jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elisa Roldan | Tasneem Sabir
Source: Open MIND (preprint, 2026-02-09)
Field: Materials Science — Biomaterials

SpinCastML an Open Decision-Making Application for Inverse Design of Electrospinning Manufacturing: A Machine Learning, Optimal Sampling and Inverse Monte Carlo Approach Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures. Small variations in solution or operating conditions can shift the jet regime, generating non Gaussian fiber diameter distributions. Despite substantial progress, no existing framework enables inverse design toward desired fiber outcomes while integrating polymer solvent chemical constraints or predicting full distributions. SpinCastML is an open source, distribution aware, chemically informed machine learning and Inverse Monte Carlo (IMC) software for inverse electrospinning design. Built on a rigorously curated dataset of 68,480 fiber diameters from 1,778 datasets across 16 polymers, SpinCastML integrates three structured sampling methods, a suite of 11 high-performance learners, and chemistry aware constraints to predict not only mean diameter but the entire distribution. Cubist model with a polymer balanced Sobol D optimal sampling provides the highest global performance (R2 &gt; 0.92). IMC accurately captures the fiber distributions, achieving R2 &gt; 0.90 and &lt;1% error between predicted and experimental success rates. The IMC engine supports both retrospective analysis and forward-looking inverse design, generating physically and chemically feasible polymer solvent parameter combinations with quantified success probabilities for user-defined targets. SpinCastML reframes electrospinning from trial and error to a reproducible, data driven design process. As an open source executable, it enables laboratories to analyze their own datasets and co create an expanding community software. SpinCastML reduces experimental waste, accelerates discovery, and democratizes access to advanced modeling, establishing distribution aware inverse design as a new standard for sustainable nanofiber manufacturing across biomedical, filtration, and energy applications. Monte Carlo method ; Inverse ; Electrospinning ; Sampling (signal processing) ; Nanofiber ; Sobol sequence ; Fiber ; Design of experiments Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science
Authors: Wenbo Zhang | Yihui Wang | Jin Liu | Bowen Ke | Jiancheng Lv | Xianggen Liu
Source: Briefings in Bioinformatics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Task-specific pre-training for molecular property prediction Molecular property prediction is a critical task in computational chemistry and drug discovery. While deep learning has advanced this field, the increasing complexity of models contrasts with the scarcity of labeled data, leading to severe overfitting and limited generalization. In this paper, we propose TasProp, a task-specific pre-training strategy for molecular property prediction, particularly for the scenarios with small labeled datasets. To learn a robust molecular representation, TasProp first projects both labeled and unlabeled data into a unified latent space. Then, we introduce a task-specific contrastive loss that aligns closely with the final prediction task and apply it to the labeled data. This contrastive loss encourages the model to learn more cohesive and distinguishable molecular representations corresponding to property categories, which in turn, enhances the model's performance on downstream property prediction tasks. Additionally, we propose a novel data augmentation method, accompanied by a theoretical analysis, to mitigate the challenge of labeled data scarcity. With the task-specific pre-training and augmented data, TasProp outperforms the state-of-the-art methods on many molecular property prediction tasks, including three publicly available datasets and two curated datasets related to anesthesiology. Furthermore, we provide an interactive web resource to facilitate model exploration and application, allowing users to easily predict the properties of input molecules online. Overfitting ; Property (philosophy) ; Task (project management) ; Resource (disambiguation) ; Labeled data ; Deep learning ; Drug target ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Seema I. Habib | Jayashree  Thakre | Shaziya Mohammed Irfan Momin | M.A. Baseer | Deeba Quraishi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-17)
Field: Materials Science — Materials Chemistry

Autonomous Laboratories: Redefining Chemical Discovery Through AI and Robotics Artificial intelligence (AI) and robotics are rapidly transforming the chemical sciences by automating complex experimental workflows and accelerating discoveries. The emergence of the “RoboChemist” an autonomous chemical laboratory capable of planning, executing, and analyzing experiments with minimal human intervention represents a revolutionary advancement in scientific research. By integrating AI, machine learning (ML), robotics, high-throughput experimentation (HTE), and cloud-based data management, these systems promise improved efficiency, reproducibility, and sustainability in chemical research. This review article explores the concept, architecture, current developments, benefits, limitations, and future perspectives of RoboChemist systems, emphasizing their growing importance in modern chemistry. Robotics ; Workflow ; Scientific discovery ; Automation ; Robot Machine Learning in Materials Science ; Scientific Computing and Data Management ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ehsan Entezari | Reza Khatib Zadeh Davani | Axel Adam | Jerzy A. Szpunar
Source: Journal of Pipeline Science and Engineering (article, 2026-01-01)
Field: Materials Science — Metals and Alloys

Synergistic Effects of Ferrite Grain Features and Corrosive-Media Chemistry on the Corrosion Resistance of Nb-Microalloyed X70 Pipeline Steel: An Experimental, Machine-Learning, and Density Functional Theory-Based Study Corrosion ; Pipeline (software) ; Ferrite (magnet) ; Stress corrosion cracking ; Intergranular corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis ; Corrosion Behavior and Inhibition
Authors: Dhammadip A. Kamble | T. A. Jadhav | Anirban Sur | Ashok P. Tadamalle | Kuldeep Mishra
Source: Discover Materials (article, 2026-03-04)
Field: Materials Science — Polymers and Plastics

Advances in extraction, characterization, and surface functionalization of natural fibers for composite material applications The development of natural fiber-reinforced composite materials has gained attention due to their sustainability, renewability, lower cost, biodegradability, safety, and health benefits. Many natural fibers, sourced from plants such as banana, sisal, jute, hemp, kenaf, and coir, are favoured over synthetic fibers like nylon and linen in industries such as aerospace, automotive, sports, construction, and food packaging. These fibers are extracted from abundant sources like animals, minerals, and plants. The quality and yield of fibers depend on the extraction methods used. Additionally, various chemical treatments are applied to natural fibers to improve their compatibility with composite materials. This review article explores various methods for extracting natural fibers, potential beneficial chemical treatments, and their characteristics. These approaches aim to tailor natural fibers as reinforcement materials in polymer matrices to create composite materials. The chemical treatments used to produce application-specific fibers, along with their advantages and disadvantages, have been discussed. The article also examines the mechanical, structural, morphological, and thermal properties of these fibers in detail. The article consists of separate sections on how modern artificial intelligence and machine learning tools assist in predicting the mechanical strength, thermal stability, and structural integrity of natural fibers and their composites. The article dedicates a section to the applications of various natural fibers as reinforcements in composite materials. Finally, the research findings are summarized to provide guidelines for selecting commercially viable natural fibers suitable for composite materials. This study does not involve any clinical trials on human participants; hence, clinical trial registration is not applicable. Synthetic fiber ; Composite number ; Natural fiber ; Compatibility (geochemistry) ; Surface modification ; Fiber ; Natural (archaeology) Natural Fiber Reinforced Composites ; Scientific and Engineering Research Topics ; Electrospun Nanofibers in Biomedical Applications
Authors: Dhanabal R | Yamini J | Gomathi Priya P
Source: Next Nanotechnology (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plantain pseudostem derived activated carbon decorated zinc oxide nanoflakes for Methyl violet 10B degradation and electrochemical applications ZnO nanoparticles infused with 1%, 2% and 3% of activated carbon was chemically precipitated and utilized for photocatalytic degradation of aqueous Methyl violet 10B dye wastewater. The structural, morphological and optical properties were characterized and optical properties showed a better reduction of bandgap from 3.10 eV to 2.98 eV for 2% activated carbon infused structures. The maximum degradation of 97.43% were obtained under optimized dye concentration of 20 mg/L, catalyst dosage of 100 mg and pH of 7 with UV irradiation time of 120 min. The reusability studies proved the stability of the nanocomposites and the scavengers test showed the effectiveness of hydroxyl radicals in the degradation process. The efficiencies obtained were then compared with the predicted efficiencies from Artificial Neural Network modelling which demonstrated its effectiveness in predicting the results. Further, electrochemical studies showed a maximum specific capacitance of 583.33 F/g with an energy density of 81.02 Wh/Kg and power density of 515.48 W/Kg illustrating the materials multifunctional capabilities. • ZnO and ZPC nanocomposites were synthesized using P.nigrum seed extract via chemical precipitation method. • ZPC nanoflakes were characterized and utilized for the photocatalytic degradation of Methyl violet 10B dye wastewater. • ZPC-2% nanocomposites showed maximum degradation efficiency of 97.43% compared using predicted Artificial neural Network. • CV, GCD and EIS were performed using a three-electrode system for ZPC-2% nanocomposites. • ZPC-2% nanocomposites showed a maximum specific capacitance of 583.33 F/g at 1 A/g with an energy density of 81.02 Wh/Kg. Degradation (telecommunications) ; Nanocomposite ; Methyl violet ; Activated carbon ; Photocatalysis ; Zinc ; Capacitance ; Aqueous solution Supercapacitor Materials and Fabrication ; Advanced Cellulose Research Studies ; Carbon and Quantum Dots Applications
Authors: Maicon Pierre Lourenço | Hadi Zadeh-Haghighi | Jiří Hostaš | Mosayeb Naseri | Daya Ram Gaur | Christoph Simon | Dennis R. Salahub
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Exploring quantum active learning for materials design and discovery The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterial codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization - by density functional theory - of a doped nanoparticle (3Al@Si<sub>11</sub>) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the "roughness" of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry. Finally, we have termed the integration of quantum chemistry with QML the "QQ method": Quantum for building the potential energy surface and Quantum for new inferences and discoveries with small data using quantum algorithms. Quantum ; Quantum computer ; Quantum machine learning ; Active learning (machine learning) ; Quantum chemical Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Max Großmann | Malte Grunert | Erich Runge
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Broken neural scaling laws in materials science In materials science, data are scarce and expensive to generate, whether computationally or experimentally. Therefore, it is crucial to identify how model performance scales with dataset size and model capacity to distinguish between data- and model-limited regimes. Neural scaling laws provide a framework for quantifying this behavior and guide the design of materials datasets and machine learning architectures. Here, we investigate neural scaling laws for a paradigmatic materials science task: predicting the dielectric function of metals, a high-dimensional response that governs how solids interact with light. Using over 200,000 dielectric functions from high-throughput ab initio calculations, we study two multi-objective graph neural networks trained to predict the frequency-dependent complex interband dielectric function and the Drude frequency. We observe broken neural scaling laws with respect to dataset size, whereas scaling with the number of model parameters follows a simple power law that rapidly saturates. Scaling law ; Scaling ; Artificial neural network ; Power law ; Function (biology) ; Physical law ; Dielectric ; Complex system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Model Reduction and Neural Networks
Authors: Anushka Asurumunige | Sebastian M. Malespini | Tish Huynh | Nia Shields | Kerry Gilmore
Source: The Journal of Organic Chemistry (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Leveraging Environmental Control for Regiodivergent Epoxide Ring-Openings The environment of a reaction plays a pivotal role in dictating both the efficiency and selectivity. In this study, we explore how environmental parameters can be strategically manipulated to achieve divergent regioselective syntheses using epoxide aminolysis as a model system. This transformation enables the formation of β-amino alcohols, a highly versatile scaffold prevalent in pharmaceuticals, natural products, and advanced materials. Regioselective aminolysis of epoxides leads to C-N bond formation at distinct positions, generating constitutional isomers via S<sub>N</sub>1- or S<sub>N</sub>2-type pathways. The inherent regioselectivity is traditionally governed by the steric and electronic properties of the epoxide and nucleophile. However, we demonstrate that by precisely tuning environmental factors, including Lewis acid catalyst, solvent, temperature, and stoichiometry, we can override the intrinsic selectivity and dynamically control regioisomeric outcomes. Our findings highlight the power of reaction environment engineering in governing selectivity beyond substrate-imposed constraints, paving the way for new strategies in reaction optimization, predictive modeling, and machine-learning-driven model design for regioselective transformations. Regioselectivity ; Aminolysis ; Epoxide ; Steric effects ; Selectivity ; Allylic rearrangement Synthesis and Catalytic Reactions ; Asymmetric Synthesis and Catalysis ; Cyclopropane Reaction Mechanisms
Authors: Li-Yang Fan | Xuetao Li | Wen-Xin Nie | Bo Zhu | Wei Guan
Source: Chinese Chemical Letters (article, 2026-03-01)
Field: Chemistry — Organic Chemistry

Machine learning-enabled prediction of reactivity in metallaphotoredox SH2-mediated C(sp3)–C(sp3) coupling Coupling (piping) ; Reactivity (psychology) ; Work (physics) Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Shoma Kawano | Futoshi Utsuno | Aoto Matsuo | Takahiro Ohkubo
Source: ChemRxiv (article, 2026-02-02)
Field: Chemistry — Organic Chemistry

Li + Transport in Ca-Substituted β-Li 3 PS 4 : Role of PS 4 3- Vibrations and Rotations from Machine-Learning Molecular Dynamics Molecular dynamics ; Vibration ; Dynamics (music) ; Work (physics) Coordination Chemistry and Organometallics ; Machine Learning in Bioinformatics ; Machine Learning in Materials Science
Authors: Jingjing Qiao | Zhouyun Chen | Zheng Liang | Weijun Wu | Zhi Luo
Source: ChemRxiv (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Graph learning of sequence statistics for polymer representation Polymers underpin critical technologies from medicine to energy, but their immense chemical and structural diversity makes rational design exceptionally difficult. Machine learning offers a way to navigate this space, yet prevailing approaches inherit small-molecule representations that fail to encode polymer-specific architecture; the distinction between random, block and other statistical copolymers is often collapsed into a categorical tag or ignored. Here, we introduce SCALE (Statistical Copolymer Architecture with Learning Edges), which recasts a copolymer as a Markovian sequence over a monomer alphabet and embeds the transition probabilities P(j/i) as edge features within a graph attention network. Message passing thus computes contextualized monomer states analogous to applying a transfer operator along the chain, while attention learns a data-driven kernel over paths that weights sequence heterogeneity versus block persistence. On a robotically synthesized, high-throughput fluorescence library, SCALE attained RMSE ≈228 and R² ≈0.84, surpassing polymer-adapted neural baselines and descriptor regressors (e.g., wDMPNN RMSE ≈326; XGBoost RMSE ≈254). The model is interpretable: edges dominate predictions for statistical (random) copolymers, whereas nodes prevail for block copolymers, consistent with NOESY 2D NMR. Beyond photophysics, SCALE generalized to antibacterial design across penta- and hexa-copolymer libraries with validation from <300 syntheses. By elevating sequence statistics to first-class learning variables, SCALE provides a generalizable, data-efficient route to closed-loop polymer discovery. Categorical variable ; Block (permutation group theory) ; Sequence (biology) ; Pattern recognition (psychology) ; Kernel (algebra) ; Representation (politics) ; ENCODE ; Graph ; Kernel method Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Xi Liu | Zixuan Yu | Hao Zhu | Wenquan Tao | Zhuo Li | Boyu Li | Aziz Ghoufi
Source: Small Methods (article, 2026-01-29)
Field: Chemistry — Inorganic Chemistry

Enhancing the CO <sub>2</sub> Separation Performance of MOFs‐Based Membranes: From Strategic Modifications to Computational Insights Carbon dioxide (CO<sub>2</sub>) separation and capture technologie have been considered as a viable strategy for mitigating CO<sub>2</sub> emissions to reduce the greenhouse effect. Metal organic frameworks (MOFs)-based membrane separation technology has demonstrated to be an efficient and sustainable approach for CO<sub>2</sub> separation and capture compared to the conventional methods. The development of high-performance materials and fabrication techniques, assisted with modern computational methods, has become an active research area. In this review, the state of the art and applications of MOFs-based membrane for CO<sub>2</sub> separation technology were summarized, and the materials synthesis and modification methods reported in the last five years were comprehensively compared to evaluate the advantages and limitations in improving the permeability and selectivity of the membranes. The most recent progress of computational methods involving molecular simulations and machine learning was outlined to understand the underlying molecular mechanisms of the separation performance and high-throughput screening of materials. Finally, the challenges and prospects of the current development status of MOFs-based membranes in both experiments and data-driven methods for CO<sub>2</sub> separation were addressed. Separation (statistics) ; Membrane technology ; Membrane ; Separation method ; Gas separation Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon Dioxide Capture Technologies
Authors: Alexey A. Orlov | Mikhail Volkov | Evgeniia Milova | Dragos Horvath | Alexandre Varnek
Source: ChemRxiv (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

ChemSpace Copilot: Agentic AI for Interactive Visualization and Exploration of Chemical Space Chemography provides a foundational approach to exploring chemical space by compressing high-dimensional representations of chemical structures into 2D maps, enabling clearer interpretation of similarity, diversity, and activity. Such maps power core chemoinformatics tasksstructure-activity relationship analysis, library design, virtual screening, and de novo design. In practice, effective use of chemography relies on manual application of data analysis tools and expert-driven interpretation of the resulting landscapes for hypothesis generation. To automate chemography-guided discovery, we introduce ChemSpace Copilotan agentic AI application that encapsulates standard chemographic workflows based on the non-linear dimensionality reduction technique Generative Topographic Mapping (GTM). ChemSpace Copilot enables researchers to perform chemographic tasks end-to-end through an intuitive, chatbot-style interface. Its modular toolkit integrates chemical data retrieval from ChEMBL, GTM parameter optimization, chemotype analysis and report generation. We further demonstrate the extensibility of the framework by integrating generative deep learning models with the synthesis planning, enabling effective and illustrative end-to-end molecular discovery from design to synthetic feasibility. We demonstrate its utility across three tasks: exploration of ChEMBL's chemical space, chemography-guided compound generation, and synthesis prediction. Chemical space ; Cheminformatics ; Workflow ; Visualization ; Modular design ; Interpretation (philosophy) ; Data exploration ; Data visualization ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis