jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: T. Barillot | Baptiste Schindler | Baptiste Moge | Elisa Fadda | F. Lépine | Isabelle Compagnon
Source: Beilstein Journal of Organic Chemistry (article, 2023-12-05)
Field: Chemistry — Spectroscopy

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data Carbohydrate sequencing is a formidable task identified as a strategic goal in modern biochemistry. It relies on identifying a large number of isomers and their connectivity with high accuracy. Recently, gas phase vibrational laser spectroscopy combined with mass spectrometry tools have been proposed as a very promising sequencing approach. However, its use as a generic analytical tool relies on the development of recognition techniques that can analyse complex vibrational fingerprints for a large number of monomers. In this study, we used a Bayesian deep neural network model to automatically identify and classify vibrational fingerprints of several monosaccharides. We report high performances of the obtained trained algorithm (GlAIcomics), that can be used to discriminate contamination and identify a molecule with a high degree of confidence. It opens the possibility to use artificial intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. Glycomics ; Chemistry ; Mass spectrometry ; Classifier (UML) ; Artificial neural network ; Artificial intelligence ; Glycan ; Pattern recognition (psychology) ; Bayesian optimization ; Machine learning ; Computational biology ; Computer science ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Mustafa Dağ
Source: Polymers from Renewable Resources (article, 2023-10-19)
Field: Materials Science — Polymers and Plastics

Optimization of safflower oil-based polyester biocomposite reinforced with diatomite: An response surface methodology approach and assessment of artificial neural network findings In this investigation, the examination revolves around the characterization of diatomite-enhanced modified safflower oil (MSO)-derived polyester biocomposites. The primary objective is to explore the feasibility of these biocomposites as a substitute for petrochemical-based unsaturated polyester (UP) materials, with the overarching goal of enhancing their economic sustainability. Experimental data analysis employed Response Surface Methodology (RSM) and Artificial Neural Network (ANN), uncovering the optimal composition for the polyester biocomposite to be 6.7 wt.% MSO and 4.5 wt.% diatomite. During the RSM analysis, it was noted that the response parameters exhibited quadratic p-values, specifically, for density ( p &lt; .0001), thermal conductivity ( p &lt; .0001), and Shore D hardness ( p &lt; .0003). However, higher ratios of MSO lead to decreased hardness and increased curing time. SEM images reveal a detrimental impact on the surface morphology of the polyester biocomposite when the diatomite content reaches 8 wt.%. Additionally, Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) offer valuable insights into the chemical bond structure and thermal behavior of the biocomposite, respectively. The Cure Index (CI) value for the diatomite-enhanced composite was determined to be 0.925, indicating a favorable contribution to the polyester curing process. The study finds that diatomite contributes to a linear change in the thermal conductivity coefficient, making the biocomposite suitable for use in the insulation industry. Overall, the study suggests that diatomite reinforced MSO-based polyester biocomposites have the potential as an alternative to petrochemical unsaturated polyester. Biocomposite ; Materials science ; Polyester ; Thermogravimetric analysis ; Response surface methodology ; Fourier transform infrared spectroscopy ; Curing (chemistry) ; Composite material ; Petrochemical ; Composite number ; Chemical engineering ; Organic chemistry ; Chemistry ; Chromatography Polymer composites and self-healing ; Electrospun Nanofibers in Biomedical Applications ; Polymer Science and PVC
Authors: Felix Draxler | Peter Sorrenson | Lea Zimmermann | Armand Rousselot | Ullrich Köthe
Source: arXiv (Cornell University) (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Free-form Flows: Make Any Architecture a Normalizing Flow Normalizing Flows are generative models that directly maximize the likelihood. Previously, the design of normalizing flows was largely constrained by the need for analytical invertibility. We overcome this constraint by a training procedure that uses an efficient estimator for the gradient of the change of variables formula. This enables any dimension-preserving neural network to serve as a generative model through maximum likelihood training. Our approach allows placing the emphasis on tailoring inductive biases precisely to the task at hand. Specifically, we achieve excellent results in molecule generation benchmarks utilizing $E(n)$-equivariant networks. Moreover, our method is competitive in an inverse problem benchmark, while employing off-the-shelf ResNet architectures. Computer science ; Benchmark (surveying) ; Constraint (computer-aided design) ; Dimension (graph theory) ; Estimator ; Flow (mathematics) ; Inverse ; Mathematical optimization ; Task (project management) ; Generative grammar ; Artificial neural network ; Generative model ; Architecture ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Patrick Schuenke | Kerstin Heinecke | George Narvaez | Moritz Zaiß | Christoph Kolbitsch
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Simultaneous Mapping of B0, B1 and T1 Using a CEST-like Pulse Sequence and Neural Network Analysis Obtaining isolated effects in Chemical Exchange Saturation Transfer (CEST) MRI requires the correction for the influence of B 0 - and B 1 -inhomogeneities and the T 1 relaxation time during post-processing. We present an extended approach for simultaneous mapping of B 0 , B 1 and T 1 using a CEST-like pulse sequence and a regularized neural network-based analysis that provides additional uncertainty estimations. We demonstrate the in vitro and in vivo applicability of the proposed approach, which is trained using simulated data and confirm its accordance with established reference methods. Pulse sequence ; Artificial neural network ; Sequence (biology) ; Pulse (music) ; Computer science ; Nuclear magnetic resonance ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Chromatography Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Faridun Jalolov | Evgeny V. Podryabinkin | Artem R. Oganov | Alexander V. Shapeev | Alexander G. Kvashnin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Mechanical Properties of Single and Polycrystalline Solids from Machine Learning Crystallite ; Materials science ; Computer science ; Composite material ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and mechanical properties
Authors: Rana Hossain | Hajime Kimizuka | Shigenobu Ogata
Source: Physical Review Materials (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Asymmetry in core structure and mobility of basal dislocations in a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>Ti</mml:mi></mml:mrow><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mi>SiC</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math> MAX phase: An atomistic study with machine-learned force fields MAX phases are a unique class of atomically layered ternary ceramics that deform plastically at room temperature owing to highly mobile basal dislocations (BDs). To understand and control the ductility of these materials, it is crucial to study the core structures and mobilities of BDs. In this study we developed a machine-learning-based spectral neighbor analysis potential (SNAP) to perform atomistic simulations of edge, screw, and mixed BDs in a ${\mathrm{Ti}}_{3}{\mathrm{SiC}}_{2}$ MAX phase. The SNAP was trained on density functional theory (DFT)-calculated data. The SNAP calculations demonstrate that the BD core structure exhibits significant asymmetry that is dependent on the position of the weakly bonded Si layer relative to the $\mathrm{Ti}(4f)--\mathrm{Si}$ slip plane. The dislocation core either splits into Shockley partials or remains compact, depending on whether compressive or tensile stresses act on the Si layer. This asymmetry in the BD core structure agrees well with DFT results. Differential displacement and Nye tensor distribution analyses reveal that undissociated BD cores are centered on Si layers and spread over adjacent parallel basal planes. Additionally, they are three orders of magnitude less mobile than partial BDs. The Peierls stresses of partial edge BDs ($\ensuremath{\sim}\phantom{\rule{0.16em}{0ex}}71$ MPa) are closer to those of metals than those of ceramics, suggesting that edge BDs play a key role in the incipient plasticity of ${\mathrm{Ti}}_{3}{\mathrm{SiC}}_{2}$ MAX phases at low temperatures. The findings of this study contribute to a deeper understanding of the complex behavior of BDs at the atomic level and provide theoretical support for elucidating the unique deformation modes of crystals with atomically layered structures owing to the motion of BDs. Materials science ; Asymmetry ; Condensed matter physics ; Dislocation ; Crystallography ; Phase (matter) ; Physics ; Composite material ; Chemistry MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Yinan Huang | William W. Lu | Joshua Robinson | Yang Yu | Muhan Zhang | Stefanie Jegelka | Pan Li
Source: arXiv (Cornell University) (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

On the Stability of Expressive Positional Encodings for Graphs Designing effective positional encodings for graphs is key to building powerful graph transformers and enhancing message-passing graph neural networks. Although widespread, using Laplacian eigenvectors as positional encodings faces two fundamental challenges: (1) \emph{Non-uniqueness}: there are many different eigendecompositions of the same Laplacian, and (2) \emph{Instability}: small perturbations to the Laplacian could result in completely different eigenspaces, leading to unpredictable changes in positional encoding. Despite many attempts to address non-uniqueness, most methods overlook stability, leading to poor generalization on unseen graph structures. We identify the cause of instability to be a ``hard partition'' of eigenspaces. Hence, we introduce Stable and Expressive Positional Encodings (SPE), an architecture for processing eigenvectors that uses eigenvalues to ``softly partition'' eigenspaces. SPE is the first architecture that is (1) provably stable, and (2) universally expressive for basis invariant functions whilst respecting all symmetries of eigenvectors. Besides guaranteed stability, we prove that SPE is at least as expressive as existing methods, and highly capable of counting graph structures. Finally, we evaluate the effectiveness of our method on molecular property prediction, and out-of-distribution generalization tasks, finding improved generalization compared to existing positional encoding methods. Our code is available at \url{https://github.com/Graph-COM/SPE}. Laplacian matrix ; Eigenvalues and eigenvectors ; Theoretical computer science ; Uniqueness ; Computer science ; Graph ; Laplace operator ; Generalization ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Xiaoqing Wu | Guifang Wu | Bo Wang | Jie Li
Source: BioResources (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

Classification of alfalfa hay based on infrared spectroscopy Alfalfa hay plays a decisive role in the quality and safety of livestock products. Chemical analytical methods for alfalfa hays are laborious, time-consuming, and costly. Therefore, suitable methods are required for rapid and accurate detection of alfalfa hay. This study evaluated the feasibility of infrared spectroscopy (IR) in identifying different alfalfa hays. 105 alfalfa hay samples under three different drying methods were analysed. Results indicated that the full spectra model constructed through standard normal variable transformation (SNV), first-derivative (FD), and second-derivative (SD) preprocessing by BP and SVM had the best performance. The accuracies were all up to 100%. Under the same preprocessing method, the accuracy of BP neural networks was better than that of support vector machine models in most cases. The characteristic wavelength-based SNV-SD-SPA by BP exhibited better performance than the other pretreatment methods, such as: SNV-SPA, SNV-FD-SPA, and SNV-GA, etc. The classification accuracy of moldy-dried alfalfa, sun-dried alfalfa, and shade-dried alfalfa in the training set were 100%, 100%, and 99.5%, respectively, and the accuracy of the prediction set reached 100%, 97.6%, and 97.4%, respectively. Thus, a better theoretical basis was obtained for the grading and online monitoring of alfalfa hay. Hay ; Preprocessor ; Alfalfa hay ; Mathematics ; Support vector machine ; Artificial intelligence ; Biology ; Agronomy ; Biological system ; Computer science ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Edoardo Cignoni | Divya Suman | Jigyasa Nigam | Lorenzo Cupellini | Benedetta Mennucci | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Materials Science — Materials Chemistry

Electronic excited states from physically-constrained machine learning Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods. Interpretability ; Computer science ; Electronic structure ; Context (archaeology) ; Hamiltonian (control theory) ; Quantum ; Artificial intelligence ; Machine learning ; Physics ; Mathematical optimization ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mehnaz | Bo Da | Zejun Ding
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Exploring the absolute yield curve of secondary electrons using machine learning methods Knowledge of absolute secondary electron yield (<i>δ</i>) is important for various applications of electron emission materials. Besides, it is also crucial to know the dependence of <i>δ</i> on primary electron energy <i>E</i><sub>p</sub> and material properties like atomic number Z. The available experimental database of <i>δ</i> reveals a large discrepancy among the measurement data, while the oversimplified semi-empirical theories of secondary electron emission can only present the general shape of the yield curve but not the absolute yield value. This limits not only the validation of a Monte Carlo model for theoretical simulations but also presents large uncertainties in the applications of different materials for various purposes. In applications, it is highly desirable to have the knowledge of the absolute yield of a material. Therefore, it is highly desirable to establish the relationship of the absolute yield with material and electron energy based on the available experimental data. Recently, machine learning (ML) methods have been increasingly used for the prediction of material properties mainly based on the atomistic calculations with the first-principles theory. We propose here the application of ML models to a material property study, starting with experimental observations and unfolding the relationship of <i>δ</i> with basic material properties and primary electron energy. Our ML models are able to predict <i>δ</i>(<i>E</i><sub>p</sub>)-curve covering a wide energy range of 10 eV-30 keV for unknown elements within the uncertainty range of the experimental data and can suggest more reliable data among the scattered experimental data. Yield (engineering) ; Electron ; Range (aeronautics) ; Monte Carlo method ; Experimental data ; Mean absolute percentage error ; Absolute zero ; Statistical physics ; Materials science ; Computational physics ; Physics ; Computer science ; Thermodynamics ; Machine learning ; Mathematics ; Statistics ; Nuclear physics ; Artificial neural network Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Gabriel T. Gialluca | Gustavo T. Gialluca | Bruno Masiero | Eduardo Rodrigues de Lima | Larissa M. Almeida | Fabiano Fruett
