jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juejing Liu | Haydn Anderson | Noah I. Waxman | Vsevolod Kovalev | Byron Fisher | Elizabeth Li | Xiaofeng Guo
Source: Advanced Intelligent Systems (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning New discoveries in chemistry and materials science, with an increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in accelerating research efficiency. This work demonstrates 1) the use of large language models (LLMs) for automated literature reviews; and 2) the training of an ML model to predict chemical knowledge (thermodynamic parameters). The LLM‐based literature review tool (LMExt) successfully extracted chemical information and beyond into a machine‐readable structure, including stability constants for metal cation–ligand interactions, thermodynamic properties, and other broader data types (medical research papers and financial reports), effectively overcoming the challenges inherent in each domain. Using the autonomous acquisition of thermodynamic data, an ML model is trained using the CatBoost algorithm for accurately predicting thermodynamic parameters (e.g., enthalpy of formation) of minerals. This work highlights the transformative potential of integrated ML approaches to reshape chemistry and materials science research. Transformative learning ; Work (physics) ; Stability (learning theory) ; Enthalpy ; Experimental data ; Chemical stability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Khalil Rejiba | Sang-Hyeok Lee | Christina Gasper | Martina Freund | Sandra Korte-Kerzel | U. Kerzel
Source: Advanced Engineering Materials (article, 2026-04-04)
Field: Materials Science — Materials Chemistry

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows Defect phase diagrams provide a unified description of crystal defect states for materials design and are central to the scientific objectives of the Collaborative Research Centre (CRC) 1394. Their construction requires the systematic integration of heterogeneous experimental and simulation data across research groups and locations. In this setting, research data management (RDM) is a key enabler of new scientific insight by linking distributed research activities and making complex data reproducible and reusable. To address the challenge of heterogeneous data sources and formats, a comprehensive RDM infrastructure has been established that links experiment, data and analysis in a seamless workflow. The system combines: (1) a joint electronic laboratory notebook and laboratory information management system, (2) easy‐to‐use large‐object data storage, (3) automatic metadata extraction from heterogeneous and proprietary file formats, (4) interactive provenance graphs for data exploration and reuse and (5) automated reporting and analysis workflows. The two key technological elements are the openBIS electronic laboratory notebook and laboratory information management system, and a newly developed companion application that extends openBIS with large‐scale data handling, automated metadata capture and federated access to distributed research data. This integrated approach reduces friction in data capture and curation, enabling traceable and reusable datasets that accelerate the construction of defect phase diagrams across institutions. Workflow ; Metadata ; RDM ; Data management ; Key (lock) ; Data mapping ; Data integration ; Data access ; Data element ; Data sharing Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuxing Fei | Matthew J. McDermott | Christopher L. Rom | Shilong Wang | Gerbrand Ceder
Source: Chemistry of Materials (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans' efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery. Rietveld refinement ; Characterization (materials science) ; Isostructural ; Identification (biology) ; Powder diffraction ; Cluster analysis ; Phase (matter) ; Diffraction X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Ethan C. Quinn | Levi J. Hamernik | Jeffrey N. Law | Ryan W. Clarke | Maya Milrod | Shivani Kozarekar | Rebecca M. Mick | Margaret J. Sobkowicz | Linda J. Broadbelt | Brandon C. Knott | Katrina M. Knauer
Source: Nature Communications (article, 2026-02-03)
Field: Materials Science — Biomaterials

Cracking the code of multi-layer films to promote circularity in single-use plastic packaging Multi-layer film packaging (MLF) revolutionized food preservation by combining diverse material layers to optimize barrier properties, mechanical strength, and shelf-life. These materials are essential for transporting perishables across various climates and allow for access to fresh goods in "food deserts", but they pose significant recycling challenges due to their structural complexity. This perspective examines key structure-property relationships governing barrier performance and highlights innovations in material design. We explore how machine learning can predict performance metrics and propose recyclable alternatives, integrating data-driven approaches with material science insights. By challenging the status quo of MLF design, we advocate for circularity in food packaging, inspiring innovation at the intersection of sustainability, material science, and artificial intelligence. Intersection (aeronautics) ; Status quo ; Cracking ; Code (set theory) ; Perspective (graphical) ; Food packaging Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Sustainable Supply Chain Management
Authors: Sreya Vangara | Jagjit Nanda | Yan‐Kai Tzeng
Source: MRS Energy & Sustainability (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research Abstract Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies. Graphical abstract Conceptual overview of machine learning-driven battery research, illustrating how physics-based constraints, feedback-driven optimization, and predictive forecasting jointly enable adaptive and robust battery systems. The figure was generated with the assistance of generative AI tools (Gemini and ChatGPT) and curated by the authors. Battery (electricity) ; Reinforcement learning ; Categorization ; Domain (mathematical analysis) ; Generative grammar ; Feature extraction ; Generative model Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced battery technologies research
Authors: Pouyan Navabi | Christos G. Takoudis
