jablonkagroup/rise_ai_scientists huggingface dataset:
Source: International Journal of Science and Innovation (article, 2025-01-01)
Field: Materials Science — Biomaterials

In Silico Development of EGFRvIII-Targeted Aptamer for Fluorescence Imaging of Glioma The most prevalent and deadly type of malignant brain tumor is glioma.It has been established that one of the most significant indicators of overall survival, progression-free survival, and neurological prognosis is the extent of resection.Maximum safe resection therefore becomes crucial.Thus, there is an urgent need for a method that can identify the tumor's border in order to achieve maximal safe excision while safeguarding the healthy brain tissues.Recently, a nanoprobe tagged with quantum dots (QDs) has been designed to specifically bind to tumor cells through aptamers.These aptamers attach to the overexpressed EGFRvIII (epidermal growth factor receptor variation) in glioma cells.Our theory is that these aptamers' strong binding and high activity are caused by their ability to bind to the receptor's active site, which allows them to inhibit the receptor's function.In the present study, we have investigated how these aptamers interact with EGDRvIII receptors using molecular docking simulations.Additionally, we employed artificial intelligence (AI) techniques such as graph neural networks (GNN) and machine learning (ML) to corroborate our findings and elucidate the workings of aptamers.Our findings indicate that the aptamer binds firmly to the receptor's active site.In order to improve the current aptamers, we have also executed changes in the aptamer sequences.As a result, we discovered that E-M4 created binds to the protein's active site.In conclusion, the current study will contribute to the development of aptamers targeted at glioma detection and treatments, as well as to our understanding of the binding mechanism of these aptamers to the EGFRvIII receptor. Aptamer ; In silico ; Glioma ; Fluorescence ; Fluorescence-lifetime imaging microscopy ; Computational biology ; Chemistry ; Nanotechnology ; Cancer research ; Biology ; Molecular biology ; Materials science ; Biochemistry ; Physics ; Gene ; Optics Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Nathan J. Meigs | Luther W. McDonald | Tara Mastren
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Forensic Implications of Steam Reduction Morphologies in Uranium Oxide Samples Magnesium ; Uranyl ; Oxide ; Ammonium carbonate ; Fluorite ; Sodium carbonate ; Impurity ; Sodium Nuclear Materials and Properties ; Radioactive contamination and transfer ; Radioactive element chemistry and processing
Authors: M. Vignesh | K Vasugi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning-Based Prediction of Tensile Strength and Cellulose Content in Potassium Hydroxide - Treated Sisal Fiber: A Synergistic Approach Ultimate tensile strength ; SISAL ; Potassium hydroxide ; Cellulose ; Fiber ; Potassium ; Cellulose fiber ; Materials science ; Composite material ; Hydroxide ; Chemistry ; Organic chemistry ; Metallurgy Textile materials and evaluations ; Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites
Authors: Alicja K. Surowiak | Daniel Jan Strub | Lucyna Balcerzak | Kinga Baberowska | Zuzanna Bacińska
Source: Wiadomości Chemiczne (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

NATURALNE I SYNTETYCZNE SUBSTANCJE ZAPACHOWE - FUNKCJONALNE SUROWCE DLA PRZEMYSŁU This article provides a comprehensive overview of natural and synthetic fragrance substances and their functional significance across multiple industrial sectors. It discusses the role of aroma compounds in cosmetics, food production, household chemicals, and therapeutic applications, highlighting their contribution to sensory perception and consumer experience. The article explores the main types of fragrance materials, including essential oils and solvent extracts, along with the benefits and challenges associated with their production. A separate focus is placed on synthetic fragrances, detailing both their historical development and current industrial relevance. Importantly, the article addresses the growing application of artificial intelligence (AI) and machine learning (ML) in the design, prediction, and personalization of fragrance and flavor compounds. Regulatory frameworks related to fragrance safety and labeling in the European Union are also reviewed. The paper emphasizes the need for interdisciplinary research to support sustainable innovation, consumersafety, and environmental responsibility in the evolving fragrance industry. Physics Corrosion Behavior and Inhibition ; Analytical chemistry methods development
Authors: Youssef Jabari | Imad El Adraoui | Hassan Gziri
Source: International Review of Mechanical Engineering (IREME) (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

An Experimental Approach for Evaluating the Impact of Chemical Addition Elements in Cement on the Wear Degradation of Rollers in a Vertical Roller Mills (VRM) In the heavy industry of cement, Vertical Roller Mills (VRMs) stand out for their reliability, efficiency, and adaptability across various segments, offering solutions that not only reduce energy consumption but also increase throughput and material recovery rates. The modular approach ensures long-term operational stability with lower maintenance requirements. The wear-induced deterioration in VRMs generates an immense challenge to ensure the reliability and efficiency of cement production operations. This study examines the causal link involving cement composition, mechanical wear, and predictive maintenance measures to manage production and keep effective costs at moderate levels by controlling and then reducing roller and track wear. The abrasiveness, which affects wear rates, is greatly influenced by chemical and physical qualities, including silica concentration, slag additions, and particle size. Advanced techniques, such as chemical and particle size measurements, integrated with physical intervention or developed by machine learning models, offer predictive insights into wear behaviors and improve the optimization of preventative maintenance. The purpose is to establish a causal link between the wear and specific wear of the roller and track, and all chemical components of grinding material, taking into consideration all their intrinsic parameters, in order to identify suitable correlations for ensuring the most effective performance of the VRM. Cement ; Comminution ; Reliability (semiconductor) ; Adaptability ; Energy consumption ; Production (economics) ; Grinding ; Preventive maintenance Metal Alloys Wear and Properties ; Mineral Processing and Grinding ; Tunneling and Rock Mechanics
Authors: Haoran Lin (496312) | Ming Wu (238132) | Zihang Zhao (20505904) | Fengyi Zhang (805423) | Chengye Zhou (22101605) | Disheng Yang (3155586) | Yikai Fu (16871322) | Keying Feng (19851836) | Lijun Liang (423330)
Source: Figshare (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Design of High-PerformanceMXene-Based 2D Membranesfor Desalination via Machine Learning and Hybrid Optimization Algorithms The global scarcity of freshwater resources has intensified the demand for efficient seawater desalination technologies. However, conventional approaches, such as reverse osmosis and nanofiltration, often suffer from high energy consumption and limited membrane performance. MXene, a promising two-dimensional (2D) material, offers unique structural and surface chemical properties that can enhance membrane-based separations. This study presents an integrated inverse design framework that combines machine learning (ML), optimization algorithms, and molecular dynamics (MD) simulations to accelerate the development of high-performance MXene membranes. A dual-target predictive model based on XGBoost was constructed to estimate the water flux and salt rejection, identifying surface charge density and pore area as key governing parameters. A range of intelligent optimization strategies was used to design the MXene membrane with a high desalination performance. Especially, the hybrid optimization methods showed clear advantages over baseline algorithms, delivering faster convergence and obtaining an optimized MXene membrane. MD simulations validated the predicted performance of optimized structures, showing good agreement with the prediction results of the ML model. The optimal configuration of Ti<sub>3</sub>C<sub>2</sub>O<sub>2</sub>with a charge coefficient of 1.1 and a pore area of 82.72 Å<sup>2</sup>achieved an excellent desalination performance with water permeability and salt rejection. This work demonstrates the power of integrating artificial intelligence and molecular modeling for the rational design of desalination membranes. The proposed framework provides a generalizable and scalable approach for advancing the intelligent development of 2D materials in water treatment applications. Desalination ; Scalability ; Reverse osmosis ; Capacitive deionization ; Optimization problem ; Nanofiltration ; Convergence (economics) MXene and MAX Phase Materials ; Membrane Separation Technologies ; Nanopore and Nanochannel Transport Studies
Authors: Dongke Chen (674151) | Han Cai (3961724) | Xiaoyu Xuan (8349366) | Zhili Hu (2629759) | Yang Lu (59885) | Wanlin Guo (1279932) | Zhuhua Zhang (1279929)
Source: Figshare (article, 2025-08-02)
Field: Materials Science — Materials Chemistry

