jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Goranov Vitaly
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2024-12-03)
Field: Materials Science — Materials Chemistry

Novel route to assessment of iron nanoparticles toxicity considering time-dependent processes (conceptual framework). In this study, a new framework for the evaluation of the toxicity of superparamagnetic iron oxide nanoparticles (SPIONs) is proposed. To achieve this, in vitro and in vivo experimental data are analysed by consequent mathematical modelling and using parameters that were determined by the ML/AI approach. The framework takes into account the inherent complexity of nanoparticle toxicity, which differs from conventional chemical compounds considering time-dependent processes such as cellular uptake, intracellular transport and degradation of SPIONs. In vitro experiments with CaCo2 cells evaluate cytotoxicity, reactive oxygen species (ROS) production and expression of antioxidant enzymes over 96 hours. C. elegans is used for in vivo studies to evaluate interspecies extrapolation. Convolutional neural networks are trained to segment and classify cells based on nanoparticle uptake, allowing quantitative assessment of morphological changes. The results show a strong correlation between increased nanoparticle uptake, morphological alterations, increased intracellular iron levels and oxidative stress. The mathematical models are validated using experimental data and an IC50 value for in vitro and in vivo toxicity is calculated. Finally, the study discusses the potential for interspecies extrapolation and highlights the potential of the framework to reduce animal testing in future nanotoxicity assessments. Toxicity ; Conceptual framework ; Nanoparticle ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Epistemology ; Philosophy Nanoparticles: synthesis and applications
Authors: Gökhan Bilgin | Ayça Kumluca Topallı | Tomislav Kilić | Murat Elibol
Source:  (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Combining response surface methodology and machine learning for harmonic mean diameter prediction and optimization in the nanoparticle biosynthesis The synthesis of nanoparticles from biological sources by green synthesis method and production optimization studies are increasing in popularity today. However, the variability of biological source and environmental effects in such processes leads to different morphology and functionality in the final product. In this study, microalgae was used as a bioreduction agent in nanoparticle synthesis and analyses of the harmonic mean particle diameter of FeSO4 concentration and its ratio with microalgae medium were carried out in particle synthesis. In this two-stage study, the experimental design was carried out first, and the particle diameters obtained by data generation were developed by machine learning. The error rates at both stages were compared and improvements were recorded. As a result, a new low-cost, fast, simple and environmentally friendly approach was introduced to solve the data insufficiency problem and used in particle diameter estimation. The results obtained showed that the proposed combined strategy provides better nanoparticle size estimates than the statistical approach alone. The proposed method is applicable to a wide range of biotechnology and bioengineering applications with significant advanced knowledge Nanoparticle ; Response surface methodology ; Harmonic mean ; Surface (topology) ; Harmonic ; Computer science ; Materials science ; Biological system ; Artificial intelligence ; Mathematics ; Machine learning ; Nanotechnology ; Physics ; Statistics ; Acoustics ; Biology ; Geometry Nanoparticles: synthesis and applications ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Chenluwei Ruan
Source: Journal of Physics Conference Series (article, 2024-06-01)
Field: Materials Science — Polymers and Plastics

Research on surface modification evaluation on composites interfacial properties Abstract At present, composites are formed by multi-phase materials with reinforcement and matrix, which can produce a discontinuous interface layer between two phases. Therefore, surface modification methods are proposed to enhance the performance and the composite properties. However, the evaluation of these properties primarily relies on manual detection and this may cause mistakes in some situations. In this work, an automatic evaluation model is proposed to achieve the interfacial properties estimation. The neural network consists of multiple layers of neural cells and the evaluation is simulated by the prediction process with enough training iterations. Initially, the neural network model is trained with tremendous composite properties with different surface modification methods and the evaluation results are labelled. Further, the inner parameters are set and the convergence condition is controlled to obtain an acceptable evaluation model. Finally, our model is compared with existing evaluation methods. From our extensive experiments and analysis, it can be concluded that our model can realize the evaluation for the performance of surface modification in interfacial properties. Artificial neural network ; Convergence (economics) ; Surface modification ; Materials science ; Composite number ; Computer science ; Reinforcement ; Composite material ; Matrix (chemical analysis) ; Surface (topology) ; Process (computing) ; Evaluation methods ; Interface (matter) ; Biological system ; Artificial intelligence ; Mechanical engineering ; Reliability engineering ; Mathematics ; Engineering ; Contact angle Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Epoxy Resin Curing Processes
Authors: Yuxinxin Chen | Pavlo O. Dral
Source: arXiv (Cornell University) (preprint, 2024-09-18)
Field: Materials Science — Materials Chemistry