Source: arXiv (Cornell University) (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Tuning of Atomic Layer Deposition Pulse Time through Physics-Informed Bayesian Active Learning Atomic Layer Deposition (ALD) process development is often hindered by time-consuming and precursor-intensive tuning cycles required to identify saturation conditions. We introduce a physics-informed Bayesian active learning (BAL) framework that autonomously tunes precursor pulse times by integrating a Langmuir adsorption model directly into the Gaussian Process (GP) kernel. A key innovation is a two-stage parameter estimation strategy that decouples noise filtering from physical parameter extraction: the GP first smooths noisy data through standard prediction, then Langmuir parameters are fitted to the noise-filtered GP predictions. This approach effectively separates signal from experimental noise. We evaluate the framework against a standard data-driven GP across four simulated regimes, demonstrating convergence within five iterations, up to fourfold improvement in prediction accuracy, and two to fourfold reduction in precursor usage. Experimental validation using TiO2 deposition via Tetrakisdimethylamido Titanium (TDMAT) and ozone confirms that the physics-informed model accurately identifies saturation times for high-coverage targets ($\geq$95\%), with observed deviations at lower saturation levels providing valuable insight into non-ideal desorption behaviors. Saturation (graph theory) ; Gaussian process ; Bayesian probability ; Standard deviation ; Atomic layer deposition ; Deposition (geology) ; Gaussian ; Convergence (economics) ; Desorption Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Alexandra L. Day | Carolin B. Wahl | Roberto dos Reis | Youjia Li | Liao Wei-keng | Vinayak P. Dravid | Chad A. Mirkin | Choudhary Alok | Ankit Agrawal
Source: Scientific Reports (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Image processing pipeline for AI-driven nanoparticle megalibrary characterization Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery. Pipeline (software) ; Image processing ; Noise (video) ; Pixel ; Artificial neural network ; Pattern recognition (psychology) ; Throughput ; Characterization (materials science) ; Image (mathematics) Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Mineral Processing and Grinding
Authors: Felix Neubauer | Kenichi Endo | Frederic Bender | Esengül Çiftçi | Niels Hansen | Simon Krause | Benjamin Rodenberg | Jürgen Pleiss
Source: ChemRxiv (article, 2026-02-02)
Field: Chemistry — Inorganic Chemistry

Data-Model-Driven Management and Analysis of MOF Synthesis Data The successful synthesis of metal-organic frameworks (MOFs) in high yield and purity critically depends on the details of the procedure. Therefore, the machine-readable as well as findable, accessible, interoperable, and reusable (FAIR) documentation of the synthesis procedure and the associated characterization data is crucial to ensure reproducibility and to enable the data-driven analysis and systematic optimization of synthesis. Here, we demonstrate a data-processing workflow development based on a JSON Schema data model for the synthesis and characterization of MOFs. Its feasibility and usefulness is demonstrated by synthesis data of two MOF systems, Fe-terephthalate MOF and MOCOF-1, and their subsequent characterization by powder X-ray diffraction (PXRD). The data model supports the development of an integrated workflow to (1) parse synthesis data from a table or an electronic lab notebook (ELN) into standardized JSON forms, (2) validate the datasets for errors and incompleteness, (3) serialize the data into the standardized data exchange formats MPIF and XDL, and (4) analyze PXRD data by a decision tree to identify critical synthesis parameters that control phase selectivity and yield. The data model and the workflow are modular and extensible, and can be adapted to other data sources, characterizations, and AI methods for analysis. The proposed data model strategy makes MOF synthesis FAIR and AI-ready, fosters the digitalization of synthetic chemistry, and accelerates discovery. Workflow ; JSON ; Modular design ; Data exchange ; Data management ; Serialization ; Documentation ; Data modeling ; Modbus ; Data manipulation language Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Tiancai Zhang | Yi Yang | Tao Hong
Source: Magnetochemistry (article, 2026-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Guided Design and Performance Prediction of Multidimensional Magnetic MXene-Based Nanocomposites for High-Efficiency Microwave Absorption MXene-based microwave absorbers have received extensive attention owing to their high electrical conductivity, abundant interfacial polarization sites, and tunable surface terminations. However, the structure–property relationship of MXene composites remains highly nonlinear, and the design of high-efficiency absorbers still relies heavily on trial-and-error experiments. Herein, multidimensional magnetic components, including zero-dimensional (0D) Fe3O4 nanoparticles, one-dimensional (1D) Fe3O4/Co3O4 nanowires, and two-dimensional (2D) Fe3O4-based heterostructures, were rationally integrated with Fe/MXene and Fe/Co/MXene nanosheets to engineer synergistic dielectric and magnetic losses. Comprehensive electromagnetic characterization and loss mechanism analysis reveal that the structural dimensionality strongly impacts impedance matching and attenuation capability. To further enable predictive and data-driven optimization, a machine learning framework was established to correlate the microstructure, component ratio, thickness, and electromagnetic parameters with the microwave absorption performance (e.g., minimum reflection loss (RLmin), effective absorption bandwidth (EAB)). The optimized multidimensional composite achieves an RLmin of −56.4 dB at 10.2 GHz with an EAB of 8.4 GHz (9.6–18.0 GHz) at a thin matching thickness of 1.8 mm. The machine learning model demonstrates excellent accuracy (R2 = 0.947) and enables the inverse design of absorber geometries to target specific operational frequencies. This work provides a generalizable paradigm for the intelligent design of MXene-based microwave absorbers and opens up broader opportunities for the AI-accelerated discovery of advanced electromagnetic functional materials. Microwave ; Attenuation ; Impedance matching ; Dielectric ; Reflection loss ; Nanocomposite ; Electromagnetic radiation ; Bandwidth (computing) ; Polarization (electrochemistry) ; Absorption (acoustics) Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; MXene and MAX Phase Materials