Source:  (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

A Wireless Weatherproof Acoustic Sensor System to Detect Anomalies in Substation Power Transformers Power transformers are essential components in electrical substations. Depending on the operating conditions, they can have failures capable of damaging the functioning of the power grid. To mitigate these problems, it is suitable for the transmission and distribution companies to implement a sensor system capable of monitoring the equipment and detecting anomalies in its operation. Acoustic measurements and signal processing present a promising non-invasive method for monitoring the working condition of power transformers. To achieve this goal, the authors developed an acoustic sensor system, featuring a suitable microphone, battery power supply, weatherproof enclosure, signal processing unit, and Wi-Fi connectivity. Additionally, they proposed an audio processing algorithm and a machine learning model for anomaly detection that analyzes the audio parameters and provides the transformer diagnosis. The proposed sensor system with anomaly detection approach can aid in preventing power transformer failures by analyzing audio parameters and providing valuable insights into the equipment's health. With this information, maintenance teams can intervene before critical failures occur, avoiding financial losses and interruptions in the power supply. The proposed approach can be readily integrated into existing substation infrastructures, enhancing the overall reliability of the power grid. Transformer ; Microphone ; Computer science ; Wireless ; Electrical engineering ; Grid ; Power transmission ; Electronic engineering ; Engineering ; Reliability engineering ; Power (physics) ; Telecommunications ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization
Authors: Moad Al-dbissi | Riccardo Rossa | Alessandro Borella | I. Pázsit | Paolo Vinai
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Identification of Diversions in Spent Pwr Fuel Assemblies by Pdet Signatures Using Artificial Neural Networks (Anns) Artificial neural network ; Identification (biology) ; Environmental science ; Nuclear engineering ; Engineering ; Computer science ; Artificial intelligence ; Biology ; Botany Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Christiano Bruneli Peres | Leandro Cardoso de Morais | Pedro R. Resende
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Carbon Adsorption on Waste Biomass of Passion Fruit Peel: A Promising Machine Learning Model for Co2 Capture Passion fruit ; Adsorption ; Passion ; Biomass (ecology) ; Waste management ; Pulp and paper industry ; Chemistry ; Chemical engineering ; Environmental science ; Food science ; Organic chemistry ; Engineering ; Agronomy ; Psychology ; Biology Adsorption, diffusion, and thermodynamic properties of materials ; Carbon Dioxide Capture Technologies
Authors: Abhishek Chandra | Taniya Kapoor | Bram Daniels | Mitrofan Curti | Koen Tiels | Daniel M. Tartakovsky | E.A. Lomonova
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural oscillators for magnetic hysteresis modeling Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We develop an ordinary differential equation-based recurrent neural network (RNN) approach to model and quantify the hysteresis, which manifests itself in sequentiality and history-dependence. Our neural oscillator, HystRNN, draws inspiration from coupled-oscillatory RNN and phenomenological hysteresis models to update the hidden states. The performance of HystRNN is evaluated to predict generalized scenarios, involving first-order reversal curves and minor loops. The findings show the ability of HystRNN to generalize its behavior to previously untrained regions, an essential feature that hysteresis models must have. This research highlights the advantage of neural oscillators over the traditional RNN-based methods in capturing complex hysteresis patterns in magnetic materials, where traditional rate-dependent methods are inadequate to capture intrinsic nonlinearity. Hysteresis ; Artificial neural network ; Recurrent neural network ; Nonlinear system ; Computer science ; Phenomenological model ; Preisach model of hysteresis ; Magnetic hysteresis ; Ordinary differential equation ; Feature (linguistics) ; Identification (biology) ; Control theory (sociology) ; Differential equation ; Statistical physics ; Artificial intelligence ; Physics ; Condensed matter physics ; Magnetic field ; Control (management) ; Quantum mechanics ; Philosophy Magnetic Properties and Applications ; Model Reduction and Neural Networks ; Neural Networks and Applications