMILES strings for around 90% of molecules containing up to 10 heavy atoms, and in one case solving a structure comprising 25 heavy atoms. For each task, we found evidence that the reasoning process mirrors that of a human chemist. Our results demonstrate that the latest reasoning models can, in some cases, perform advanced chemical reasoning. Comprehension ; Construct (python library) ; Verbal reasoning ; Task (project management) ; Process (computing) ; Qualitative reasoning ; Automated reasoning ; Range (aeronautics) ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Diego A. Gómez‐Gualdrón | Tatiane Gercina de Vilas | Katherine Ardila | Jair Fernando Fajardo-Rojas | Alexander J. Pak
Source: Materials Horizons (article, 2025-12-04)
Field: Chemistry — Inorganic Chemistry

Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective This review critically examines work at the intersection of machine learning (ML) and metal-organic frameworks (MOFs). The modular nature of MOFs enables immense design flexibility and applicability to a wide range of applications. However, the combinatorially large design space also stresses the resource-intensive nature of traditional high-throughput screening approaches. Due to the increasing availability of data in the form of experimental and hypothetical MOF structures and their properties, ML methods have emerged as a promising solution to accelerate MOF discovery, yet successful application of these methods will require strategies that maximize data and resource efficiency. This work surveys approaches to reduce data and resource burdens for MOF property prediction and design through feature engineering, model architecture choices, transfer learning, active learning, and generative models. We also discuss challenges related to data quality and scalability, as well as future opportunities for ML-empowered methods that, up to this point, have primarily focused on MOF adsorption properties. By focusing on efficiency at every stage (from data generation to model inference), we identify future pathways for making ML-aided MOF design more robust and accessible to both theorists and experimentalists alike. Modular design ; Flexibility (engineering) ; Intersection (aeronautics) ; Resource (disambiguation) ; Resource efficiency ; Perspective (graphical) ; Architecture ; Generative grammar Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Lin Yang | Jim Gaffney
Source: Qeios (preprint, 2025-04-30)
Field: Chemistry — Electrochemistry