Authors: Guillermo Restrepo
Source: Digital Discovery (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Higher order structures in chemistry: hypergraphs reshape the molecule and the reaction Chemical systems contain higher-order relationships that exceed the binary constraints of traditional graph-based models. Order (exchange) ; Binary number ; Set (abstract data type) ; Molecule ; Class (philosophy) History and advancements in chemistry ; Chemistry and Stereochemistry Studies ; Graph theory and applications
Authors: Chikondi Shaba | Decibel P. Elpa | Pawel L. Urban
Source: Digital Discovery (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Automated and robotic sample delivery systems for mass spectrometry and ion-mobility spectrometry Mass spectrometry and ion-mobility spectrometry are two complementary tools for molecular analysis. They have been extensively automated opening the doors to exciting applications such as single-cell analysis. Mass spectrometry ; Sample (material) ; Sample preparation ; Mass spectrometry imaging ; Ion-mobility spectrometry–mass spectrometry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Naveed Farhana
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bio-Inspired Swarm-Transformer Hybrid Algorithm for HighPrecision Nanomaterial Design and Experimental Validation in Biomedical Applications Rational design of nanomaterials with tightly defined physicochemical characteristics is essential to the development of biomedical uses of nanomaterials including targeted delivery of drugs, biosensing, and therapeutic imaging. The current methods of computational design, however, cannot effectively search high dimensional design spaces and remain predictively accurate and experimentally viable. Mainstream optimization methods tend to reach a solution at an early stage, but deep learning models need massive, labelled datasets and do not support any form of exploration. This poses a very important gap between the computer prediction and experimentally achievable nanomaterial designs. To cope with this difficulty, this article presents a bio-inspired swarm-Transformer hybrid algorithm, which may be applied to design nanomaterial with a high degree of precision and biomedical implementation. The structure combines the global exploration through swarm intelligence with a Transformer-based attention mechanism to understand long-range interactions of nanomaterial descriptors. The swarm module intelligently directs the search to likely areas of designs, whereas the Transformer narrows candidate representations used in predicting and optimizing the desired properties. Making use of publicly available nanomaterial property datasets, the model is assessed and experimental synthesis and characterization of the biomedical performance metrics are validated. The experimental outcomes indicate that on average, it has been able to improve the prediction accuracy of deep learning baselines by 6.8% and also achieves 23 reduction in convergence time. In vitro tests also conclude reaching improved biocompatibility and functional effectiveness of the optimized nanomaterials. These findings demonstrate the usefulness of the suggested hybrid methodology in the gap between computational intelligence and experimental nanomedicine. Nanomaterials ; Swarm intelligence ; Convergence (economics) ; Particle swarm optimization ; Reduction (mathematics) ; Experimental data ; Swarm behaviour Machine Learning in Materials Science ; Graphene and Nanomaterials Applications ; Advanced Technologies and Applied Computing
Authors: Shaan Pakala | Aldair E. Gongora | Brian Giera | Evangelos E. Papalexakis
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Tensor Methods: A Unified and Interpretable Approach for Material Design When designing new materials, it is often necessary to tailor the material design (with respect to its design parameters) to have some desired properties (e.g. Young's modulus). As the set of design parameters grow, the search space grows exponentially, making the actual synthesis and evaluation of all material combinations virtually impossible. Even using traditional computational methods such as Finite Element Analysis becomes too computationally heavy to search the design space. Recent methods use machine learning (ML) surrogate models to more efficiently determine optimal material designs; unfortunately, these methods often (i) are notoriously difficult to interpret and (ii) under perform when the training data comes from a non-uniform sampling of the design space. We suggest the use of tensor completion methods as an all-in-one approach for interpretability and predictions. We observe classical tensor methods are able to compete with traditional ML in predictions, with the added benefit of their interpretable tensor factors (which are given completely for free, as a result of the prediction). In our experiments, we are able to rediscover physical phenomena via the tensor factors, indicating that our predictions are aligned with the true underlying physics of the problem. This also means these tensor factors could be used by experimentalists to identify potentially novel patterns, given we are able to rediscover existing ones. We also study the effects of both types of surrogate models when we encounter training data from a non-uniform sampling of the design space. We observe more specialized tensor methods that can give better generalization in these non-uniforms sampling scenarios. We find the best generalization comes from a tensor model, which is able to improve upon the baseline ML methods by up to 5% on aggregate $R^2$, and halve the error in some out of distribution regions. Interpretability ; Tensor (intrinsic definition) ; Generalization ; Sampling (signal processing) ; Set (abstract data type) ; Tensor contraction ; Material Design Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Tensor decomposition and applications