jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jintian Lyu | Jiamin Zhong | Nan Zhou | D. Shen | Jiayi Xu | Shaolong Lin | Li Qin | Zhao Chen | Kui Du
Source: Journal of Chemical Information and Modeling (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

KPGT-Fluor: A Graph Transformer Framework for Accurate Property Prediction of Fluorescent Dyes under Different Solvent Environment Data-driven machine learning (ML) technologies have become increasingly prevalent in the prediction of the optical properties of fluorescent dyes, especially across diverse solvent environments─a key requirement for the rational design of small solvatochromic systems. Here, we introduce KPGT-Fluor, a novel adaptation of the Knowledge-guided Pretraining of Graph Transformer (KPGT) framework, designed to model solvent-dependent photophysical behavior. Through the integration of solvent molecular descriptors, KPGT-Fluor effectively captures solvent environmental effects that influence optical properties. KPGT-Fluor exhibits strong predictive performance, achieving mean absolute error (MAE) of 10.55 and 12.09 nm for absorption wavelengths (λ<sub>abs</sub>) and emission wavelengths (λ<sub>em</sub>), respectively. For the logarithm of the extinction coefficient (ε) and quantum yield (Φ), the MAE values are 0.104 and 0.081, demonstrating a high accuracy. Compared with the existing models, a comprehensive evaluation across the four key property prediction tasks shows that KPGT-Fluor exhibits a more balanced and competitive overall performance. To further demonstrate the effectiveness of the proposed framework, an external test set containing representative main ring structures was selected. Furthermore, two novel D-π-A molecules were synthesized, and their optical properties in different solvents were experimentally compared with KPGT-Fluor predictions. These results highlight KPGT-Fluor as a powerful tool for predicting and discovering solvatochromic materials. Solvatochromism ; Fluorescence ; Logarithm ; Solvent ; Molar absorptivity ; Quantum yield ; Transformer ; Wavelength ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Sensors and Ion Detection
Authors: Felipe Hawthorne | Leandro Dias Seixas | James M. Almeida | Cristiano F. Woellner | Raphael M. Tromer
Source: Open MIND (preprint, 2026-02-19)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training-set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. These insights provide a transparent physical interpretation of the learned models and establish a natural pathway toward active learning-driven exploration of complex nanoparticle configurational spaces. Interpretability ; Stability (learning theory) ; Ranking (information retrieval) ; Representation (politics) ; Curse of dimensionality ; Weighting ; Divide and conquer algorithms ; Nanoparticle ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Hoan Nguyen | Thanh-Binh Nguyen
Source: Micron (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Deep Learning segmentation with metal intrusion for quantitative microstructure analysis of hardened cement paste Quantitative microstructure analysis of hardened cement paste is critical but challenging due to the highly disordered nature of the materials. Here, we introduced an advanced approach combining metal intrusion and deep learning segmentation to enhance the microstructure analysis of the composites. First, a low-melting-point metal, Field metal, was injected into the cement samples under pressure in a vessel to enhance discrimination between pores and solid phases under backscatter electron (BSE) imaging. The subsequent segmentation of pore, unhydrated, and hydrated cement phases in BSE images of these metal-intruded hardened cement paste samples was performed using deep learning segmentation models such as Linknet and Unet. Microstructure parameter size and shape analysis were conducted for each segmented phase, including area, equivalent diameter, solidity, circularity, and aspect ratio. The results showed that segmenting the different phases in the hardened cement paste was possible using this approach. While both U-Net and LinkNet networks showed good segmentation results with mean IoU scores of 0.89 and 0.87, respectively, the U-Net outperformed with more details and complex boundaries. This segmentation simplifies the microstructure of cement composites, enabling the quantitative analysis of each phase. These detailed analyses are crucial for evaluating material behaviour and optimising the performance of cement composites. Microstructure ; Cement ; Segmentation ; Deep learning ; Intrusion ; Fiber Machine Learning in Materials Science ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Eun Bee Ko | Junho Sung | Seon Yeon Choi | Yasir Hassan | Jeong-Ju Bae | Jongseok Kim | Hyun You Kim | Eunho Lee | Min Sup Choi | Hyun Ho Kim
Source: Nano-Micro Letters (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Dipole-Driven Charge Trapping in Monolayer Janus MoSSe for Ultrathin Nonvolatile Memory Devices The continued scaling of flash memory technologies faces challenges such as limited operation speed, poor data retention, and interface defects inherent to conventional three-dimensional architectures. Two-dimensional (2D) materials, with van der Waals interfaces and atomic-scale thickness, offer a promising pathway to overcome these limitations by enabling efficient charge modulation while minimizing surface defects. In this work, a nonvolatile 2D flash memory device is developed employing monolayer Janus MoSSe as the charge-trapping layer and hexagonal boron nitride (h-BN) as an ultrathin tunneling barrier. The intrinsic structural asymmetry of Janus MoSSe induces a strong vertical dipole moment, resulting in enhanced charge trapping, deeper energy barriers, and directional polarization compared with symmetric 2D materials. Consequently, the devices exhibit outstanding retention times exceeding 10<sup>4</sup> s, endurance beyond 10<sup>4</sup> program/erase cycles, and large memory window ratios (ΔV/V<sub>G,max</sub> of 50%-70% for 10 and 6 nm h-BN, respectively), with charge-trapping rates up to 8.96 × 10<sup>14</sup> cm<sup>-2</sup> s<sup>-1</sup>. In addition, Janus MoSSe-based devices show synaptic characteristics under electrical pulses and perform recognition simulations in artificial neural networks. These findings establish a design paradigm for 2D memory devices, enabling ultrathin, flexible, and energy-efficient nonvolatile memories. Janus ; Non-volatile memory ; Monolayer ; Dipole ; Superlattice ; Charge (physics) ; Quantum tunnelling ; Heterojunction ; Trapping 2D Materials and Applications ; Neural Networks and Reservoir Computing ; Advanced Memory and Neural Computing
Authors: David Lilek | Sebnem Sara Yayla | Hana Stipanović | Thomas-Klement Fink | Jeannie Egan | Birgit Herbinger | Alexia Tischberger-Aldrian | Christian B. Schimper
Source: Applied Sciences (article, 2026-03-27)
Field: Chemistry — Analytical Chemistry

NIR Spectroscopy and Machine Learning for the Quantification of Blended Textiles: Towards Improved Understanding for Textile Recycling Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified. Partial least squares regression ; Preprocessor ; Textile ; Sorting ; Pattern recognition (psychology) ; Data pre-processing ; Near-infrared spectroscopy ; Pipeline (software) Spectroscopy and Chemometric Analyses ; Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Yang Zhou | Rui Tang | Rui Huang | Hui Dong | Weijie Lan
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

High-performance prediction of protein content in brown rice via multi-spectral fusion and deep learning: a comparative study of visible, near infrared, mid infrared spectroscopy and hyperspectral imaging Hyperspectral imaging ; Feature selection ; Convolutional neural network ; Pattern recognition (psychology) ; Fusion ; Feature (linguistics) ; Brown rice ; Principal component analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Shun Muroga | Hideaki Nakajima | Taiyo Shimizu | Kazufumi Kobashi | Kenji Hata
Source: ArXiv.org (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a general strategy for data-driven, explainable characterization of complex materials. Intersection (aeronautics) ; Cluster analysis ; Visualization ; Surface (topology) ; Artificial neural network ; Pattern recognition (psychology) ; Unsupervised learning ; Feature (linguistics) ; Feature extraction Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Nanopore and Nanochannel Transport Studies
Authors: Qi Pan | Qixuan Zhong | Jiang Pu | G. Yu | Jinxing Chen | Muhan Cao | Bo Feng
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Perovskite solar cells towards industrialization: overcoming challenges with data-driven strategies Perovskite solar cells (PSCs) represent a transformative photovoltaic technology, achieving a certified power conversion efficiency (PCE) of 27% and demonstrating the potential for low-cost manufacturing. However, their path to widespread commercialization is hindered by critical challenges in the operational stability and scalable fabrication of high-efficiency large-area modules. Traditional development cycles, reliant on trial-and-error, are too slow to address these complex, multi-faceted problems. In this context, machine learning (ML) emerges as a powerful fourth paradigm, capable of decoding non-linear structure-process-property relationships and accelerating the research and experimental development (R&D) pipeline. This review provides a unique synthesis of the PSCs' industrial landscape, analyzing the key bottlenecks of stability, scalability, and cost. It then establishes a novel framework that maps specific ML techniques from high-throughput virtual screening to process optimization as targeted solutions to these industrialization challenges. We detail how ML enables the rapid discovery of stable materials, predicts the device performance and lifetime, and optimizes manufacturing parameters, supported by emerging industrial case studies. Finally, we outline a strategic roadmap for the field, emphasizing the need for standardized data, explainable AI, and closed-loop automation to fully realize a data-driven future for PSC development and commercialization. Commercialization ; Scalability ; Transformative learning ; Photovoltaic system ; Process (computing) ; Key (lock) ; Automation ; Photovoltaics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Domain Adaptation and Few-Shot Learning
Authors: Xingliang Ji | Yajun Tian | Jinghao Zhao | Bo Wu | Yu Ch | Xuzhen Wang | Jieshan Qiu
Source: Small (article, 2026-01-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Assisted Tailoring of Pore Structures in Coal‐Derived Porous Carbons for Enhanced Performance Porous carbon materials (PCMs) have emerged as key players in energy storage and environmental remediation thanks to their highly tunable pore structure parameters. However, traditional empirical approaches for optimizing these parameters are time-consuming and resource-intensive. Herein, a workflow is introduced that integrates machine learning-assisted pore parameters prediction with experimental validation for PCMs, thereby facilitating the identification of candidate PCMs for different application requirements. Coal-based activated carbon (CAC) is first employed to validate the effectiveness of this workflow, given its high tunability and extensive application potential. During the validation process, machine learning models are developed to establish a predictive map linking precursor and preparation parameters to the resulting specific surface area and total pore volume. The proposed strategy for CAC is applied to the design of a high-performance supercapacitor electrode (specific capacitance of 491.2 F g<sup>-1</sup> at 0.1 A g<sup>-1</sup>) and a methylene blue adsorbent (adsorption capacity of 2196.8 mg g<sup>-1</sup> at room temperature), demonstrating its effectiveness. Furthermore, the workflow is extended to coal tar pitch-based carbon materials, a more complex system, and demonstrates encouraging outcomes. This work extends the strategies for controlling pore structure parameters of CAC and provides a transferable workflow for exploring other PCMs. Supercapacitor ; Workflow ; Characterization (materials science) ; Carbon fibers ; Porosity ; Capacitance ; Specific surface area ; Adsorption ; Energy storage Supercapacitor Materials and Fabrication ; Adsorption and biosorption for pollutant removal ; activated carbon and charcoal
Authors: Sejin Lee | Sohee Kim | Kihun Jeong
Source:  (article, 2026-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanocavity-enhanced SERS platform for continuous monitoring of microphysiological systems (Withdrawn by Authors) This study reports a dielectric nanocavity-based surface-enhanced Raman scattering (SERS) substrate for real-time molecular monitoring in microphysiological systems (MPS). The nanocavity SERS substrate consists of gold nanoislands (AuNIs) fabricated by thermal dewetting and a precisely engineered dielectric gap layer to achieve strong plasmonic coupling and enhanced optical field confinement. Integrated with a gut-on-a-chip model, this platform enables sensitive, time-resolved detection of biomolecules from extracellular media under continuous flow. Machine learning-based spectral analysis further improves signal interpretation, demonstrating the potential of this system for advanced molecular sensing in organ-on-a-chip applications. Dewetting ; Raman scattering ; Substrate (aquarium) ; Plasmon ; Biomolecule ; Dielectric ; Coupling (piping) ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Xinhang Jin | Aji P. Mathew
Source: ChemRxiv (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Data-Driven Property Prediction of Cellulose Nanofiber/h-BN Composites via RVE Modeling and Machine Learning Sustainable materials with high mechanical strength and thermal conductivity are essential for next-generation high-density electronics and for reducing electronic waste. Cellulose nanofiber (CNF)-based composites incorporating thermally conductive yet electrically insulating hexagonal boron nitride (h-BN) fillers represent a promising solution to this challenge. In this work, an integrated workflow combining experiments, representative volume element (RVE) modeling, and machine-learning (ML) prediction is developed to design high-performance CNF/h-BN nanopapers. A limited number of experimental datasets are used to construct numerically reliable RVE models. Based on the RVE-derived data, a random forest-based ML framework is established to predict Young's modulus and thermal conductivity for CNF/h-BN composites containing h-BN fillers with different characteristic sizes and porosity. The results reveal that porosity significantly degrades both Young's modulus and thermal conductivity, while the synergistic incorporation of h-BN fillers with different sizes enables concurrent enhancement of mechanical strength and thermal transport. Overall, this study provides a scalable, data-driven strategy for the efficient design and optimization of sustainable, multifunctional composite materials. Thermal conductivity ; Boron nitride ; Composite number ; Porosity ; Nanofiber ; Specific modulus ; Cellulose ; Thermal ; Electrical conductor Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites
Authors: Huiyu Li | Hanyi Zhang | Wanting Ma | Yuan Gao | Wen Zhou | Wei Zhang
Source: Journal of Materials Informatics (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Multiscale simulations of Ge–Sb–Se–Te phase-change alloys for photonic memory applications Phase-change materials (PCMs) are among the most promising candidates for next-generation non-volatile memory and neuromorphic computing technologies. However, their photonic applications are hindered by a trade-off between refractive index contrast and optical absorption losses. Artificial intelligence-assisted computational approaches are essential for fundamental understanding and device modeling of PCMs. In this work, we systematically investigate structural and optical properties of crystalline and amorphous Ge&lt;sub&gt;2&lt;/sub&gt;Sb&lt;sub&gt;2&lt;/sub&gt;Se&lt;sub&gt;x&lt;/sub&gt;Te&lt;sub&gt;5-&lt;/sub&gt;&lt;sub&gt;x&lt;/sub&gt; (x = 0 to 4) alloys using density functional theory (DFT), and then use the DFT-computed optical parameters for modeling and optimization of photonic computing devices via the finite-difference time-domain method. Among the investigated compositions, we identify a promising candidate, i.e., Ge&lt;sub&gt;2&lt;/sub&gt;Sb&lt;sub&gt;2&lt;/sub&gt;Se&lt;sub&gt;3&lt;/sub&gt;Te&lt;sub&gt;2&lt;/sub&gt; for all-optical switching on a silicon-on-insulator (SOI) platform. Finally, we design a dual-disk PCM waveguide structure on SOI with an enhanced switching contrast and a low optical loss for scalable photonic neural network application. Photonics ; Neuromorphic engineering ; Artificial neural network ; Scalability ; Waveguide ; Refractive index ; Absorption (acoustics) Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials ; Neural Networks and Reservoir Computing
Authors: Sizhe Qiu | Haris Saeed | Will Leonard | Feiran Li | Aidong Yang
Source: Briefings in Bioinformatics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for enzyme catalytic activity: current progress and future horizons Enzyme catalysis, with its advantages in environmental sustainability and efficiency, is gaining traction across diverse industrial applications, such as waste utilization and pharmaceutical biomanufacturing. However, optimizing enzyme catalytic activity remains a significant challenge. To facilitate enzyme mining and engineering, machine learning (ML) models have emerged to predict enzyme substrate specificity, enzyme turnover number, and enzyme catalytic optimum. This review endeavored to assist researchers in effectively utilizing predictive models for enzyme catalytic activity through presenting recent advancements and analyzing different approaches. We also pointed out existing limitations (e.g. dataset imbalance) and offered suggestions on potential enhancements to address them. We identified that the attention mechanism, inclusion of new features such as product information and temperature, and using transfer learning to leverage different datasets were three main useful modeling strategies. Furthermore, we envisaged that accurate predictors of enzyme catalytic activity would potentially transform enzyme and metabolic engineering, and the optimization of biocatalysis. Leverage (statistics) ; Substrate specificity ; Substrate (aquarium) ; Enzyme ; Sustainability Machine Learning in Materials Science ; Enzyme Catalysis and Immobilization ; Machine Learning in Bioinformatics