Development of an Uncertainty-Aware Equation of State for Gold This study introduces a framework that employs Gaussian Processes (GPs) to develop high-fidelity equation of state (EOS) tables, essential for modeling material properties across varying temperatures and pressures. GPs offer a robust predictive modeling approach and are especially adept at handling uncertainties systematically. By integrating Error-in-Variables (EIV) into the GP model, we adeptly navigate uncertainties in both input parameters (like temperature and density) and output variables (including pressure and other thermodynamic properties). Our methodology is demonstrated using first-principles density functional theory (DFT) data for gold, observing its properties over maximum density compression (up to 100 g/cc) and extreme temperatures within the warm dense matter region (reaching 300 eV). Furthermore, we assess the resilience of our uncertainty propagation within the resultant EOS tables under various conditions, including data scarcity and the intrinsic noise of experiments and simulations. State (computer science) ; Equation of state ; Applied mathematics ; Computer science ; Mathematics ; Mathematical economics ; Physics ; Thermodynamics ; Algorithm Electrochemical Analysis and Applications ; Scientific Measurement and Uncertainty Evaluation ; Radioactive Decay and Measurement Techniques
Authors: Yi Huang | Bowen Zheng | Yunxi Dong | Hong Tang | Huan Zhao | Rakibul Hasan Shawon | Sensong An | Hualiang Zhang
Source: Nanophotonics (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MCP‐enabled LLM for meta‐optics inverse design: leveraging differentiable solver without LLM expertise Automatic differentiation (AD) enables powerful metasurface inverse design but requires extensive theoretical and programming expertise. We present a Model Context Protocol (MCP) assisted framework that allows researchers to conduct inverse design with differentiable solvers through large language models (LLMs). Since LLMs inherently lack knowledge of specialized solvers, our proposed solution provides dynamic access to verified code templates and comprehensive documentation through dedicated servers. The LLM autonomously accesses these resources to generate complete inverse design codes without prescribed coordination rules. Evaluation on the Huygens meta-atom design task with the differentiable TorchRDIT solver shows that while both natural language and structured prompting strategies achieve high success rates, structured prompting significantly outperforms in design quality, workflow efficiency, computational cost, and error reduction. The minimalist server design, using only 5 APIs, demonstrates how MCP makes sophisticated computational tools accessible to researchers without programming expertise, offering a generalizable integration solution for other scientific tasks. Workflow ; Solver ; Differentiable function ; Context (archaeology) ; Protocol (science) ; Inverse ; Documentation ; Task (project management) ; Template Metamaterials and Metasurfaces Applications ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Haijuan Wu | Zhicheng Lin | Chao Tan | Guohua Hu | Siyuan Luo | Zegao Wang
Source: Advanced Functional Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Engineering Symmetry Breaking in Twisted ReS <sub>2</sub> Homojunction for Polarization‐Enhanced Visual Intelligence Abstract Due to the non‐complementarity of extra‐nuclear electrons, ReS 2 shows anisotropic optical property and deliver polarization‐sensitive photodetection without using complex optical components. However, the low polarization sensitivity does not satisfy to accurate identification. Herein, by employing the symmetry breaking via twisting atomic configuration, the anisotropic optical and photoresponse is improved from both the intralayer and interlayer. By twisting the ReS 2 layers, the dark current decreases by three‐orders to ≈10 pA caused by the interlayer van der Waals gap. Moreover, twisting engineering not only suppress the dark current but also improve the in‐plane optical anisotropy. The optical anisotropic factors of 60°‐twisted ReS 2 and 90°‐twisted ReS 2 are 2.5 and 6.4, respectively, which indicate that the twist angle can alter the relative orientation of the Re‐Re chains and the interface coupling intensity. Furthermore, the device exhibits polarization sensitivity, that the 90°‐twisted ReS 2 exhibited well polarization ratio of 26.8 which is much larger than the value of 5.9 in 60°‐twisted ReS 2 . Moreover, the 90°‐twisted ReS 2 homojunctions as polarization‐sensitive photodetectors in complex visual recognition tasks is validated demonstrating that the captured polarization information significantly enhances image recognition efficiency (98.6%), confirming its practical application potential in artificial intelligence visual perception tasks. Polarization (electrochemistry) ; Anisotropy ; Symmetry breaking ; Photodetection ; Homojunction ; Photodetector ; Dark current ; Numerical aperture 2D Materials and Applications ; Metamaterials and Metasurfaces Applications ; Topological Materials and Phenomena
Authors: Ben Ikenson
Source: Scilight (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

The paradigm-shifting potential of AI in materials science Autonomous microscopy promises path towards improved materials characterization – and unprecedented advances in physics Paradigm shift ; Computer science ; Cognitive science ; Data science ; Engineering ethics ; Epistemology ; Psychology ; Engineering ; Philosophy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Janet E. Joy | Andrew I. Su
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-02)
Field: Materials Science — Materials Chemistry

Federated Knowledge Retrieval Elevates Large Language Model Performance on Biomedical Benchmarks Federated knowledge retrieval provides transparent improvements in accuracy for large language models, establishing BTE-RAG as a valuable and practical tool for mechanistic exploration and translational biomedical research. Computer science ; Language model ; Information retrieval ; Natural language processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Patrick M. Blies | Sophia Keller | Ulrich Kuenzer | Yassine El Manyari | Franz Maier | Markus G. R. Sause | Marcel Wasserer | R. Hinterhölzl
Source: Journal of Manufacturing Processes (article, 2025-03-06)
Field: Materials Science — Polymers and Plastics

Towards the automation of woven fabric draping via reinforcement learning and Extended Position Based Dynamics The draping process in the preforming stage of composite manufacturing is very cost- and time-expensive and requires substantial manual labor. One strategy towards automation is the use of collaborative robots. Recent advances in AI have made it possible to train robots on difficult real-world tasks with reinforcement learning. However, training a robot using reinforcement learning is practically challenging and leveraging simulation is often the only option to use reinforcement learning in real-world settings at all. Existing FE models, which are commonly used for optimization of preforming processes, are too slow for reinforcement learning training. We addressed this issue by developing an XPBD-based surrogate model, drastically reducing simulation times compared to a classic FE model. With the achieved speedup, the training of a reinforcement learning agent became feasible and a draping trajectory could successfully be transferred to a real-world cobot, demonstrating the potential and capabilities of this innovative approach. • Created a fully parametrized FE digital twin for a dry glass fiber fabric. • Developed PBD-based surrogate for FE model that accelerates simulation by 3 orders of magnitude. • Trained RL agent that can drape a wrinkle with the help of the surrogate simulation. • Transferred AI-optimized path to real-world cobot and confirmed simulation outcome. • Created a test rig for draping the dry fabric with a cobot. Materials science ; Reinforcement learning ; Position (finance) ; Automation ; Reinforcement ; Dynamics (music) ; Mechanical engineering ; Composite material ; Nanotechnology ; Artificial intelligence ; Computer science ; Engineering ; Acoustics ; Physics Textile materials and evaluations ; Vibration and Dynamic Analysis ; Advanced Materials and Mechanics
Authors: Reza Marzban | Ali Adibi | Raphaël Pestourie
Source: Advanced Optical Materials (article, 2025-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design in Nanophotonics via Representation Learning Abstract Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition‐driven or iterative optimization methods struggle with the inherently high‐dimensional, non‐convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML‐enhanced inverse design methodologies through the lens of representation learning , classifying them into two categories: output‐side and input‐side approaches. Output‐side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input‐side techniques employ ML to learn compact, latent‐space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade‐offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics‐based optimization with data‐driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. Data efficiency, transferable representations, fabrication‐aware design, faster solvers, and hybrid multiphysics co‐design are emphasized. Multiphysics ; Solver ; Representation (politics) ; Inverse problem ; Differentiable function ; Inverse ; Generalization ; Key (lock) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Plasmonic and Surface Plasmon Research
Authors: Keshinro Kazeem Kolawole | Mohamad Shukri Bin Zainal Abidin | Mohd Farizal bin Kamaroddin | Muhammad Shahrul Azwan Ramli | Sikudhan Lucas Mpuhus | Ardiansyah Rizqi
Source: Environmental Monitoring and Assessment (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

A hybrid ACO–random forest optimization framework for scalable microalgae biomass estimation using multispectral imaging Accurate estimation of algal biomass is essential for monitoring ecosystem productivity, managing aquaculture systems, and optimizing bioresource applications. However, traditional in situ methods are labor-intensive and spatially limited, while remote sensing approaches struggle with nonlinear spectral-biological relationships and the complexity of high-dimensional models. This study develops a hybrid ant colony optimization-random forest regression (ACO-RFR) framework that integrates feature selection with hyperparameter optimization to improve biomass prediction from multispectral imagery. The preprocessing pipeline combined reflectance normalization, multicollinearity screening, and outlier detection to reduce redundancy and noise. The ACO-RFR achieved both feature reduction and robust optimization, yielding high predictive accuracy (R<sup>2</sup> = 0.96, 95% CI 0.94-0.98; RMSE = 0.05 g L<sup>-1</sup>, 95% CI 0.04-0.07) while reducing model dimensionality by more than 60%. Feature importance analysis highlighted NDVI, NIR/red ratios, and texture entropy as key biologically meaningful predictors of chlorophyll-a concentration. By leveraging low-cost imaging and computational efficiency, the framework enables scalable, real-time monitoring for aquaculture, ecological assessments, and biofuel production. Multispectral image ; Feature selection ; Random forest ; Multicollinearity ; Preprocessor ; Collinearity ; Dimensionality reduction ; Biomass (ecology) ; Pipeline (software) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring Technologies