jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chukwuka Elendu | Dependable C. Amaechi | Tochi C. Elendu | Emmanuel C. Amaechi | Ijeoma D. Elendu | Janet C. Omeludike | Eunice Omeludike | Nwamaka C. Onubogu | Emmanuel C. Ogelle | Oluwatobi O.M. Meduoye | Praise O. Oloyede | Chiamaka P. Ezeh | Ikpembhosa J. Esangbedo | Augustina C. Adigwe | Nnachi M. Akuma | Silas U. Okafor
Source: Annals of Medicine and Surgery (review, 2024-12-19)
Field: Materials Science — Materials Chemistry

Essential information about nanotechnology in cardiology Cardiology, as a medical specialty, addresses cardiovascular diseases (CVDs), a leading cause of global mortality. Nanomaterials offer transformative potential across key areas such as drug delivery, stem cell therapy, imaging, and gene delivery. Nanomaterials improve solubility, bioavailability, and targeted delivery in drug delivery, reducing systemic side effects. Examples include gas microbubbles, liposomal preparations, and paramagnetic nanoparticles, which show promise in treating atherosclerosis. Stem cell therapy benefits from nanotechnology through enhanced cell culture conditions and three-dimensional scaffolds that support cardiomyocyte growth and survival. Gold nanoparticles and poly(lactic-co-glycolic acid)-derived microparticles further improve stem cell viability. In imaging, nanomaterials enable advanced visualization techniques such as magnetic resonance imaging with direct labeling and optical tracking via dye-conjugated nanoparticles. In gene delivery, polymeric nanocarriers like polyethyleneimine, dendrimers, and graphene-based materials offer efficient, non-viral alternatives, with magnetic nanoparticles showing promise in targeted applications. Ongoing research highlights the potential of nanomaterials to revolutionize CVD management by improving therapeutic outcomes and enabling precision medicine. These advancements position nanotechnology as a cornerstone of modern cardiology. Medicine ; Interventional cardiology ; Medical physics ; Internal medicine ; Intensive care medicine ; Cardiology ; Nanotechnology Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Coronary Interventions and Diagnostics
Authors: Defne Çırcı | Ghazal Khalighinejad | Anlan Chen | Bhuwan Dhingra | L. Catherine Brinson
Source: Integrating materials and manufacturing innovation (article, 2024-07-19)
Field: Materials Science — Materials Chemistry

How Well Do Large Language Models Understand Tables in Materials Science? Computer science High voltage insulation and dielectric phenomena ; Concrete and Cement Materials Research ; Machine Learning in Materials Science
Authors: Sushen Chandra Devsharma | Lutfor Rahman | Md. Jakir Hossain | Bristy Biswas | Md. Farid Ahmed | Nahid Sharmin
Source: Heliyon (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Elucidation of structural, electromagnetic, and optical properties of Cu–Mg ferrite nanoparticles Copper doped magnesium ferrite, Mg<sub>1</sub>-<sub>x</sub>Cu<sub>x</sub>Fe<sub>2</sub>O<sub>4</sub>(x = 0.0-1.0) nanomaterials were synthesized via. sol-gel method sintered at 600 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>°C</mml:mo></mml:mrow> </mml:math> for 2 h. The synthesized materials were characterized using modern sophisticated techniques viz. X-ray diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy, Energy dispersive x-ray spectroscopy (EDS), Vibrating sample magnetometer, UV-visible diffuse reflectance spectra and Impedance analyzer. XRD analysis revealed that all the samples were single phase cubic spinel structure with Fd3m space group and investigated the change in structural parameters with copper concentration. The average crystallite size in the range of 11-23 nm and lattice parameters decrease with increasing Cu doping, due to the cationic distribution and ionic radius. The SEM images show the agglomeration of the particles with spherical like shape and elemental percentage were obtained from EDX. The saturation magnetization showed an increasing trend with increasing Cu concentration at a certain level and then decreases due to the rearrangement of cations at tetrahedral and octahedral sites. The Coercivity, Retentivity and magnetic crystalline anisotropy increase with changing dopant concentration. The magnetic measurements showed enhanced saturation magnetization at certain level (28.96emu/gm) and increase in coercivity up to 1102 Oe with changing dopant concentration. The estimated band gap energy is found to increase with Cu content. The dielectric constant, dielectric loss and impedance show normal behavior of ferrite. The frequency dependent dielectric constant decrease and tan delta shows a relaxation behavior at low frequencies. The synthesized nano Mg-Cu nanoparticles will be applied as humidity sensor, gas sensor, microwave devices and photocatalyst. Materials science ; Analytical Chemistry (journal) ; Coercivity ; Dielectric ; Crystallite ; Dopant ; Scanning electron microscope ; Lattice constant ; Ionic radius ; Transmission electron microscopy ; Nuclear magnetic resonance ; Doping ; Condensed matter physics ; Optics ; Nanotechnology ; Chemistry ; Composite material ; Diffraction ; Ion ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Electromagnetic wave absorption materials
Authors: Muhammad Huzaifa | Muhammad Shafiq | Nida Ali | Caterina Cocchi | Mohammad Nur‐e‐Alam | Zaheer Ul‐Haq
Source: ACS Omega (article, 2024-12-23)
Field: Materials Science — Materials Chemistry

Au-Decorated Ti<sub>3</sub>C<sub>2</sub> MXene Sensor for Enhanced Detection of Gaseous Toxins (CO, COCl<sub>2</sub>, H<sub>2</sub>S, NH<sub>3</sub>, NO<sub>2</sub>): A DFT Study The rising level of toxic gases in the environment poses a high demand for efficient gas sensing materials. MXenes, an emerging class of two-dimensional (2D) materials, have gained significant interest in this area for having an active-site rich structure, tunable surface properties, and remarkable stability. Herein, an extensive density functional theory (DFT) study is conducted to investigate the sensing properties of pristine and Au-functionalized Ti<sub>3</sub>C<sub>2</sub> MXene for five toxic gas molecules: CO, COCl<sub>2</sub>, H<sub>2</sub>S, NH<sub>3</sub>, and NO<sub>2</sub>. Pristine Ti<sub>3</sub>C<sub>2</sub> displays high affinity for CO, H<sub>2</sub>S, and NH<sub>3</sub>, as assessed by density of states and a large binding energy, resulting in the chemisorption of these gas molecules providing a relatively large recovery time. In contrast, Au-functionalized Ti<sub>3</sub>C<sub>2</sub> is able to sense all five toxins which are physisorbed on it, as indicated by lower adsorption energy and faster recovery time. As an example, the adsorption energy computed for CO is -0.14 eV and the resulting recovery time 0.21 ns. These results reveal that Au-functionalized Ti<sub>3</sub>C<sub>2</sub> can serve as a highly efficient material for toxic gas sensing, particularly CO. MXenes ; Chemisorption ; Adsorption ; Density functional theory ; Molecule ; Materials science ; Binding energy ; Chemical engineering ; Nanotechnology ; Physical chemistry ; Chemistry ; Computational chemistry ; Organic chemistry ; Physics ; Atomic physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Jiayun Wang | Boyi Yao | Yuanyuan Niu | Jian Ma | Yuanhui Wang | Zeng Qu | Junping Duan | Binzhen Zhang
Source: Advanced Composites and Hybrid Materials (article, 2024-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generative adversarial networks for high degree of freedom metasurface designs Adversarial system ; Generative grammar ; Degree (music) ; Computer science ; Generative adversarial network ; Artificial intelligence ; Deep learning ; Physics ; Acoustics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Jing Zhou | Xiayong Chen | Jiang Xiao | Zean Tian | Wangyu Hu | Bowen Huang | Dingwang Yuan
Source: Applied Surface Science (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated screening of Heusler alloys for nitrogen reduction reaction with graph neural network Artificial neural network ; Reduction (mathematics) ; Graph ; Nitrogen ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Mathematics ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Hydrogen Storage and Materials
Authors: Puck van Gerwen | Ksenia R. Briling | Charlotte Bunne | Vignesh Ram Somnath | Rubén Laplaza | Andreas Krause | Clémence Corminbœuf
Source: Journal of Chemical Information and Modeling (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

3DReact: Geometric Deep Learning for Chemical Reactions Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks. Extrapolation ; Computer science ; Invariant (physics) ; Interpolation (computer graphics) ; Deep learning ; Equivariant map ; Homogeneous space ; Artificial neural network ; Atom (system on chip) ; Artificial intelligence ; Algorithm ; Theoretical computer science ; Mathematics ; Geometry ; Pure mathematics ; Image (mathematics) ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Arun Mannodi‐Kanakkithodi
Source: Computational Materials Science (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning Semiconductor ; Materials science ; Nanotechnology ; Computer science ; Optoelectronics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Kevin P. Hickey | Jeremy Feinstein | Ganesh Sivaraman | Margaret MacDonell | Eugene Yan | Carlos Matherson | Scott Coia | Jason Xu | Kurt Picel
Source: Computational Materials Science (article, 2024-03-16)
Field: Materials Science — Materials Chemistry

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers Mean squared error ; Glass transition ; Polymer ; Biological system ; Computer science ; Graph ; Materials science ; Work (physics) ; Thermal ; Regression ; Machine learning ; Artificial intelligence ; Statistical physics ; Thermodynamics ; Physics ; Mathematics ; Statistics ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Ionic liquids properties and applications
Authors: Tengyu Yao | Zhenming Xu | Tingsong Hu | Kang Hu | Xueliang Cui | Laifa Shen
Source: The Journal of Physical Chemistry C (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

High-Throughput Computation and Machine Learning Prediction Accelerating the Design of Cathode Catalysts for Li–CO<sub>2</sub> Batteries Developing new cathode catalysts to facilitate the electrochemical reaction toward the product of lithium oxalate would enhance the performance of Li–CO2 batteries. Herein, high-throughput calculations and machine learning technology were synergistically utilized to effectively screen new transition metal-based cathode catalysts. Gibbs free energies of CO2 reduction on the surfaces of 11 transition metal compounds were explored by the high-throughput first-principles calculations, and MoN was forecasted to be a promising cathode catalyst material, promoting the reaction product of lithium oxalate and possessing the minimum overpotential. On the other hand, based on the data of first-principles calculations, machine learning models with respect to overpotential were built to evaluate the electrochemical performances of transition metal nitrides. The origin of catalytic activity was revealed to be the adsorption energy by the feature importance analysis of overpotential. Our work not only puts forward a paradigm of the highly effective screening of cathode catalysts for Li–CO2 batteries but also provides a new understanding of the overpotential of CO2 reduction from the perspective of molecule adsorption. Throughput ; Cathode ; Catalysis ; Computation ; Computer science ; Materials science ; Chemistry ; Engineering ; Electrical engineering ; Wireless ; Operating system ; Algorithm Machine Learning in Materials Science ; Advancements in Battery Materials ; Fuel Cells and Related Materials
Authors: Julien Steffen | Amin Alibakhshi
Source: The Journal of Chemical Physics (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Hydrogen diffusion on Ni(100): A combined machine-learning, ring polymer molecular dynamics, and kinetic Monte Carlo study We introduce a methodological framework coupling machine-learning potentials, ring polymer molecular dynamics (RPMD), and kinetic Monte Carlo (kMC) to draw a comprehensive physical picture of the collective diffusion of hydrogen atoms on metal surfaces. For the benchmark case of hydrogen diffusion on a Ni(100) surface, the hydrogen adsorption and diffusion energetics and its dependence on the local coverage is described via a neural-network potential, where the training data are computed via periodic density functional theory (DFT) and include all relevant optimized diffusion and desorption paths, sampled by nudged elastic band optimizations and molecular dynamics simulations. Nuclear quantum effects, being crucial for processes involving hydrogen at low temperatures, are treated by RPMD. The diffusion rate constants are calculated with a combination of umbrella samplings employed to map the free energy profile and separate samplings of recrossing trajectories to obtain the transmission coefficient. The calculated diffusion rates for different temperatures and local environments are then combined and fitted into a kMC model allowing access to larger time and length scales. Our results demonstrate an outstanding performance for the trained neural network potential in reproducing reference DFT energies and forces. We report the effective diffusion rates for different temperatures and hydrogen surface coverages obtained via this recipe in good agreement with the experimental results. The method combination proposed in this study can be instrumental for a wide range of applications in materials science. Molecular dynamics ; Kinetic Monte Carlo ; Diffusion ; Diffusion Monte Carlo ; Monte Carlo method ; Hydrogen ; Statistical physics ; Kinetic energy ; Density functional theory ; Chemical physics ; Materials science ; Chemistry ; Thermodynamics ; Computational chemistry ; Physics ; Hybrid Monte Carlo ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Nuclear Materials and Properties
Authors: Xiaoyue He | Yanxu Chen | Shao Wang | Genqiang Zhang
Source: ACS Applied Materials & Interfaces (article, 2024-05-04)
Field: Materials Science — Materials Chemistry