Data-DrivenDescription of the Lattice Thermal Conductivityof Two-Dimensional Materials Two-dimensional (2D) materials hold great promise for advanced thermal management due to their unique phonon transport properties, but 2D semiconductors with a lattice thermal conductivity (κ<sub>L</sub>) of more than 10 W/mK remain scarce. Using high-throughput computation and first-principles calculations, we identify 18 2D materials with room-temperature κ<sub>L</sub> values exceeding 20 W/mK. Our analysis reveals a low mean atomic mass, a high Young’s modulus, and small surface corrugation as critical descriptors for enhanced κ<sub>L</sub> values in 2D materials. We further developed a machine learning-assisted model predicting a series of new 2D materials with κ<sub>L</sub> values exceeding 300 W/mK. Notably, a C<sub>2</sub>N<sub>2</sub> monolayer is predicted to exhibit a high room-temperature κ<sub>L</sub> of 1300 W/mK and a wide bandgap of 5.19 eV, while a B<sub>4</sub>C<sub>4</sub> monolayer achieves a balanced κ<sub>L</sub> of 574 W/mK and a bandgap of 0.98 eV. These findings offer robust guidance for evaluating and designing the κ<sub>L</sub> of 2D materials for effective thermal management in nanodevices. Thermal conductivity ; Monolayer ; Phonon ; Semiconductor ; Lattice (music) ; Band gap ; Thermal Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Mangalassery, Neethu Mohan | Singh, Abhishek Kumar
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Edge-Aware Graph Attention Model for Structural Optimization of High Entropy Carbides Predicting relaxed atomic structures of chemically complex materials remains a major computational challenge, particularly for high-entropy systems where traditional first-principles methods become prohibitively expensive. We introduce the edge-aware graph attention model, a physics-informed graph neural network tailored for predicting relaxed atomic structures of high-entropy systems. the edge-aware graph attention model employs chemically and geometrically informed descriptors that capture both atomic properties and local structural environments. To effectively capture atomic interactions, our model integrates a multi-head self-attention mechanism that adaptively weighs neighbouring atoms using both node and edge features. This edge-aware attention framework learn complex chemical and structural relationships independent of global orientation or position. We trained and evaluated the edge-aware GAT model on a dataset of carbide systems, spanning binary to high-entropy carbide compositions, and demonstrated its accuracy, convergence efficiency, and transferability. The architecture is lightweight, with a very low computational footprint, making it highly suitable for large-scale materials screening. By providing invariance to rigid-body transformations and leveraging domain-informed attention mechanisms, our model delivers a fast, scalable, and cost-effective alternative to DFT, enabling accelerated discovery and screening of entropy-stabilised materials. Graph ; Binary number ; Carbide ; Artificial neural network ; Complex system ; Entropy (arrow of time) ; Convergence (economics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Graph Neural Networks
Authors: Rei Oshima | Mikito Fujinami | Yuya Nakajima | Hiromi Nakai
Source: Journal of Computational Chemistry (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Large‐Scale Calculations by Integrating the Fragmentation Approach With Neural Network Potentials A fragmentation method is introduced to enable large-scale molecular simulations using neural network potentials (NNPs). The method partitions a system into cube-shaped fragments and reconstructs the total energy using a many-body expansion formalism with a distance-based cut-off approximation. Validation with Au, NaCl, diamond, H<sub>2</sub>O, and graphite crystals demonstrated that including three-body interactions with 26 neighboring fragments reduces per-atom energy error to within 0.04 eV. This approach enables simulations of systems with up to 1 million atoms, surpassing conventional NNP limits. The scaling exponent for three-body calculations remains below 1.64, suggesting feasibility for even larger-scale applications. Scaling ; Diamond ; Formalism (music) ; Exponent ; Fragmentation (computing) ; Statistical physics ; Cube (algebra) ; Artificial neural network ; Physics ; Computational physics ; Computer science ; Algorithm ; Materials science ; Mathematics ; Geometry ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Chen Chen | Mingyang Li | Pengbo Ji | Wentao Liu | Guannan Ren
Source:  (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Research on Optimization Method of Process Parameters for Small Quantity Samples In recent years, data-based information learning methods are gradually being integrated into the research and development process of materials, especially the use of machine learning and optimization algorithms to mine hidden rules in data, and accelerate the development of new materials with excellent performance. The researchers found that a past study of the welding process of nuclear fuel elements did not use the orthogonal table correctly, and while the product test met the requirements, there may be process parameters that could achieve the same or even better results. In this study, machine learning method was used to explore and optimize the process parameters of small sample size. Process (computing) ; Table (database) ; Sample (material) ; Product (mathematics) ; Process optimization ; New product development Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Machine Learning and ELM
Authors: Ashutosh Pandey | Justin Jetter | Hanlin Gu | Eckhard Quandt | Richard D. James
Source: arXiv (Cornell University) (preprint, 2025-04-02)
Field: Materials Science — Materials Chemistry

