jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wentao Li | Y. H. Li | Lei Qi | Zhi Wang | Xiaonan Wang
Source: Digital Discovery (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

PolyRL: reinforcement learning-guided polymer generation for multi-objective polymer discovery PolyRL integrates property prediction, polymer generation, reinforcement learning, and molecular dynamics validation into a closed-loop framework for polymer discovery. Polymer ; Reinforcement ; Property (philosophy) ; Component (thermodynamics) ; Reinforcement learning Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Simran Rani | Shikha Dhankher | Pradeep Kumar | Priyanka Dahiya | Kiran Arora | Amita Suneja Dang | Pooja Suneja
Source: Frontiers in Nanotechnology (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

Biosynthesis, optimization, and characterization of CuNPs using Bacillus licheniformis CPJN13S and their antibacterial activity Nanoparticles (NPs) possess unique properties due to their higher surface-to-volume ratio and reactivity. Negative environmental impact and high cost of traditional modes of synthesis have driven the shift towards utilization of microbes and plants for synthesising NPs, referred as the biological or ‘Green’ synthesis. This study reported extracellular synthesis of copper NPs (CuNPs) using the supernatant of Bacillus licheniformis CPJN13S. The parameters affecting this process were optimized by OFAT approach and were reported to be 5 mM concentration of copper sulfate (CuSO 4 ), 32 h incubation period, 18 h reaction time, 20:20 filtrate/substrate ratio, 7 pH, and 37 °C temperature. CuNPs produced a characteristic UV-Visible absorption peak between 550–650 nm, Z-average of 305.3 nm and zeta potential value of −24.7 mV. SEM and HR-TEM revealed hexagonal shape of NPs having average size of 12.4 nm. XRD peaks obtained at 2θ positions of 45.52°, 56.52°, and 75.34° matched to diffraction from Cu. Antimicrobial assay conducted using 100 μg/mL CuNPs led to highest inhibition of 20.4% ( Bacillus subtilis MTCC No. 441) and 43% ( Staphylococcus aureus MTCC No. 737), at 21 h and 27 h, respectively. The results suggest that biological synthesis can serve as the eco-friendly alternative of physical and chemical modes of synthesizing CuNPs and can be used to develop highly effective antibacterial agents. Antibacterial activity ; Bacillus licheniformis ; Zeta potential ; Copper ; Biological activity ; Antimicrobial ; Bacteria ; Antibacterial agent Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Medicinal Plants and Neuroprotection
Authors: Danial Ebrahimzadeh | Safura Sharifi | Yaser Mike Banad
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

Synthesizability Prediction of Crystalline Structures with a Hierarchical Transformer and Uncertainty Quantification Predicting which hypothetical inorganic crystals can be experimentally realized remains a central challenge in accelerating materials discovery. SyntheFormer is a positive-unlabeled framework that learns synthesizability directly from crystal structure, combining a Fourier-transformed crystal periodicity (FTCP) representation with hierarchical feature extraction, Random-Forest feature selection, and a compact deep MLP classifier. The model is trained on historical data from 2011 through 2018 and evaluated prospectively on future years from 2019 to 2025, where the positive class constitutes only 1.02 per cent of samples. Under this temporally separated evaluation, SyntheFormer achieves a test area under the ROC curve of 0.735 and, with dual-threshold calibration, attains high-recall screening with 97.6 per cent recall at 94.2 per cent coverage, which minimizes missed opportunities while preserving discriminative power. Crucially, the model recovers experimentally confirmed metastable compounds that lie far from the convex hull and simultaneously assigns low scores to many thermodynamically stable yet unsynthesized candidates, demonstrating that stability alone is insufficient to predict experimental attainability. By aligning structure-aware representation with uncertainty-aware decision rules, SyntheFormer provides a practical route to prioritize synthesis targets and focus laboratory effort on the most promising new inorganic materials. Discriminative model ; Feature (linguistics) ; Representation (politics) ; Uncertainty quantification ; Pattern recognition (psychology) ; Convex hull ; Stability (learning theory) ; Regular polygon ; Focus (optics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Rejiba, Khalil | Lee, Sang-Hyeok | Gasper, Christina | Freund, Martina | Korte-Kerzel, Sandra | Kerzel, Ulrich
Source: ArXiv.org (article, 2025-11-03)
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: Eduardo Rial‐Rodríguez | C. Oliver Kappe | Gabriele Laudadio
Source: Chemistry - Methods (article, 2025-09-11)
Field: Chemistry — Electrochemistry