Employing Graph Neural Networks for Predicting Electrode Average Voltages and Screening High-Voltage Sodium Cathode Materials For many years, humans have been relentlessly focused on enhancing battery longevity and boosting energy storage capacities. The performance and durability of a battery depend significantly on the material used for its electrodes. In this context, merging machine learning with density functional theory (DFT) calculations has emerged as a pivotal approach to advancing the exploration of battery crystal structures. We present a new method that combines a graph convolutional neural network (GNN) with a Transformer convolutional layer, which we call Transformer-GNN. To underscore its efficacy, we benchmarked Transformer-GNN against three established statistical machine learning models: Support Vector Machine, Random Forest, and XGBoost. We also developed a standard GNN, which we refer to as Basic-GNN. Additionally, we compared Basic-GNN with Transformer-GNN to highlight the improvements brought about by incorporating the Transformer convolutional layer. The Transformer-GNN model outperforms the other models, achieving the highest <i>R</i><sup>2</sup> value of 0.82 and the lowest mean squared error of 0.3161. Our findings demonstrate that the Transformer-GNN can profoundly understand battery crystal structures, thus forging the path toward more sophisticated and durable battery systems. Leveraging the GNN model's voltage predictions in tandem with the capacity data sourced from the database, we screened and pinpointed Na(NiO<sub>2</sub>)<sub>2</sub> as a high-voltage (higher than 5 V), high-capacity sodium cathode material. We conducted DFT calculations on Na(NiO<sub>2</sub>)<sub>2</sub> and revealed the migration mechanism of the Na ions. Transformer ; Cathode ; Voltage ; Computer science ; Materials science ; Support vector machine ; Electrode ; Convolutional neural network ; Machine learning ; Electrical engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Ran An | Congwei Xie | Dongdong Chu | Fuming Li | Shilie Pan | Zhihua Yang
Source: ACS Applied Materials & Interfaces (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery Modern crystal structure prediction methods based on structure generation algorithms and first-principles calculations play important roles in the design of new materials. However, the cost of these methods is very expensive because their success mostly relies on the efficient sampling of structures and the accurate evaluation of energies for those sampled structures. Herein, we develop a Machine-learning-Assisted CRYStalline Materials sAmpling sysTem (MAXMAT) aiming to accelerate the prediction of new crystal structures. For a given chemical composition, MAXMAT can generate efficient crystal structures with the help of a Python package for crystal structure generation (PyXtal) and can quickly evaluate the energies of these generated structures using a well-developed machine learning interaction potential model (M3GNET). We have used MAXMAT to perform crystal structure searches for three different chemical systems (TiO2, MgAl2O4, and BaBOF3) to test its accuracy and efficiency. Furthermore, we apply MAXMAT to predict new nonlinear optical materials, suggesting several thermodynamically synthesizable structures with high performance in LiZnGaS3 and CaBOF3 systems. Materials science ; Nanotechnology ; Machine learning ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Takuya Taniguchi
Source: CrystEngComm (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Exploration of elastic moduli of molecular crystals <i>via</i> database screening by pretrained neural network potential Elastic moduli of molecular crystals can be predicted using pretrained neural network potential, showing sufficient agreement with experimental data. Moduli ; Artificial neural network ; Elastic modulus ; Computer science ; Materials science ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Physics ; Composite material ; Biology Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Qibang Liu | Diab Abueidda | Sagar Vyas | Yuan Gao | Seid Korić | Philippe H. Geubelle
Source: The Journal of Physical Chemistry B (article, 2024-01-31)
Field: Chemistry — Organic Chemistry

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. In this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields. Sobol sequence ; Surrogate model ; Residual ; Artificial neural network ; Computer science ; Finite element method ; Adaptive sampling ; Redundancy (engineering) ; Dicyclopentadiene ; Algorithm ; Artificial intelligence ; Sensitivity (control systems) ; Machine learning ; Materials science ; Polymerization ; Mathematics ; Engineering ; Structural engineering ; Statistics ; Monte Carlo method ; Polymer ; Electronic engineering Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Xue Zong | Tianjun Xie | Dionisios G. Vlachos
Source: Applied Catalysis B: Environmental (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Predicting hydrogenolysis reaction barriers of large hydrocarbons on metal surfaces using machine learning: Implications for polymer deconstruction Hydrogenolysis ; Deconstruction (building) ; Metal ; Polymer ; Materials science ; Chemical engineering ; Catalysis ; Chemistry ; Organic chemistry ; Engineering ; Composite material ; Waste management Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and Data Classification
Authors: Subhadeep Dasgupta | Amal R.S. | Prabal K. Maiti
Source: Separation and Purification Technology (preprint, 2024-06-24)
Field: Chemistry — Inorganic Chemistry

Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning Adsorption ; Molecular sieve ; Metal-organic framework ; Binary number ; Work (physics) ; Materials science ; Computer science ; Chemistry ; Thermodynamics ; Organic chemistry ; Mathematics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport