jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eunjae Shim | Ambuj Tewari | Paul M. Zimmerman | Tim Cernak
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Prospective active transfer learning on the formal coupling of amines and carboxylic acids to form secondary alkyl bonds Tailoring a reaction condition to suit new substrates can be labor-intensive. While machine learning can aid this endeavor, conventional strategies require large datasets to make useful predictions. Active transfer learning (ATL) tackles this problem by leveraging previously collected reaction data and adaptively selecting reagent combinations. Here, ATL is prospectively applied to find improved reagent combinations for C(sp<sup>3</sup>)-C(sp<sup>3</sup>) cross-couplings between activated amines and carboxylic acids. The formation of carbon-carbon bonds from amines and acids is a powerful complement to the classic amide coupling, but the formation of sterically congested secondary alkyl groups studied here represents a challenge for catalysis. Our results demonstrate ATL consistently improved yields within three batches of experiments, making the method of practical utility for chemical space exploration studies, such as drug discovery. Reagent ; Steric effects ; Alkyl ; Amide ; Complement (music) ; Carboxylic acid ; Peptide bond ; Transfer of learning Advanced Synthetic Organic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Isabella Tavernaro | Philipp C. Sander | Elina Andresen | Uwe Schedler | Ute Resch‐Genger
Source: ACS Measurement Science Au (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Expanding the Toolbox of Simple, Cost-Efficient, and Automatable Methods for Quantifying Surface Functional Groups on Nanoparticles─Potentiometric Titration Measuring surface functional groups (FGs) on nanomaterials (NMs) is essential for designing dispersible and stable NMs with tailored and predictable functionality. FG screening and quantification also plays a critical role for subsequent processing steps, NM long-term stability, quality control of NM production, and risk assessment studies and enables the implementation of sustainable and safe-(r)-by-design concepts. This calls for simple and cost-efficient methods for broadly utilized FGs that can be ideally automated to speed up FG screening, monitoring, and quantification. To expand our NM surface analysis toolbox, focusing on simple methods and broadly available, cost-efficient instrumentation, we explored a NM-adapted pH titration method with potentiometric and optical readout for measuring the total number of (de)-protonable FGs on representatively chosen commercial and custom-made aminated silica nanoparticles (SiO<sub>2</sub> NPs). The accuracy and robustness of our stepwise optimized workflows was assessed by several operators in two laboratories and method validation was done by cross-comparison with two analytical methods relying on different signal generation principles. This included traceable, chemo-selective quantitative nuclear magnetic resonance spectroscopy (qNMR) and thermogravimetric analysis (TGA), providing the amounts of amino silanes released by particle dissolution and the total mass of the surface coatings. A comparison of the potentiometric titration results with the reporter-specific amounts of surface amino FGs determined with the previously automated fluorescamine (Fluram) assay highlights the importance of determining both quantities for surface-functionalized NMs. In the future, combined NM surface analysis with optical assays and pH titration will simplify quality control of NM production processes and stability studies and can yield large data sets for NM grouping that facilitates further developments in regulation and standardization. Potentiometric titration ; Toolbox ; Titration ; Simple (philosophy) ; Nanoparticle ; Surface modification ; Chemistry ; Computer science ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Inorganic chemistry ; Physical chemistry ; Electrode ; Programming language Quantum Dots Synthesis And Properties ; Molecular Junctions and Nanostructures ; Nanocluster Synthesis and Applications
Authors: Mehrdad T. Kiani | Alexander C. Tyner | Atharv Jog | Ching-Tzu Chen | James M. Rondinelli | J. Judy
Source: Newton (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Searching for materials for next-generation on-chip interconnects Chip ; Computer science ; Materials science ; Computer architecture ; Embedded system ; Telecommunications Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Graphene research and applications
Authors: Jason L. Wu | David M. Friday | Changhyun Hwang | Seungjoo Yi | Tiara C. Torres-Flores | Martin D. Burke | Ying Diao | Charles M. Schroeder | Nicholas E. Jackson
Source: Digital Discovery (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Democratizing machine learning in chemistry with community-engaged test sets Machine learning (ML) is increasingly central to chemical discovery, yet most efforts remain confined to distributed and isolated research groups, limiting external validation and community engagement. Limiting ; Test (biology) ; Set (abstract data type) ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zuowei Ji | Ziyu Yin
Source: Biomimetics (review, 2025-10-24)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Reactive Oxygen Species Dynamics for Assessing Nanomaterials’ Cytotoxicity Nanomaterials (NMs) possess unique physicochemical features that set them apart from bulk counterparts. Their adjustable properties provide remarkable flexibility, giving rise to a wide array of variants. However, these attributes can also trigger complex biological interactions, particularly the generation of reactive oxygen species (ROS), which are central to nanomaterial-induced cytotoxicity. The ambivalent nature of ROS, essential for physiological signaling yet harmful when dysregulated, can lead to substantial health consequences. The scarcity of reliable toxicity and safety data, together with the inadequacies of conventional testing methods, highlights the urgent need for more effective strategies to assess nanomaterial-related hazards and risks. Given the intricate interplay between NMs and biological systems, computational approaches, particularly machine learning (ML), have emerged as powerful tools to model ROS dynamics, predict cytotoxic outcomes, and optimize nanomaterial design. This review highlights recent advances in applying ML to predict both the generation and neutralization of ROS by diverse NMs and to identify the critical determinants underlying ROS-mediated toxicity. These insights provide new opportunities for predictive nanotoxicology and the development of safer, application-tailored NMs. Nanotoxicology ; Reactive oxygen species ; Cytotoxicity ; Scarcity ; Human health ; Computational model ; Pathway analysis Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods ; Graphene and Nanomaterials Applications
Authors: Alexander Liehr | Kristina Dingel | Daniel Kottke | Sebastian Degener | David Meier | Bernhard Sick | Thomas Niendorf
Source: Scientific Reports (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Data selection strategies for minimizing measurement time in materials characterization Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities. Selection (genetic algorithm) ; Computer science ; Characterization (materials science) ; Data mining ; Artificial intelligence ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced Materials Characterization Techniques
Authors: Pim F. J. Dankloff | M. van Rossum | Mathieu G. Baltussen | Stefan Hödl | Derk H. W. ten Klooster | Eline van de Voort | Peter A. Korevaar | Wilhelm T. S. Huck | William E. Robinson
Source: npj Computational Materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

An autonomous robotic module for efficient surface tension measurements of formulations Chemical space is vast, and the space of physical properties derived from it even more so. Despite over a century of physical chemistry measurements, we still lack large, well-organised data sets of key physical properties such as the interfacial tension of surfactant solutions. Access to such data sets is essential to developing the next generation of predictive models for physicochemical properties using modern approaches such as artificial intelligence and machine learning (ML). Thus, we require experimental methods which can efficiently provide large, reproducible and informative data sets. In this work, we developed a robotic, automated pendant drop module for the efficient characterisation of the interfacial properties of surfactant-containing solutions. Provided with surfactant stock solutions, the module measures a surface tension isotherm, from which multiple important physical properties can be determined, including the critical micelle concentration and the maximum surface excess concentration. The module handles critical experimental challenges and decisions such as choosing an appropriate drop volume, detecting failed measurements and selecting concentration points to measure. To obtain a maximally informative dataset, the platform leverages an active learning algorithm combining Bayesian inference and mutual information to dynamically design experiments in response to collected data. We validate the platform by characterising a range of surfactants and demonstrate the effectiveness of its capabilities by mapping the surface tension of binary surfactant mixtures. This module lays the foundation for efficiently and autonomously generating informative datasets of the interfacial properties of surfactant formulations, paving the way to the next generation of ML models for the prediction of formulation properties. Surface tension ; Pulmonary surfactant ; Drop (telecommunication) ; Critical micelle concentration ; Inference ; Data point ; Experimental data ; Surface (topology) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Block Copolymer Self-Assembly
Authors: Liang Cheng | Zheng Tan | Zongbin Jia | Qifeng Lin | Qi Yang | Sanzhong Luo
Source: ChemRxiv (preprint, 2025-05-22)
Field: Chemistry — Inorganic Chemistry

Heuristic Data-Driven Evolution of Synergistic Cobalt (IV)-Enamine Catalysis Discovering new chemical reactions is often a slow, intuition-driven process, limited by the vastness of chemical space and the challenges of pinpointing optimal conditions. Here, we introduce a data-driven strategy that integrates expert intuition with AI-guided exploration, enabling rapid identification of effective reaction conditions with minimal experimentation. Applying this approach, we establish an Co(IV)-enamine catalytic system that enables a new strategy on the α-polarity inversion of carbonyl compounds. A Bayesian optimization framework systematically explored over one million reaction conditions, converging on an optimized system within just 63 experiments. This strategy identified superior reaction conditions with minimal screening, uncovered unexpected reactivity patterns, and expanded the substrate scope through AI-guided clustering analysis. By integrating human expertise with AI-driven exploration, this workflow establishes a scalable and generalizable paradigm for reaction discovery, offering a powerful tool for more efficient and targeted development of new catalytic transformations. Enamine ; Cobalt ; Heuristic ; Catalysis ; Chemistry ; Computer science ; Business ; Organic chemistry ; Artificial intelligence Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalysis for Biomass Conversion
Authors: Z. Bian | Lei Mou
Source: Preprints.org (preprint, 2025-11-03)
Field: Materials Science — Materials Chemistry

A Large Language Model Framework for Causal Reasoning and Performance Prediction in Multimodal Time-Series Data Understanding the dynamic evolution of electrocatalysts under operando conditions is critical for advancing sustainable energy conversion. However, interpreting complex multimodal time-series data remains challenging. In this work, we presents Multimode Operando GPT (MOGPT), a large language model-based framework for causal reasoning and performance prediction in electrocatalysis. MOGPT integrates multimodal data processing with a Temporal Causal Discovery Module, a Catalytic Evolution Knowledge Graph, and a Causal Consistency Loss to identify temporal and causal relationships in catalyst behavior. A large-scale dataset of causal question–answer pairs across various catalyst systems is constructed for benchmarking. Experimental results show that MOGPT achieves superior performance in spatio-temporal reasoning, causal inference, and performance prediction, while maintaining strong robustness and generalization. This approach highlights the potential of large language models for interpretable and data-driven discovery in electrocatalysis. Robustness (evolution) ; Causal model ; Consistency (knowledge bases) ; Causal reasoning ; Language model ; Causal structure Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Bayesian Modeling and Causal Inference
Authors: Can Özkan | Lisa Sahlmann | Tim Würger | Christian Feiler | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Peyman Taheri | J.M.C. Mol
Source: npj Materials Degradation (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

Gaining scientific understanding with small data machine learning: explainable molecule representations and their consensus Abstract Despite the remarkable success of machine learning in materials science, challenges persist in gaining mechanistic insights, especially in low-data regimes where dataset sizes limit the precise applicability of machine learning. The prevailing reliance on high-confidence predictions from the models often leaves the underlying decision-making mechanisms opaque, limiting scientific understanding. This study presents an alternative approach that emphasizes understanding the model decision-making process over individual predictions, enabling the extraction of scientifically meaningful insights from small datasets. Focusing on 107 small organic molecules and their corrosion inhibition properties as a case study, we systematically evaluate 29 molecular featurization methods and 9 target representations, generating over 12 thousand model configurations to identify robust feature-target pairings. We reveal common trends by reverse engineering the best-performing models based on featurization methods of physicochemical descriptors, hashed fingerprints, and structural keys, which we integrate with domain knowledge to create a molecular substructure template for candidate molecules. Using this template, we filter a toxicity database to identify non-toxic corrosion inhibitors, aiming to replace the de facto but hazardous corrosion inhibitor hexavalent chromium. The resulting candidate’s efficacy is validated through electrochemical testing, illustrating the feasibility of achieving mechanistic insights from statistical models in data-scarce environments. Process (computing) ; Limiting ; Domain (mathematical analysis) ; Small molecule ; Limit (mathematics) ; Applicability domain ; De facto Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Corrosion Behavior and Inhibition
Authors: Anvesh Nathani | Arthur R. C. McCray | Yingtao Liu | Hanping Ding | Pejman Kazempoor | Shuozhi Xu | Colin Ophus | Iman Ghamarian
Source: Research Square (preprint, 2025-10-09)
Field: Materials Science — Surfaces, Coatings and Films

Accelerating Electron Diffraction Analysis Using Graph Neural Networks and Attention Mechanisms Graph ; Encoder ; Diffraction ; Pattern recognition (psychology) ; Convolutional neural network ; Graph theory ; Artificial neural network Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Longxiao Huang | Yang Liu | Hanguang Fu
Source: Metals (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron High-chromium cast irons (HCCIs) have emerged as preferred materials for critical wear-resistant components operating under extreme conditions, owing to their excellent wear resistance, low cost, and good castability. They are widely used in metallurgy, energy, and mechanical engineering industries. The evolution of solidification microstructure directly governs the final properties of HCCIs, making the in-depth investigation of their solidification behavior of great significance. This paper provides a comprehensive review of recent experimental and simulation-based advances in understanding the solidification microstructure evolution of HCCIs. The effects of alloy composition, cooling rate, and inoculation treatments on microstructure development and phase distribution during solidification are critically analyzed. Furthermore, the application of simulation techniques—including thermodynamic modeling, phase-field method, cellular automata, and finite element analysis—is discussed in detail, highlighting their roles in revealing the mechanisms of microstructural evolution. Finally, the current challenges and potential future research directions in the study of the solidification behavior of high-chromium cast irons are outlined. Chromium ; Cast iron ; Metallurgy ; Materials science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: David Milsted | Tara P. Mishra | Lauren N. Walters | Yuxing Fei | Bernardus Rendy | Pragnay Nevatia | Haegyeom Kim | Gerbrand Ceder
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated electron microscopy sample preparation system An automated, modular, solvent-free system enabling preparation of powder samples for electron microscopy in self-driving labs. EMSBot addresses challenges in automating sample preparation and improves consistency and reproducibility. Electron microscope ; Sample preparation ; Microscopy ; Sample (material) ; Materials science ; Energy filtered transmission electron microscopy ; Nanotechnology ; Computer science ; Chemistry ; Transmission electron microscopy ; Optics ; Chromatography ; Physics ; Scanning transmission electron microscopy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Lanting Zhang | Mingyang Liu | Hongjian Yuan | Hong Wang
Source: Chinese Science Bulletin (Chinese Version) (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

AI-driven new materials development and data standardization 人工智能（AI）和自主实验技术的飞速发展使得AI驱动的自主实验室在新材料智能研发领域展现出巨大潜力，将成为新材料研发范式变革的“新基建”。本文聚焦AI驱动的自主实验室在加速新材料发现中的国内外现状和核心挑战。首先讨论了国内外自主实验室的特征技术和自主化程度分级。重点论述了数据标准化是打破自主实验室信息孤岛，从“孤立智能”迈向“协同智能”的关键环节。最后探讨了自主实验室建设面临的挑战并对未来发展方向进行了展望。 Political science Machine Learning in Materials Science ; Engineering and Materials Science Studies ; Digital Transformation in Industry
Authors: Mohamadreza Ramezani | Poulomi Nandi | Pablo Antonio De La Fuente-Moreno | Majid Beidaghi
Source: Digital Discovery (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

BRINE: a cost-effective electrochemical self-driving laboratory for accelerated discovery of high-performance electrolytes BRINE, an electrochemical self-driving lab, autonomously prepares and measures multicomponent electrolytes, guided by Bayesian optimization, completing &gt;230 electrochemical experiments in &lt;20 h and identifying high-conductivity mixtures. Electrochemistry ; Electrolyte ; Electrode ; Context (archaeology) ; Electrochemical cell Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: A N M Nafiz Abeer | Sanket Jantre | Nathan M. Urban | Byung-Jun Yoon
Source: Molecular Systems Design & Engineering (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Enhancing generative molecular design <i>via</i> uncertainty-guided fine-tuning of variational autoencoders This study explores low-dimensional active subspace of pre-trained VAE-based generative molecular design models to perform uncertainty-guided fine-tuning of the VAE parameters to enhance downstream design task performance of the pre-trained models. Generative grammar ; Task (project management) ; Subspace topology ; Generative model ; Generative Design Machine Learning in Materials Science ; Computational Drug Discovery Methods