Automating Synthetic Organic Electrochemistry: Concepts and Advancements Automated electrochemical platforms are attracting attention for their potential in streamlining reaction preparation, electrolysis, and analysis to generate large datasets in a fast and efficient manner. Moreover, the recent implementation of on‐line analytics together with closed‐loop workflows has enabled the emergence of electrochemical self‐driving laboratories. In this review, the common guidelines for the development of these platforms are presented, covering the main modules to assemble these systems. Furthermore, a comprehensive survey of all the platforms applied in synthetic organic electrochemistry is reported, where each example is described in terms of elements and applications and classified by category. Finally, an outlook including a perspective on the topic is presented, proposing potential future directions of the field. Workflow ; Perspective (graphical) ; Analytics ; Data analysis Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; CO2 Reduction Techniques and Catalysts
Authors: Hongmei Xing | Qiuxin Si
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Learning Continuous Solvent Effects from Transient Flow Data: A Graph Neural Network Benchmark on Catechol Rearrangement Predicting reaction outcomes across continuous solvent composition ranges remains a critical challenge in organic synthesis and process chemistry. Traditional machine learning approaches often treat solvent identity as a discrete categorical variable, which prevents systematic interpolation and extrapolation across the solvent space. This work introduces the \textbf{Catechol Benchmark}, a high-throughput transient flow chemistry dataset comprising 1,227 experimental yield measurements for the rearrangement of allyl-substituted catechol in 24 pure solvents and their binary mixtures, parameterized by continuous volume fractions ($\% B$). We evaluate various architectures under rigorous leave-one-solvent-out and leave-one-mixture-out protocols to test generalization to unseen chemical environments. Our results demonstrate that classical tabular methods (e.g., Gradient-Boosted Decision Trees) and large language model embeddings (e.g., Qwen-7B) struggle with quantitative precision, yielding Mean Squared Errors (MSE) of 0.099 and 0.129, respectively. In contrast, we propose a hybrid GNN-based architecture that integrates Graph Attention Networks (GATs) with Differential Reaction Fingerprints (DRFP) and learned mixture-aware solvent encodings. This approach achieves an \textbf{MSE of 0.0039} ($\pm$ 0.0003), representing a 60\% error reduction over competitive baselines and a $>25\times$ improvement over tabular ensembles. Ablation studies confirm that explicit molecular graph message-passing and continuous mixture encoding are essential for robust generalization. The complete dataset, evaluation protocols, and reference implementations are released to facilitate data-efficient reaction prediction and continuous solvent representation learning. Extrapolation ; Categorical variable ; Artificial neural network ; Interpolation (computer graphics) ; Graph ; Benchmark (surveying) ; Binary number ; Generalization Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Kentaro Fuku | Takefumi Yoshida
Source: ChemRxiv (preprint, 2025-01-17)
Field: Chemistry — Analytical Chemistry

Unsupervised machine learning-based image recognition of raw IR spectra: Toward chemist-like chemical structural classification and beyond numerical data Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical classification was demonstrated by extracting IR spectral images from the Spectral Database for Organic Compounds and converting them into 208620-dimensional vector data. Hierarchical clustering of 230 compounds revealed distinct main clusters (A–G), each with specific subclusters exhibiting higher intracluster similarities. Despite the challenges, including sensitivity to spectral deviations and difficulty of distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the proposed image recognition approach exhibits good potential. Both principal component analysis (PCA) and k-means clustering produced similar results. Furthermore, the method demonstrated high robustness to noise. The Tanimoto coefficient was used to evaluate the molecular similarity, providing valuable insights. However, some results deviated from chemists’ intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. A comparison of the clustering results obtained from molecular fingerprints, using the adjusted Rand index as a metric, indicated that the image data provided better classification performance than numerical data of the same resolution. Overall, this study demonstrates the feasibility of using machine learning with IR spectral image data for chemical classification and offers a novel perspective that complements traditional methods, although the classifications may not always align with chemists’ intuitions. This approach has broader implications for fields such as drug discovery, materials science, and automated spectral analysis, where handling large, raw spectral datasets is essential. Chemist ; Raw data ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Image (mathematics) ; Machine learning ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xia‐Feng Lu | Min Cao | Min Ma | Yan Wu | Moyuan Qu | Feng Du | Rendong Ji | Mengran Duan | Leichi Dong | Kai Liu | Xueqi Zhang | Zhibo Luo
Source: Food Bioengineering (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Accelerating Enzyme Engineering With Artificial Intelligence in Biocatalysis: Challenges and Opportunities ABSTRACT Artificial intelligence (AI) has emerged as a paradigm‐shifting force in enzyme engineering, enabling data‐driven prediction of catalytic activity, stability, and substrate specificity. By integrating large‐scale datasets from structured databases (e.g., PDB, BRENDA) and high‐throughput experimentation (e.g., deep mutational scanning, microfluidics), machine learning (ML) approaches—including random forests, support vector machines, and deep neural networks—have accelerated enzyme optimization across key domains: mutational profiling, catalytic condition refinement, and mechanistic elucidation. Notably, AlphaFold has revolutionized structure prediction, while AI‐directed evolution enhanced enantioselectivity in nonnatural reactions (e.g., C─Si bond formation). Nevertheless, persistent challenges include data heterogeneity, model overfitting with sparse datasets, and limited interpretability of deep learning frameworks. Future advancements necessitate hybrid strategies merging AI with physics‐based simulations (e.g., molecular dynamics), rigorous standardization of databases (aligned with FAIR principles), and synergistic integration of rational design with data‐driven optimization. This review critically evaluates AI's transformative potential and methodological gaps in enzyme engineering, highlighting implications for sustainable biomanufacturing and industrial biocatalysis. Interpretability ; Overfitting ; Biomanufacturing ; Transformative learning ; Synthetic biology ; Deep learning ; Standardization Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Asymmetric Hydrogenation and Catalysis
Authors: Samrat Chakraborty
Source: Preprints.org (preprint, 2025-10-16)
Field: Materials Science — Materials Chemistry

Machine Learning in Chemical Synthesis: A Student’s Guide to AI-Powered Chemistry Machine learning is rapidly reshaping the field of chemical synthesis, influencing how chemists design, plan, and carry out reactions. Yet for many students, the role of artificial intelligence in synthesis remains abstract or inaccessible. This article introduces the current landscape of machine learning in chemistry, focusing on why these new approaches are emerging and how they complement, rather than replace, human creativity. We outline the challenges of traditional retrosynthetic planning, describe the rise of AI-driven platforms such as IBM RXN and AiZynthFinder, and discuss how they exemplify broader changes in the practice of chemistry. Practical “try it yourself” suggestions are included to help students and educators explore these developments firsthand. By highlighting both opportunities and limitations, this guide equips learners to critically understand the future of synthesis as a collaboration between chemists and computational tools. Field (mathematics) ; IBM ; Work (physics) ; Retrosynthetic analysis Machine Learning in Materials Science ; Various Chemistry Research Topics ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Citra Ayu Dewi | Martini | Mujakir
Source: Multidisciplinary Science Journal (article, 2025-10-24)
Field: Chemistry — Physical and Theoretical Chemistry

The impact of chemlab-based interactive media to improving students' information literacy on electrochemical Information literacy in higher education is crucial because it enhances students’ critical thinking skills. This encompasses their ability to identify, gather, comprehend, analyze, synthesize, and utilize information, as well as their confidence in managing information throughout their academic journey. This study investigates the impact of ChemLab-based interactive media in online learning on improving students’ information literacy. A one-group pretest-posttest experimental design was employed, involving 75 students enrolled in a basic chemistry course focusing on electrochemistry. A validated and reliable 30-item multiple-choice instrument was used to measure outcomes. The results indicated an N-Gain value of 0.33, which falls into the medium category. This suggests that students’ information literacy improved following instruction with ChemLab-based interactive media. Furthermore, students’ evaluations of the ChemLab platform indicated a greater inclination to engage in learning through ChemLab compared to non-ChemLab or traditional educational platforms. Therefore, it can be concluded that the use of ChemLab-based interactive media in chemistry education enhances students’ cognitive abilities and supports a deeper understanding of chemistry concepts through virtual activities. Information literacy ; Interactive Learning ; Interactive media ; Media literacy ; Cognition ; Literacy ; Electronic learning Various Chemistry Research Topics ; Science Education and Pedagogy ; Educational Strategies and Epistemologies
Authors: Gautam V. Soni | Manjit Singh | S. K. Jaiswal
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

AI-Driven Nanotechnology: Transforming Materials Science, Medicine, and Electronics Artificial intelligence (AI) is transforming nanotechnology by processing large datasets, modeling nanoscale interactions, and optimizing experimental and manufacturing processes, thereby advancing materials science, nanomedicine, nanoelectronics, and environmental applications. AI-driven machine learning enhances the characterization and classification of nanomaterials, improves image analysis in atomic force and scanning probe microscopy, and accelerates the discovery of novel nanostructures with tailored properties. In nanomedicine, AI supports the optimization of nano-structured biomaterials for diagnostics and treatments, the prediction of nanotoxicity, and the design of nanoparticles for targeted drug delivery, while in nanoelectronics it enables the development of advanced nanocomputing architectures, optimizing device design and exploring quantum computing applications. Additionally, AI contributes to environmental nanotechnology by simulating nanoparticle interactions with biological and ecological systems, leading to the development of safer, more effective nanomaterials. However, challenges such as the need for interpretable models aligned with experiments, computational complexity, and data standardization persist, along with ethical concerns regarding regulatory compliance and nanotoxicity evaluation. Future research must focus on building robust AI frameworks that bridge theoretical models with experimental validation, foster interdisciplinary collaboration, and ensure the ethical use of AI in nanoscience, highlighting its vast potential to drive scientific progress and address pressing global challenges. Keywords: Artificial Intelligence (AI), Nanotechnology, Nanotoxicity, Nanoscience, Quantum Computing. Nanotechnology ; Electronics ; Engineering ; Materials science ; Electrical engineering Machine Learning in Materials Science
Authors: Huat Thart Chiang | Naomi Kern | Zachery R. Wylie | Abdul Moeez | Haoqing Zhang | Daniel McKeen | Nicholas S. M. Herringer | Oleg Gang | Andrew L. Ferguson | Zachary M. Sherman | Lilo D. Pozzo
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Assembly of small silica nanoparticles using lipid-tethered DNA ‘bonds’ Single-stranded DNA molecules modified with cholesterol functional groups are physically tethered to silica nanoparticles (diameter 25 nm) that are encapsulated in a lipid bilayer. Such tethering increases the azimuthal mobility of the DNA molecules across the nanoparticle surface and enables nonspecific bonding, eliminating the need for specialized surface chemistries (such as silane or thiol ligands). To induce assembly, double-stranded DNA 'bridge' molecules are then added with complementary nucleotides to the DNA 'anchor' molecules that are physically tethered to the lipids on the surface of the particles. Assembly is observed to occur at room temperature and without the need for temperature annealing. Using automated liquid handling tools, assemblies are created in high throughput and rapidly characterized using SAXS. It is determined that the relative concentration of DNA-to-silica and the ionic strength of the solution are important parameters that affect the resulting assembly. Analysis of SAXS data is performed using coarse-grained particle dynamics simulations. The results support the spontaneous formation of semi-crystalline particle assemblies by particle condensation, where the interparticle distance is tuned by the sequence of the DNA 'bridge' used to link the particles. Crystallinity analysis performed on the resulting simulations, optimized to match SAXS observations, suggest that particle clusters display increased crystallinity in the center of the clusters, but their maximum size remains relatively small (sub-micron) before settling occurs, which limits the extent of crystallization. Crystallinity ; Small-angle X-ray scattering ; Nanoparticle ; Particle (ecology) ; Molecule ; DNA ; Polymer ; Particle size ; Particle aggregation Mesoporous Materials and Catalysis ; Pickering emulsions and particle stabilization ; Diatoms and Algae Research