Composition Design of Shape Memory Ceramics based on Gaussian Processes We present a Gaussian process machine learning model to predict the transformation temperature and lattice parameters of ZrO$_2$-based ceramics. Our overall goal is to search for a shape memory ceramic with a reversible transformation and low hysteresis. The identification of a new low hysteresis composition is based on design criteria that have been successful in metal alloys: (1) $λ_2 = 1$, where $λ_2$ is the middle eigenvalue of the transformation stretch tensor, (2) minimizing the max$|q(f)|$, which measures the deviation from satisfying the cofactor conditions, (3) high transformation temperature, (4) low transformational volume change, and (5) solid solubility. We generate many synthetic compositions, and identify a promising composition, 31.75Zr-37.75Hf-14.5Y-14.5Ta-1.5Er, which closely satisfies all the design criteria based on predictions from machine learning. However, differential thermal analysis reveals a relatively high thermal hysteresis of 137°C for this composition, indicating that the proposed design criteria are not universally applicable to all ZrO$_2$-based ceramics. We also explore reducing tetragonality of the austenite phase by addition of Er$_2$O$_3$. The idea is to tune the lattice parameters of austenite phase towards a cubic structure will increase the number of martensite variants, thus, allowing more flexibility for them to accommodate high strain during transformation. We find the effect of Er$_2$O$_3$ on tetragonality is weak due to limited solubility. We conclude that a more effective dopant is needed to achieve significant tetragonality reduction. Overall, Gaussian process machine learning models are shown to be highly useful for prediction of compositions and lattice parameters, but the discovery of low hysteresis ceramic materials apparently involves other factors not relevant to phase transformations in metals. Austenite ; Shape-memory alloy ; Ceramic ; Hysteresis ; Martensite ; Lattice (music) ; Gaussian ; Thermal Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; High Entropy Alloys Studies
Authors: Kirill Trunov | Yu. Yu. Lebedinskiǐ | Ilya A. Zavidovskiy | Sergey M. Novikov | Alexander Morozov | Petr Shvets | Ksenia Maksimova | A. Zenkevich | Anton Khanas
Source: ArXiv.org (preprint, 2025-11-08)
Field: Materials Science — Polymers and Plastics

Targeted synthesis of polycrystalline vanadium dioxide thin films via post-deposition annealing Implementation of neuromorphic hardware is a promising way to improve the computing efficiency and decrease the energy consumption of artificial neural networks. For this purpose, electronic elements emulating the behavior of synapses and neurons have to be developed. In order to realize electronic artificial neurons, threshold resistive switches or memristors can be efficiently used. One of the most widespread materials for threshold switches is vanadium dioxide due to its property to demonstrate the metal-insulator transition at a temperature about 70 °C. However, the processes of VO$_{2}$ synthesis are quite restrictive in temperature and gas atmosphere conditions, which hinders its integration into CMOS fabrication. In this work, we propose a new method of VO$_{2}$ synthesis: reactive pulsed laser deposition from metallic V target in oxygen atmosphere at room temperature, followed by vacuum annealing. Our method enables target synthesis of an appropriate VO$_{2}$ phase in a polycrystalline thin film form by finely tuning oxygen pressure during room temperature deposition, which allows to relax the equipment demands, such as high temperature heating in oxygen. Successful targeted VO$_{2}$ synthesis under fabrication conditions close to back-end-of-line CMOS production, achieved in this work, show the way toward its large-scale microelectronic integration for neuromorphic hardware creation. Vanadium dioxide ; Thin film ; Microelectronics ; Neuromorphic engineering ; Fabrication ; CMOS ; Annealing (glass) ; Resistive touchscreen Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Magnetic properties of thin films
Authors: Dongke Chen | Han Cai | Xiaoyu Xuan | Zhili Hu | Yang Lü | Wanlin Guo | Zhuhua Zhang
Source: The Journal of Physical Chemistry Letters (article, 2025-08-02)
Field: Materials Science — Materials Chemistry

Data-Driven Description of the Lattice Thermal Conductivity of Two-Dimensional Materials Two-dimensional (2D) materials hold great promise for advanced thermal management due to their unique phonon transport properties, but 2D semiconductors with a lattice thermal conductivity (κ<sub>L</sub>) of more than 10 W/mK remain scarce. Using high-throughput computation and first-principles calculations, we identify 18 2D materials with room-temperature κ<sub>L</sub> values exceeding 20 W/mK. Our analysis reveals a low mean atomic mass, a high Young's modulus, and small surface corrugation as critical descriptors for enhanced κ<sub>L</sub> values in 2D materials. We further developed a machine learning-assisted model predicting a series of new 2D materials with κ<sub>L</sub> values exceeding 300 W/mK. Notably, a C<sub>2</sub>N<sub>2</sub> monolayer is predicted to exhibit a high room-temperature κ<sub>L</sub> of 1300 W/mK and a wide bandgap of 5.19 eV, while a B<sub>4</sub>C<sub>4</sub> monolayer achieves a balanced κ<sub>L</sub> of 574 W/mK and a bandgap of 0.98 eV. These findings offer robust guidance for evaluating and designing the κ<sub>L</sub> of 2D materials for effective thermal management in nanodevices. Thermal conductivity ; Materials science ; Lattice (music) ; Condensed matter physics ; Statistical physics ; Physics ; Composite material Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: João Morado | Kirill Zinovjev | Lester O. Hedges | Daniel J. Cole | Julien Michel
Source: ChemRxiv (preprint, 2025-06-20)
Field: Materials Science — Materials Chemistry

Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations Hybrid ML/MM approaches that combine machine learning (ML) potentials with molecular mechanics (MM) potentials offer a promising balance between computational cost and accuracy. Most ML/MM simulations reported to date employ mechanical embedding schemes, and rely on Lennard-Jones and Coulomb potentials to model intermolecular interactions between the ML and MM regions. A promising approach to improving ML/MM schemes is to use electrostatic embedding, where polarization effects on the ML region by the MM region are explicitly incorporated. The electrostatic machine learning embedding (EMLE) method has been developed for this purpose. Here, we compute absolute hydration free energies for a set of small organic molecules to derive robust methodologies for training EMLE models using quantum mechanical data. We establish protocols for fine-tuning the static and induced components of electrostatic interactions and evaluate the accuracy limits of fitting these components to first-principles calculations. We also introduce an empirical adjustment to enhance agreement with experimental results, strengthening the competitiveness of ML/MM simulations relative to state-of-the-art methods. Overall, our findings provide valuable insights into the challenges and opportunities of electrostatic embedding ML/MM simulations, and offer strategies for achieving robust modelling of classes of drug-like molecules where the accuracy of conventional MM force fields fall short. Embedding ; Electrostatics ; Coulomb ; Computer science ; Electric potential energy ; Intermolecular force ; Quantum ; Statistical physics ; Molecule ; Energy (signal processing) ; Algorithm ; Artificial intelligence ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Li Ma | Ming Ding | Yu Zhang | Tao Huang | Zhen-Feng Tong | Lu Sun
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Metals and Alloys

Predicting Swelling in Austenitic Steels Under Pwr Environments Using Machine Learning Austenite ; Swelling ; Materials science ; Metallurgy ; Nuclear engineering ; Environmental science ; Composite material ; Engineering ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses