jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haitao Wu | Hao Wang | Changcheng Wang | Zhaoyang Yuan | Hu Xu | Jing Zheng | Mengjin Jiang | Jinrong Wu
Source: Macromolecules (article, 2025-05-02)
Field: Materials Science — Polymers and Plastics

Machine-Learning-Assisted Design of Mechanically Robust Room-Temperature Self-Healing Epoxy Resins Epoxy resins are the most widely used thermosets, yet they typically lack the capability to self-heal at room temperature due to their molecular chains and networks being immobilized in a glassy state. Herein, machine learning identifies fractional free volume as a crucial factor for enabling self-healing in the glassy state. Guided by this insight, we designed an epoxy network incorporating dangling chains together with numerous hydrogen bonds and aromatic disulfide bonds. The dangling chains introduce large free volume, facilitating the reorganization of hydrogen bonds and the radical-mediated exchange of aromatic disulfide bonds, thereby imparting prominent self-healing capability at room temperature. Notably, the damaged epoxy not only can recover 81.2% of its tensile strength without intervention but also can autonomously and completely eliminate electrical tree damage and scratches at room temperature. Under mild compression, 100% healing occurs within tens of minutes in the glassy state. Additionally, the optimized epoxy exhibits high physicomechanical properties with a tensile strength of 42.1 MPa, a modulus of 2.9 GPa, and a glass transition temperature of 53.2 °C. Its ability to self-heal both electrical tree and mechanical damage at room temperature positions this epoxy as a promising material for advanced insulating and sealing applications. Epoxy ; Self-healing ; Materials science ; Composite material ; Polymer chemistry ; Self-healing material ; Polymer science Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Photopolymerization techniques and applications
Authors: Qundao Xu | Meng Xu | Siqi Tang | Shaojie Yuan | Ming Xu | Wei Zhang | Xianbin Li | Zhongrui Wang | Xiangshui Miao | Chengliang Wang | Matthias Wuttig
Source: InfoMat (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Machine learning for discrimination of phase‐change chalcogenide glasses Abstract Chalcogenides, despite their versatile functionality, share a notably similar local structure in their amorphous states. Particularly in electronic phase‐change memory applications, distinguishing these glasses from neighboring compositions that do not possess memory capabilities is inherently difficult when employing traditional analytical methods. This has led to a dilemma in materials design since an atomistic view of the arrangement in the amorphous state is the key to understanding and optimizing the functionality of these glasses. To tackle this challenge, we present a machine learning (ML) approach to separate electronic phase‐change materials (ePCMs) from other chalcogenides, based upon subtle differences in the short‐range order inside the glassy phase. Leveraging the established structure–property relations in chalcogenide glasses, we select suitable features to train accurate machine learning models, even with a modestly sized dataset. The trained model accurately discerns the critical transition point between glass compositions suitable for use as ePCMs and those that are not, particularly for both GeTe–GeSe and Sb 2 Te 3 –Sb 2 Se 3 materials, in line with experiments. Furthermore, by extracting the physical knowledge that the ML model has offered, we pinpoint three pivotal structural features of amorphous chalcogenides, that is, the bond angle, packing efficiency, and the length of the fourth bond, which provide a map for materials design with the ability to “predict” and “explain”. All three of the above features point to the smaller Peierls‐like distortion and more well‐defined octahedral clusters in amorphous ePCMs than non‐ePCMs. Our study delves into the mechanisms shaping these structural attributes in amorphous ePCMs, yielding valuable insights for the AI‐powered discovery of novel materials. image Chalcogenide ; Phase (matter) ; Chalcogenide glass ; Materials science ; Phase change ; Psychology ; Artificial intelligence ; Computer science ; Engineering physics ; Optoelectronics ; Chemistry ; Engineering Phase-change materials and chalcogenides
Authors: Nicolás Fuentes-Ugarte | Martin Pereira-Silva | Isaac Cortes-Rubilar | Gabriel Vallejos | Victoria Guixé | Víctor Castro‐Fernández
Source: Biophysical Reviews (review, 2025-04-01)
Field: Materials Science — Materials Chemistry

How enzyme functions evolve: genetic, structural, and kinetic perspectives Membrane biology ; Computational biology ; Enzyme ; Biology ; Biochemical engineering ; Chemistry ; Biochemistry ; Engineering Enzyme Structure and Function ; Microbial Metabolic Engineering and Bioproduction ; Photosynthetic Processes and Mechanisms
Authors: Jiandong Cui | Yan Xia | Yingqing Yu | Hong Xu | Nan Zhang | Zhiwei Tuo | Zirui Liu | Zhaohua Lin | Suqian Ma | Yunhong Liang | Luquan Ren
Source: Acta Biomaterialia (review, 2025-01-09)
Field: Materials Science — Biomaterials

Preparation and Application of Nature-inspired High-performance Mechanical Materials Materials science ; Composite material ; Nanotechnology Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Polymer composites and self-healing
Authors: Satadeep Bhattacharjee | Pritam Das | Swetarekha Ram | Seung‐Cheol Lee
Source: ACS Applied Materials & Interfaces (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

A Hybrid Machine Learning Framework for Predicting Hydrogen Storage Capacities in Metal Hydrides: Unsupervised Feature Learning with Deep Neural Networks In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. This is a critical challenge due to the scarcity of experimental data and the complexity of high-dimensional feature spaces. Our approach employs the power of unsupervised learning through the use of a state-of-the-art autoencoder. This autoencoder is trained on elemental descriptors obtained from the Mendeleev software, enabling the extraction of a meaningful and lower-dimensional latent space from the input data. This latent representation serves as the basis for our deep multilayer perceptron (MLP) model, which consists of five layers and shows good precision in predicting hydrogen storage capacities. Furthermore, our results show very good agreement with the results obtained with density functional theory (DFT). In addition to addressing the limitations caused by limited and unevenly distributed data in the field of hydrogen storage materials, we also focus on discovering new materials that show promising opportunities for hydrogen storage. These materials were identified using both feature-based approaches and predictions generated by a large language model (LLM). A significant highlight of this work is the integration of a decoder-only LLM based on GPT-2, which is fine-tuned for materials generation for hydrogen storage. Using such an approach, we have discovered new hydrogen storage materials with a selected subset subsequently validated through density functional theory (DFT) calculations. Materials science ; Artificial neural network ; Feature (linguistics) ; Artificial intelligence ; Unsupervised learning ; Machine learning ; Deep learning ; Metal ; Computer science ; Metallurgy Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Lennart Mesecke | Ina Meyer | Marcus Oel | Roland Lachmayer
Source: International Journal of Hydrogen Energy (review, 2025-03-01)
Field: Materials Science — Materials Chemistry

Challenges and potentials for additive manufacturing of hydrogen energy components: A review Climate change necessitates the development of sustainable energy systems, with hydrogen technologies playing a key role in this transition. Additive manufacturing (AM) offers a significant potential to enhance the efficiency of hydrogen energy components and reduce their costs through rapid prototyping, design freedom, and functional integration. This review provides the first comprehensive summary of the current state of research on the application of AM processes in the production, storage, and utilization of hydrogen. It highlights various AM processes such as powder bed fusion, directed energy deposition, fused filament fabrication and stereolithography for the advancement of hydrogen energy components. Current research trends include the material development, multi-material AM, hybrid processes, and the integration of artificial intelligence and machine learning. At present, the technologies presented are mainly at a development stage of TRL 4–5. The next major step towards industrialization is the demonstration of prototypes outside the laboratory. • The AM technologies relevant to hydrogen energy components are reviewed. • Promising AM research trends include multi-materials, hybrid processes and AI. • With PBF-LB/M, catalytic reactors can be printed without the need for coating. • FFF offers great potential to reduce costs of hydrogen energy components. • Future research demand derived from the literature review is given. Hydrogen ; Hydrogen fuel ; Energy (signal processing) ; Chemistry ; Materials science ; Computer science ; Process engineering ; Physics ; Engineering ; Organic chemistry Hydrogen Storage and Materials ; Additive Manufacturing and 3D Printing Technologies ; Catalytic Processes in Materials Science
Authors: Desalegn Atalie | Mulat Alubel Abtew | Li Zhao
Source: Journal of Natural Fibers (article, 2025-09-30)
Field: Materials Science — Biomaterials

Recent Advances in Bio-Based Nonwoven Materials: Sustainable Production, Applications, and Circular Economy The increasing environmental and ecological concerns from extensive petroleum-based product use underscore the social value of developing green materials from renewable resources. Nonwoven materials provide advantages like low production costs, versatile applications in various sectors, and lightweight properties over traditional textiles. Biomaterial-based nonwovens are promising due to their inherent traits, renewability, and abundance. As sustainable alternatives to petroleum-derived materials, bio-based nonwovens from cellulose, modified cellulose, regenerated cellulose, chitosan, alginate, agricultural wastes, polylactic acid (PLA), and polyhydroxyalkanoates (PHAs) tackle challenges like plastic pollution and resource depletion via enhanced biodegradability, lower carbon footprint, and circular economy alignment. This review analyzes recent advancements in eco-friendly preparation of bio-based nonwovens and technologies, focusing on green, scalable manufacturing, performance improvement, and lifecycle sustainability. It also addresses challenges and solutions, such as machine learning design, hybrid composites, and policy frameworks, while highlighting applications in bio-compatible medical textiles, home textiles, packaging, personal protective equipment, building materials, air filtration, water purification, gas adsorption, and space technology. Finally, it explores future directions in biomaterial nonwovens, regulatory best practices, and cross-sector collaborations to promote widespread adoption for a sustainable circular economy. This work fills a literature gap by providing actionable insights for researchers, industries, and policymakers in bio-based nonwovens. Circular economy ; Polylactic acid ; Renewable resource ; Resource (disambiguation) ; Sustainable production ; Work (physics) ; Resource efficiency ; Plastic pollution ; Product (mathematics) Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Advanced Cellulose Research Studies
Authors: Hasan Eskalen | Mustafa Kavgacı | Yusuf Kavun | Hakan Yaykaşlı | Harun Gülpak | Mehmet Gök
Source: Journal of Materials Science Materials in Electronics (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

CuO nanoparticle doping in barium borate glasses: effects on density prediction via machine learning, optical, mechanical, thermal, and radiation protection properties Abstract This study synthesized CuO nanoparticles (average size 2.75 nm) via combustion and doped them into a series of melt-quenched borate glasses with the composition (70- x )B₂O₃: x CuO:10CaO:10 K₂O:5Na₂O:5BaO ( x = 0, 0.25, 0.5, 0.75, 1 mol%). CuO addition increased the thermal stability, density (from 2.513 to 2.535), and refractive index (from 2.26 to 2.35) of the glasses. The experimental density values were validated using machine learning models, with the XGB Reg. model ( R 2 = 0.986) providing the most accurate predictions. FTIR spectroscopy identified vibrational modes such as B–O–B bending (718 cm⁻ 1 ), BO₄ unit vibrations (1054 cm⁻ 1 ), and B–O stretching (1360 cm⁻ 1 ), as well as non-bridging oxygen sites influenced by CuO content. Optical analysis revealed a decrease in both direct and indirect bandgap energies, an increase in refractive index, and improved functionality as absorption bandpass filters and UV shields. Mechanical evaluation using the Makishima–Mackenzie model showed a reduction in elastic modulus values with increasing CuO content, linked to lower packing density. Thermal analysis confirmed consistent stability and glass-forming ability. Radiation shielding tests using γ-rays (384, 1173, 1333 keV) showed improved efficiency with increasing CuO, highlighting the potential for developing advanced radiation shielding materials. Materials science ; Barium ; Nanoparticle ; Boron ; Doping ; Thermal ; Radiation ; Barium borate ; Nanotechnology ; Optoelectronics ; Optics ; Metallurgy ; Meteorology ; Organic chemistry Radiation Shielding Materials Analysis ; Glass properties and applications ; Luminescence Properties of Advanced Materials
Authors: Ömer Tayfuroğlu | Abdulkadır Kocak | Yunus Zorlu
Source: Langmuir (article, 2025-03-14)
Field: Chemistry — Inorganic Chemistry

Modeling Gas Adsorption and Mechanistic Insights into Flexibility in Isoreticular Metal–Organic Frameworks Using High-Dimensional Neural Network Potentials Metal-organic frameworks (MOFs), known for their remarkable porous and well-organized structures, have found extensive use in various applications, including gas storage. Predicting the bulk properties from atomistic simulations as well as gas uptakes and the adsorption mechanism requires the most accurate definition of MOF systems. The application of ab initio molecular dynamics to these extensive periodic systems exceeds the current computational capabilities. Consequently, alternative strategies need to be devised to decrease computational costs without compromising accuracy. In this work, we construct high-dimensional neural network potentials (HDNNPs) to describe rotationally and translationally invariant energies and forces of isoreticular metal-organic framework (IRMOF) series at the density functional theory level of accuracy using a fragmentation technique to study H<sub>2</sub> and CH<sub>4</sub> adsorption isotherms by means of an "adsorption-relaxation" model in which molecular dynamics and grand canonical Monte Carlo simulations were performed simultaneously. Herein, for the first time, we report that HDNNPs could be utilized for such simulations with excellent agreement with experimental values. We also report that the UFF4MOF force field may not be suitable for adsorption-relaxation simulations. In addition, we show that the real number of CH<sub>4</sub> uptake values of IRMOF-10 under the extreme conditions could be much greater than what the classical force field predicts. Adsorption-relaxation simulations enable us to characterize the behavior of MOF atoms and the distribution of gas molecules during the adsorption process, giving the most detailed mechanistic picture. Adsorption ; Flexibility (engineering) ; Metal-organic framework ; Chemistry ; Artificial neural network ; Computer science ; Organic chemistry ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis
Authors: Wei Li | Tianhui Mu | Ze Sun | Shiyan Zhang | Yang Yu | Bi Fan | Jiaying Li | Yucheng Wang | Yupan Wu | Xuetao Gan | Shaoxi Wang
Source: ACS Applied Materials & Interfaces (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Reconfigurable Multifunctional Semifloating Gate Transistors Based on the ReSe<sub>2</sub>/h-BN/Graphene van der Waals Heterostructure In the post-Moore era, semifloating gate devices have great potential to be developed into next-generation devices for their excellent nonvolatile memory and reconfigurable logic. 2D materials have been focused due to their atomically flat surfaces, high carrier mobility, and excellent photoelectrical response. The 2D ReSe<sub>2</sub> is selected as a channel material for its ambipolar characteristic and outstanding optoelectronic response. Here, we fabricated ReSe<sub>2</sub>/h-BN/Gr multifunctional semifloating gate (MFSFG) devices, which can work as bidirectional nonvolatile reconfigurable multistate P-N and N-P homojunctions, photodetectors, and artificial synaptic, reconfigurable logical, and half-wave rectification devices. The device exhibits large rectification ratios of ∼10<sup>6</sup> (P-N) and ∼10<sup>4</sup> (N-P) with great endurance (1000 cycles) and retention (1000 s). As a photodetector, it obtains the highest responsivity and detectivity of 1.98 A W<sup>-1</sup> and 6.39 × 10<sup>12</sup> Jones (N-P) and 0.93 A W<sup>-1</sup> and 2.00 × 10<sup>12</sup> Jones (P-N), respectively, under 532 nm illumination. The synaptic plasticity is perfectly achieved, and the convolutional neural network built based on synaptic data has the highest classification recognition accuracies of 96.54 and 88.99%. The logical ″XOR″, ″XNOR″, ″NAND″, ″OR″, and half-wave rectification functions are achieved on a single device under photoelectrical hybrid regulations. The integration of these various functions into a single ReSe<sub>2</sub>/h-BN/Gr MFSFG device not only broadens the possibilities for utilizing 2D materials in multifunctional devices but also opens up new avenues for their application in neuromorphic computing and logic-in-memory chips. Materials science ; Heterojunction ; Graphene ; van der Waals force ; Transistor ; Optoelectronics ; Nanotechnology ; Electrical engineering ; Quantum mechanics ; Physics ; Molecule ; Engineering ; Voltage 2D Materials and Applications ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Stuti Shrivas | Amarish Dubey
Source: Journal of Energy Storage (article, 2025-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of specific capacitance of activated carbon electrode for energy storage device by machine learning based approach Capacitance ; Energy storage ; Activated carbon ; Supercapacitor ; Electrode ; Materials science ; Carbon fibers ; Process engineering ; Computer science ; Chemistry ; Engineering ; Composite material ; Physics ; Thermodynamics ; Adsorption Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Lu Zhang | Ri Cheng | Zuyang He | Mei Mei | Bin Wu | Weimin Tan | Bo Yan | Shangfeng Wang | Fan Zhang
Source: Nature Photonics (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Excitation-encoded single-emission shortwave infrared lanthanide fluorophore palette for real-time in vivo multispectral imaging Multispectral image ; Fluorophore ; Palette (painting) ; Materials science ; Optics ; Infrared ; Photon upconversion ; Optoelectronics ; Excitation ; Lanthanide ; Fluorescence ; Remote sensing ; Physics ; Luminescence ; Computer science ; Geology Lanthanide and Transition Metal Complexes ; Nanoplatforms for cancer theranostics ; Optical Imaging and Spectroscopy Techniques
Authors: Byung-Moon Jun | Sung Ho Chae | Deokhwan Kim | Changgil Son | Tack-Jin Kim | Seok Won Hong | Yeomin Yoon | Kangmin Chon | Hojung Rho
Source: Separation and Purification Technology (article, 2025-04-05)
Field: Chemistry — Inorganic Chemistry

Remediation of acidic effluents from Uranium-Contaminated soil using coffee Residue Biochar: A Combined experimental and Machine learning approach Uranium contamination resulting from nuclear waste disposal poses significant environmental and health risks, necessitating the development of effective remediation strategies. In this study, we investigate the use of biochar-based adsorbents derived from coffee waste for the removal of U(VI) from acidic effluent produced by real uranium-contaminated soil through acid leaching, providing a novel solution to real-world contamination. Two biochars—pristine and ZnFe-modified—were synthesized, and their U(VI) adsorption performance was evaluated through a comprehensive analysis of their physicochemical properties. The biochar’s adsorption performance was assessed under diverse experimental conditions, examining its isotherm, kinetic, and thermodynamic characteristics. Additionally, its behavior was analyzed in the presence of background cations, anions, and humic acid within a real acidic effluent containing U(VI). Despite the significantly larger surface area of ZnFe-modified biochar (1218.4 m 2 /g vs. 40.8 m 2 /g for pristine biochar), pristine biochar exhibited superior U(VI) adsorption capacity at pH 4. This enhancement was attributed to its negative surface charge, which promotes electrostatic interactions with the positively charged U(VI) ions. Conversely, the positive surface charge of ZnFe-modified biochar hindered U(VI) adsorption efficiency under similar conditions. Machine learning models, including Random Forest, XGBoost, and LightGBM, were employed to predict adsorption capacity and analyze key operational parameters. SHapley Additive exPlanations analysis identified initial uranium concentration, exposure time, and pH as the most critical factors. These findings underscore the novelty of using real uranium-contaminated soil effluent and highlight pristine biochar as an effective, sustainable material for U(VI) removal. Biochar ; Environmental remediation ; Residue (chemistry) ; Contamination ; Uranium ; Effluent ; Soil remediation ; Environmental chemistry ; Environmental science ; Waste management ; Soil contamination ; Chemistry ; Pulp and paper industry ; Environmental engineering ; Engineering ; Materials science ; Soil science ; Metallurgy ; Soil water ; Organic chemistry ; Biology ; Pyrolysis Radioactive element chemistry and processing ; Groundwater flow and contamination studies ; Radioactivity and Radon Measurements