All-in-one foundational models learning across quantum chemical levels Machine learning (ML) potentials typically target a single quantum chemical (QC) level while the ML models developed for multi-fidelity learning have not been shown to provide scalable solutions for foundational models. Here we introduce the all-in-one (AIO) ANI model architecture based on multimodal learning which can learn an arbitrary number of QC levels. Our all-in-one learning approach offers a more general and easier-to-use alternative to transfer learning. We use it to train the AIO-ANI-UIP foundational model with the generalization capability comparable to semi-empirical GFN2-xTB and DFT with a double-zeta basis set for organic molecules. We show that the AIO-ANI model can learn across different QC levels ranging from semi-empirical to density functional theory to coupled cluster. We also use AIO models to design the foundational model Δ-AIO-ANI based on Δ-learning with increased accuracy and robustness compared to AIO-ANI-UIP. The code and the foundational models are available at https://github.com/dralgroup/aio-ani; they will be integrated into the universal and updatable AI-enhanced QM (UAIQM) library and made available in the MLatom package so that they can be used online at the XACS cloud computing platform (see https://github.com/dralgroup/mlatom for updates). Quantum chemical ; Psychology ; Computer science ; Cognitive science ; Epistemology ; Physics ; Philosophy ; Quantum mechanics Machine Learning in Materials Science
Authors: Balázs Nagyillés | Gergely Nagy | Bálint Kiss | E. Cormier | Péter Földi | Katalin Varjú | Subhendu Kahaly | Mousumi Upadhyay Kahaly | Zsolt Divéki
Source: arXiv (Cornell University) (preprint, 2024-07-18)
Field: Chemistry — Spectroscopy

MIR laser CEP estimation using machine learning concepts in bulk high harmonic generation Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. These approaches are adequate, but are challenging to implement on demand, at different locations as additional metrology tools, in intense few cycle laser-matter interaction experiments, such as those prevalent in sophisticated user beamlines. In addition there are inherent difficulties for CEP measured at non-conventional laser wavelengths (like e.g. mid infrared) and measurements above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate both by simulations and by experiments a machine learning (ML) driven method for CEP estimation in the mid infrared, which is readily generalizable for any laser wavelength and possibly up to MHz repetition rates. The concept relies on the observation of the spectrum of high harmonic generation (HHG) in bulk material and the use of ML techniques to estimate the CEP of the laser. Once the ML model is trained, the method provides a way for cheap and compact real-time CEP tagging. This technique can complement the otherwise sophisticated monitoring of CEP, and is able to capture the complex correlation between the CEP and the observable HHG spectra. Estimation ; Laser ; High harmonic generation ; Harmonic ; Computer science ; Artificial intelligence ; Physics ; Optics ; Engineering ; Acoustics ; Systems engineering Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser Design and Applications
Authors: Samantha Bernstein | Alexandra Litvinov | Colin M. Yee | Steven D. Kim | Wei Li | Richard Liang | Jin Gyu Park | Joseph H. Koo
Source:  (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Validation of Porous Microstructure Analysis (PuMA) against Experimental Thermal Conductivity of Thermal Protection System Materials The goal of this research is to utilize microstructures of Thermal Protection System (TPS) materials to quantify material properties. Three novel TPS materials were developed and assembled at the University of Texas at Austin. They were tested on an Oxy-Acetylene Test Bed (OTB) to allow for validation of this method for virgin and char materials. To validate this novel methodology, the thermal conductivity of the materials was quantified in two ways: experimentally and through microstructures analysis. Experimental thermal conductivity was measured using a Modified Transient Plane Source (MTPS) instrument at Florida State University. Synchrotron micro-computed tomography scans were taken at the Lawrence Berkeley National Laboratory (LBNL). Microstructural thermal conductivity was predicted using semantic segmentation, machine learning models, and NASA’s Porous Microstructure Analysis (PuMA) software. The thermal conductivities found using these two methods are compared and potential sources of error are discussed. Thermal conductivity ; Microstructure ; Puma ; Porosity ; Materials science ; Thermal ; Thermal analysis ; Thermal protection ; Nuclear engineering ; Composite material ; Engineering ; Thermodynamics ; Physics ; Chemistry Thermal properties of materials ; Additive Manufacturing and 3D Printing Technologies ; Gas Dynamics and Kinetic Theory
Authors: Jorge O. Parra | Miroslavna Kovylina | Amadeu Griol | Pablo Sanchis
Source: arXiv (Cornell University) (preprint, 2024-07-05)
Field: Materials Science — Materials Chemistry

Nonvolatile reconfigurable polarization rotator at datacom wavelengths based on a Sb2Se3/Si waveguide Silicon photonics has become a key platform for photonic integrated circuits (PICs) due to its high refractive index and compatibility with complementary metal-oxide-semiconductor manufacturing. However, the inherent birefringence in silicon waveguides requires efficient polarization management. Here, we report a reconfigurable polarization rotator (PR) using a Sb2Se3/Si waveguide operating at datacom wavelengths (1310 nm), providing nonvolatile switching with zero static power consumption. The polarization conversion relies on the interference of hybrid electric-magnetic (EH) modes, which can be reconfigured by changing the Sb2Se3 state between amorphous and crystalline. Our experimental device exhibits a polarization conversion efficiency (PCE) and a polarization extinction ratio (PER) as high as -0.08 dB and 17.65 dB, respectively, in a compact footprint of just 21$μ$m length. Therefore, the proposed reconfigurable PR offers a compact and energy-efficient solution for polarization management in silicon photonics, with potential applications in data communication networks and emerging applications benefiting from polarization information encodings, such as optical neural networks and quantum computing. Wavelength ; Polarization (electrochemistry) ; Optoelectronics ; Materials science ; Waveguide ; Chemistry Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Optical and Acousto-Optic Technologies
Authors: Bochuan Du | Pu Tian
Source: Acta Physica Sinica (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Variational analysis and AI algorithm implementation of free energy landscapes of molecular system Accurate description of the free energy landscape (FES) is the basis for understanding complex molecular systems, and for further realizing molecular design, manufacture and industrialization. Major challenges include multiple metastable states, which usually are separated by high potential barriers and are not linearly separable, and may exist at multiple levels of time and spatial scales. Consequently FES is not suitable for analytical analysis and brute force simulation. To address these challenges, many enhanced sampling methods have been developed. However, utility of them usually involves many empirical choices, which hinders research advancement, and also makes error control very unimportant. Although variational calculus has been widely applied and achieved great success in physics, engineering and statistics, its application in complex molecular systems has just begun with the development of neural networks. This brief review is to summarize the background, major developments, current limitations, and prospects of applying variation in this field. It is hoped to facilitate the AI algorithm development for complex molecular systems in general, and to promote the further methodological development in this line of research in particular. Computer science ; Implementation ; Energy (signal processing) ; Statistical physics ; Physics ; Quantum mechanics ; Software engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: M Chethan | V Meghana | Pruthvi Raj G | M Arjun | K V Malini | K. C. Ramya
Source:  (article, 2024-11-23)
Field: Chemistry — Analytical Chemistry

Determination of Artificially Ripened Fruits Using IOT Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time consuming, labor intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration. Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for pre processing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real time, data driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system. Internet of Things ; Computer science ; Embedded system Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Linlin Ge | Yashuai Han | Yingwei Xia | Zhenyu Gao
Source:  (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Layer Classification and Identification of Graphene 2D N Anosheets With the development of technology, the application of two-dimensional nanosheets has become increasingly widespread, particularly in the fields of materials science, nanotechnology, and electronic engineering, where the precise identification of graphene layers is of paramount importance. Nowadays, there are many ways to explore 2D material nanosheets in the field of materials science, but there is still the problem that the average target detection accuracy is not high at different categories and thresholds. Therefore, to solve this problem, this paper proposes a deep learning-based object detection method for different classes of graphene two-dimensional nanosheets. The accurate value of the method is 99.12%, and some performance indicators of the algorithm are also improved compared with the classical algorithm. According to the results, the algorithm has better advantages for graphene nanopayer classification. Identification (biology) ; Layer (electronics) ; Graphene ; Computer science ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Graphene research and applications ; Advanced Graph Neural Networks
Authors: Yu Chen | Edoardo Patelli | Zhen Yang | Adolphus Lye
Source: arXiv (Cornell University) (preprint, 2024-05-28)
Field: Materials Science — Materials Chemistry

Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often scarce and sparse) and insufficient consideration of the uncertainty in the data, model, and prediction. This paper therefore presents a meta-learning based approach that is both uncertainty- and prior knowledge-informed, aiming at trustful predictions of material properties for the nuclear reactor design. It is suited for robust learning under limited data. Uncertainty has been accounted for where a distribution of predictor functions are produced for extrapolation. Results suggest it achieves superior performance than existing empirical methods in rupture life prediction, a case which is typically under a small data regime. While demonstrated herein with rupture properties, this learning approach is transferable to solve similar problems of data scarcity across the nuclear industry. It is of great importance to boosting the AI analytics in the nuclear industry by proving the applicability and robustness while providing tools that can be trusted. Creep ; Property (philosophy) ; Materials science ; Nuclear engineering ; Nuclear data ; Structural engineering ; Composite material ; Nuclear physics ; Engineering ; Physics ; Neutron Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Fatigue and fracture mechanics
Authors: Eugene Kheng | Royan J. D’Mello | Anthony M. Waas
Source:  (article, 2024-01-04)
Field: Materials Science — Polymers and Plastics

Failure modeling in notched twill textile composites using Machine Learning The location of an unintended imperfection in the form of a notch in twill woven textile composites can influence its tensile deformation response depending on the size of the notch with respect to the characteristic size of tows. To accurately model the failure response using finite element analysis, the meso-structural details of the fiber-matrix tows and surrounding matrix need to be taken into account for small notches, in conjunction with physics-based constitutive models for the materials. However, given the complexity of the geometry, such detailed mesoscale models are computationally expensive. On the other hand, machine learning-based models have the potential to efficiently bound the macroscopic stress-strain response when inputs are known. Moreover, the machine learning models are at least four orders of magnitude faster than running a full-scale finite element simulation which includes the mesoscale textile geometry. In the present work, we show that the strength and the salient features of the stress-strain response can be adequately captured by using surrogate models and the Kriging technique. Composite material ; Materials science ; Textile Textile materials and evaluations ; Mechanical Behavior of Composites ; Dyeing and Modifying Textile Fibers
Authors: Ke Wei Jee | Hong Chua | Hui En Lee
Source: Future Technology (article, 2024-06-02)
Field: Chemistry — Analytical Chemistry

Product differentiation of Sarawak premium rice using MOS gas sensors: comparison between transient and frequency response In Malaysia, rice, ranked as the third most crucial crop, faces challenges due to domestic consumption outpacing production, resulting in increased instances of rice adulteration. This underscores the imperative of maintaining integrity and quality standards across the entire supply chain. This study uses an electronic nose, comprising four metal oxide semiconductor (MOS) gas sensors, and employing temperature modulation, Principal Component Analysis (PCA) and supervised machine learning (classification models) to distinguish rice varieties such as Bario, Bajong, Borneo Fragrant, Biris, and Jasmine. The study evaluated 30 classifiers based on their classification and validation accuracy. Sensor data was first extracted from the transient response of sensors output voltage, yielding a 12-dimension dataset with response times of 30 s, 50 s, and 95 s. Classification models trained from this dataset achieved classification (training) accuracy of up to 100% and validation accuracy of up to 96%, where the best performing models are subspace discriminant and kernel naïve bayes classifiers. An attempt was also made to analyze the sensor data frequency response for rice classification. Comparison between the prediction results in the transient and frequency domains showed that transient response is better suited for the classification of rice. Electronic nose ; Principal component analysis ; Pattern recognition (psychology) ; Computer science ; Transient (computer programming) ; Artificial intelligence ; Dimensionality reduction ; Dimension (graph theory) ; Linear discriminant analysis ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; GABA and Rice Research