jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Runze Zhang | Ye Yuan | Xiaohan Wang | Xianxian Sun | Shasha Wang | Zibo Yang | Yu Ma | Enshuang Zhang | Yibin Li
Source: Chemical Engineering Journal (article, 2025-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted rapid electromagnetic design of flexible graphene-based absorptive composites Graphene ; Composite material ; Materials science ; Absorptive capacity ; Mechanical engineering ; Computer science ; Nanotechnology ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Akinshola Olabamiji Akinola | E. Prabakaran | Krishna K. Govender | Kriveshini Pillay
Source: New Journal of Chemistry (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Cetyltrimethylammonium bromide modified magnetic apricot shells for removing Congo red dye and an artificial neural network model This study focused on the synthesis of a cetyltrimethylammonium bromide-modified magnetic (Fe 3 O 4 @Ap/CTAB) nanocomposite from apricot shells and used an artificial neural network (ANN) to model the process parameters for removing Congo red (CR) dye. Chemistry ; Bromide ; Congo red ; Artificial neural network ; Chemical engineering ; Artificial intelligence ; Organic chemistry ; Adsorption Dye analysis and toxicity
Authors: Zaid Abdulhamid Alhulaybi Albin Zaid | Abdulrazak Jinadu Otaru
Source: Polymers (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Low-Heating-Rate Thermal Degradation of Date Seed Powder and HDPE Plastic: Machine Learning CDNN, MLRM, and Thermokinetic Analysis Finding reliable, sustainable, and economical methods for addressing the relentless increase in plastic production and the corresponding rise in plastic waste within terrestrial and marine environments has garnered significant attention from environmental organizations and policymakers worldwide. This study presents a comprehensive analysis of the low-heating-rate thermal degradation of high-density polyethylene (HDPE) plastic in conjunction with date seed powder (DSP), utilizing thermogravimetric analysis coupled with Fourier transform infrared spectroscopy (TGA/FTIR), machine learning convolutional deep neural networks (CDNNs), multiple linear regression model (MLRM) and thermokinetics. The TGA/FTIR experimental measurements indicated a synergistic interaction between the selected materials, facilitated by the presence of hemicellulose and cellulose in the DSP biomass. In contrast, the presence of lignin was found to hinder degradation at elevated temperatures. The application of machine learning CDNNs facilitated the formulation and training of learning algorithms, resulting in an optimized architectural composition comprising three hidden neurons and employing 27,456 epochs. This modeling approach generated predicted responses that are closely aligned with experimental results (R2 ~ 0.939) when comparing the responses from a formulated MLRM model (R2 ~ 0.818). The CDNN models were utilized to estimate interpolated thermograms, representing the limits of experimental variability and conditions, thereby highlighting temperature as the most sensitive parameter governing the degradation process. The Borchardt and Daniels (BD) model-fitting and Kissinger-Akahira-Sunose (KAS) model-free kinetic methods were employed to estimate the kinetic and thermodynamic parameters of the degradation process. This yielded activation energy estimates ranging from 40.419 to 91.010 kJ·mol⁻<sup>1</sup> and from 96.316 to 226.286 kJ·mol⁻<sup>1</sup> for the selected kinetic models, respectively, while the D2 and D3 diffusion models were identified as the preferred solid-state reaction models for the process. It is anticipated that this study will aid plastic manufacturers, environmental organizations, and policymakers in identifying energy-reducing pathways for the end-of-life thermal degradation of plastics. High-density polyethylene ; Degradation (telecommunications) ; Materials science ; Thermal ; Composite material ; Thermal analysis ; Plastic waste ; Polymer science ; Waste management ; Polyethylene ; Engineering ; Thermodynamics ; Physics Thermal and Kinetic Analysis ; Thermography and Photoacoustic Techniques
Authors: Mahima Samanth | Pavan Hiremath | G. Divya Deepak | Nithesh Naik | H S Arunkumar | Srinivas Shenoy Heckadka | R. C. Shivamurthy
Source: Journal of Composites Science (article, 2025-03-06)
Field: Materials Science — Polymers and Plastics

Sustainable Composites from Sugarcane Bagasse Fibers and Bio-Based Epoxy with Insights into Wear Performance, Thermal Stability, and Machine Learning Predictive Modeling The global push for sustainable materials has intensified the research on natural fiber-reinforced composites. This study investigates the potential of sugarcane bagasse fibers, combined with a bio-based epoxy matrix, as a sustainable alternative for high-performance composites. A comprehensive approach was adopted, including wear testing, thermal and structural characterization, and machine learning predictive modeling. Ethylene dichloride-treated fibers exhibited the lowest wear rate (0.245 mg/m) and the highest thermal stability (T20% = 260 °C, char yield = 1.3 mg), highlighting the role of optimized surface modifications. XRD (X-ray diffraction) analysis revealed that pre-treated fibers achieved the highest crystallinity index of 62%, underscoring the importance of structural alignment in fiber-matrix bonding. Machine learning insights using a Random Forest model identified fiber treatment as the most significant parameter influencing wear performance, with accurate predictions validated through experimental results. This work demonstrates the transformative potential of sugarcane bagasse fibers in sustainable polymer composites, offering a pathway for environmentally friendly, lightweight, and durable material solutions. These findings integrate experimental rigor with computational insights, paving the way for advancements in natural fiber-based composite technologies. Bagasse ; Materials science ; Composite material ; Epoxy ; Fiber ; Composite number ; Natural fiber ; Thermal stability ; Response surface methodology ; Crystallinity ; Pulp and paper industry ; Computer science ; Machine learning ; Chemical engineering ; Engineering Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Mechanical Engineering and Vibrations Research
Authors: Chen Yanchun | Jinyuan Sun | Kelun Shi | Tong Zhu | Rui Feng Li | Ruiqiao Li | Xiaomeng Liu | Xinying Xie | Chao Ding | Wen‐Chao Geng | Jinwei Ren | Wenyu Shi | Yinglu Cui | Bian Wu
Source: Science (article, 2025-10-30)
Field: Materials Science — Polymers and Plastics

Glycolysis-compatible urethanases for polyurethane recycling Recycling thermoset polyurethanes is hindered by their cross-linked structures and chemically stable urethane bonds. Although chemo-enzymatic approaches offer promise, known urethanases remain inefficient under industrial glycolysis conditions. Here, we present GRASE [graph neural network (GNN)-based recommendation of active and stable enzymes], a GNN-based framework that integrates self-supervised and supervised learning to identify efficient, glycolysis-compatible urethanases. Among these, <i>Ab</i>PURase exhibited two orders of magnitude greater activity than previously known enzymes in 6 molar diethylene glycol, enabling near-complete depolymerization of commercial polyurethane at kilogram scale within 8 hours. Structural analysis revealed that a tightly packed hydrophobic core and proline-stabilized lid loop may confer <i>Ab</i>PURase's stability and efficiency in harsh solvents. This work highlights how deep learning accelerates the discovery of biocatalysts with industrial potential and addresses a critical barrier in polyurethane recycling. Polyurethane ; Depolymerization ; Thermosetting polymer ; Polyol ; Work (physics) ; Scale (ratio) ; Suberin Polymer composites and self-healing ; Carbon dioxide utilization in catalysis ; biodegradable polymer synthesis and properties
Authors: Xu Li | Haoyu Yang | Tao Yong-hong | Qingji Wang | Tongfei Shi | Lin Li
Source: Macromolecules (article, 2025-02-25)
Field: Materials Science — Polymers and Plastics

Interpretable Machine Learning Combined TD-DFT Calculations for the Study of Colorless Transparency Polyimides The application of polyimides (PIs) is extensive in flexible electronics, artificial intelligence, and chip technology. However, the relationship between the structure of PIs and their colorless transparency remains unclear. In this study, we established a data set to investigate the colorless transparency of PIs by combining machine learning (ML) with time-dependent density functional theory (TD-DFT) calculations. The quantitative structure–activity relationship (QSAR) models were constructed to represent the colorless transparency of PIs, using 46 molecular descriptors derived from TD-DFT calculations. Comparing various ML algorithms, gradient boosting (GB), and support vector regression (SVR) models exhibited optimal performance for the colorlessness and transparency of PIs, respectively. Pearson correlation coefficient and SHAP model were employed to illustrate the role of significant descriptors in determining the colorlessness and transparency of PIs. Furthermore, the key substructures serving as ten diamine units were identified based on the data-driven analysis of the target properties of PIs. The research approach will provide theoretical guidance for the targeted synthesis of colorless and transparent PIs and introduce an innovative concept for the design of novel materials. Transparency (behavior) ; Materials science ; Computational chemistry ; Computer science ; Artificial intelligence ; Chemistry ; Polymer science Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Photonic and Optical Devices
Authors: Jian Lin Su | Zi Xuan Cai | Yiqian Mao | Long Chen | Xin Yu | Zhi Cai Yu | Qian Ma | Si Qi Huang | Jianan Zhang | Jian Wei You | Tie Jun Cui
Source: Advanced Science (article, 2025-04-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi‐Dimensional Multiplexed Metasurface for Multifunctional Near‐Field Modulation by Physics‐Driven Intelligent Design Metasurface is a revolutionary platform to achieve desired properties by artificially engineering meta-atom's arrangements. However, the explosively expanding design space of advanced metasurfaces with multiple degrees of freedom (MDOF) has made the traditional human-guided design methods increasingly ineffective, limiting the development of the metasurfaces. Intelligent design methods have been presented to tackle these challenges by introducing innovative computational models, but they are predominantly data-driven and faced the issues of data scarcity, poor physical interpretability, and weak generalization capability. Here, a physics-driven intelligent design (PDID) paradigm is proposed and demonstrates its application to design MDOF multiplexed metasurfaces. The PDID method integrates the physical prior knowledge into a deep neural network, thereby enhancing its physical interpretability and reducing its reliance on extensive databases. Compared to the traditional intelligent designs, this can reduce both design time and database size by two orders of magnitude. Through experimental validation of MDOF multiplexed metasurfaces, the versatility and computational efficiency of PDID are showed. This method not only presents a novel intelligent design tool but also exemplifies the integration of physical knowledge with machine learning to address the challenges. Its interdisciplinary insights offer significant potentials for innovative applications across the materials science, computational science, and information technology. Interpretability ; Computer science ; Multiplexing ; Field (mathematics) ; Limiting ; Systems engineering ; Computer engineering ; Artificial neural network ; Computer architecture ; Artificial intelligence ; Distributed computing ; Engineering ; Mechanical engineering ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Polydiacetylene-based materials and applications
Authors: Dong Han | Xiaogang Li | Libo Zhang
Source: Coordination Chemistry Reviews (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Recent advancements for defect engineering in nanozymes Nanozymes, a class of artificial enzymes with catalytic functions similar to natural enzymes, have garnered attention due to their adjustable catalytic activity and high stability. The need for highly active, specific, and robust nanozymes has driven research into defect engineering, as defects in nanomaterials have been verified to significantly enhance catalytic performances. Defect engineering, by modulating the crystal and electronic structures of nanomaterials, generates a multitude of coordinatively unsaturated active sites, thereby improving the catalytic efficiency of nanozymes, and has become a research hotspot in the field of materials science. This review delves into the application of defect engineering in nanozymes, examines its impact on performance, and explores the applications of machine learning in the latest advancements in the field. The integration of defect engineering with nanozymes is expected to unlock new possibilities in the field. • The classification and properties of defect engineering are illustrated. • The mechanisms and kinetics by which defect engineering enhances the catalytic activity of nanozymes are elucidated. • Recent advances of defective nanozymes in the machine learning and biomedical fields were reviewed. • The challenges faced in the development of defect-engineered nanozymes are summarized. Chemistry ; Nanotechnology Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Mengfan Wu | Shiyi Zhang | Jie Ren
Source: APL Materials (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

AI-empowered digital design of zeolites: Progress, challenges, and perspectives The rise of artificial intelligence (AI) as a powerful research tool in materials science has been extensively acknowledged. Particularly, exploring zeolites with target properties is of vital significance for industrial applications, integrating AI technologies into zeolite design undoubtedly brings immense promise for the advancements in this field. Here, we provide a comprehensive review in the AI-empowered digital design of zeolites. It showcases the state-of-the-art progress in predicting zeolite-related properties, employing machine learning potentials for zeolite simulations, using generative models for the inverse design, and aiding the experimental synthesis of zeolites. The challenges and perspectives are also discussed, emphasizing the new opportunities at the intersection of AI technologies and zeolites. This review is expected to offer crucial guidance for advancing innovations in materials science through AI in the future. Materials science ; Systems engineering ; Nanotechnology ; Engineering physics ; Engineering ethics ; Process engineering ; Engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Weiting Chen | Lingwei Ma | Yiran Li | Dequan Wu | Kun Zhou | Jinke Wang | Zhibin Chen | Xin Guo | Zongbao Li | Thee Chowwanonthapunya | Xiaogang Li | Dawei Zhang
Source: npj Materials Degradation (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating barrier performance, to accurately evaluate the corrosion degradation of coatings. In the first stage, a 1-year outdoor exposure experiment of polyurethane coatings was conducted in nine representative climatic environments. A semi-supervised collaborative training regression model is established between key environmental data and physical properties of coatings (i.e., glossiness, adhesion, water contact angle, and yellowness). In the second stage, using the predicted physical property data as inputs, a machine learning model is constructed that links physical properties to the barrier performance of coatings and develops binary classification models that can distinguish between intact and damaged coatings. This two-stage modeling strategy provides enhanced prediction accuracy and scientific interpretability by incorporating intermediate physical property parameters. Physical property ; Stage (stratigraphy) ; Degradation (telecommunications) ; Corrosion ; Coating ; Property (philosophy) ; Materials science ; Computer science ; Environmental science ; Artificial intelligence ; Metallurgy ; Composite material ; Geology Corrosion Behavior and Inhibition ; Infrastructure Maintenance and Monitoring ; Concrete Corrosion and Durability
Authors: Zihan Pengmei | Chatipat Lorpaiboon | Spencer C. Guo | Jonathan Weare | Aaron R. Dinner
Source: The Journal of Chemical Physics (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neural networks (GNNs) are used as universal geometric featurizers. By pretraining equivariant GNNs on a large dataset of molecular conformations with a self-supervised denoising objective, we obtain transferable structural representations that are useful for learning conformational dynamics without further fine-tuning. We show how the learned GNN representations can capture interpretable relationships between structural units (tokens) by combining them with expressive token mixers. Importantly, decoupling training the GNNs from training for downstream tasks enables analysis of larger molecular graphs (such as small proteins at all-atom resolution) with limited computational resources. In these ways, geom2vec eliminates the need for manual feature selection and increases the robustness of simulation analyses. Computer science ; Decoupling (probability) ; Security token ; Artificial intelligence ; Graph ; Inference ; Robustness (evolution) ; Machine learning ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Chenglei He | Liya Yu | Yun Jiang | Lan Xie | Xiaoping Mai | Ai Peng | Bai Xue
Source: Journal of Colloid and Interface Science (article, 2025-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks Electromagnetic shielding ; Composite number ; Fusion ; Interference (communication) ; Materials science ; Artificial neural network ; Electromagnetic interference ; Composite material ; Nanotechnology ; Artificial intelligence ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Internet of Things and Social Network Interactions ; Environmental Engineering and Cultural Studies
Authors: Rafael F. Veríssimo | Pedro Henrique Ferreira Matias | Mateus Rodrigues Barbosa | Flávio O. Sanches-Neto | Brenno A. D. Neto | Heibbe C. B. de Oliveira
Source: Journal of Chemical Information and Modeling (article, 2025-04-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrating Machine Learning and SHAP Analysis to Advance the Rational Design of Benzothiadiazole Derivatives with Tailored Photophysical Properties 2,1,3-Benzothiadiazole (BTD) derivatives show promise in advanced photophysical applications, but designing molecules with optimal desired properties remains challenging due to complex structure-property relationships. Existing computational methods have a high cost when predicting precise photophysical characteristics. Machine learning with Morgan fingerprints was employed to forecast BTD derivative maximum absorption and emission wavelengths. Three flavors of machine learning models were applied, namely, Random Forest, LigthGBM, and XGBoost. Random forest achieved <i>R</i><sup>2</sup> values of 0.92 for absorption and 0.89 for emission, validated internally with 10-fold cross-validations and externally with recent experimental data. SHapley Additive exPlanations (SHAP) analysis revealed critical design insights, highlighting the tertiary amine presence and solvent polarity as key drivers of red-shifted emissions. By the development of a web-based predictive tool, the potential of machine learning to accelerate molecular design is demonstrated, providing researchers a powerful approach to engineer BTD derivatives with enhanced photophysical properties. Rational design ; Computer science ; Nanotechnology ; Materials science Nonlinear Optical Materials Research ; Photochromic and Fluorescence Chemistry ; Computational Drug Discovery Methods
Authors: Yingzhong Huo | Ruoxuan Guo | Wuping Yao | Liping Song | Hong Lu | Yang Liu | Yuejie Ai | Xiangke Wang
Source: Advanced Functional Materials (article, 2025-03-20)
Field: Chemistry — Inorganic Chemistry

Modulating Polar Heterogeneity and Spatial Effects for Ultra‐High ReO <sub>4</sub> <sup>−</sup> / <sup>99</sup> TcO <sub>4</sub> <sup>−</sup> Capture in Extremely Acidic and Alkaline Conditions Abstract The selective capture of 99 TcO 4 − in extremely acidic and alkaline conditions remains a long‐standing challenge in nuclear waste treatment. Herein, a strategy is proposed to rationally modulate polar substituents and spatial effects, resulting in the development of three ultra‐stable cationic polymers, designated as V‐P, V‐3B, and V‐YX. These polar‐heterogeneous materials consistently achieve removal efficiencies exceeding 98.6% across a broad pH range (1–11), with saturated adsorption capacities of 1115.0, 655.1, and 871.4 mg g −1 for ReO 4 − (a nonradioactive surrogate for 99 TcO 4 − ), respectively. The phosphonium‐functionalized V‐P demonstrates exceptional suitability for capturing ReO 4 − from extremely acidic solutions and achieves a record‐high capacity of 204.8 mg g −1 in long‐term dynamic column experiments. Through the adjacent steric hindrance and unique electron‐withdrawing effects in V‐3B and V‐YX, the alkaline stability of the imidazolium‐N + moiety is significantly enhanced, culminating in extraordinary ReO 4 − removal of ≈100% in 3 M NaOH solutions. In breakthrough experiments of simulated Beishan groundwater, V‐3B and V‐YX demonstrate dynamic capacities of 64.6 and 62.0 mg g −1 , respectively. Molecular dynamics simulations and machine learning analyses further elucidate host‐guest interaction mechanisms and the critical role of polar heterogeneity. These findings offer innovative and universal strategies for designing and screening materials tailored for radionuclide decontamination. Materials science ; Polar ; Analytical Chemistry (journal) ; Crystallography ; Physics ; Organic chemistry ; Chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Metal-Organic Frameworks: Synthesis and Applications