jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael Gully-Santiago | Caroline Morley
Source: The Astrophysical Journal (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data* Abstract Comparison of échelle spectra to synthetic models has become a computational statistics challenge, with over 10,000 individual spectral lines affecting a typical cool star échelle spectrum. Telluric artifacts, imperfect line lists, inexact continuum placement, and inflexible models frustrate the scientific promise of these information-rich data sets. Here we debut an interpretable machine-learning framework blasé that addresses these and other challenges. The semiempirical approach can be viewed as “transfer learning”—first pretraining models on noise-free precomputed synthetic spectral models, then learning the corrections to line depths and widths from whole-spectrum fitting to an observed spectrum. The auto-differentiable model employs back-propagation, the fundamental algorithm empowering modern deep learning and neural networks. Here, however, the 40,000+ parameters symbolize physically interpretable line profile properties such as amplitude, width, location, and shape, plus radial velocity and rotational broadening. This hybrid data-/model-driven framework allows joint modeling of stellar and telluric lines simultaneously, a potentially transformative step forward for mitigating the deleterious telluric contamination in the near-infrared. The blasé approach acts as both a deconvolution tool and semiempirical model. The general-purpose scaffolding may be extensible to many scientific applications, including precision radial velocities, Doppler imaging, chemical abundances for Galactic archeology, line veiling, magnetic fields, and remote sensing. Its sparse-matrix architecture and GPU acceleration make blasé fast. The open-source PyTorch-based code blase includes tutorials, Application Programming Interface documentation, and more. We show how the tool fits into the existing Python spectroscopy ecosystem, demonstrate a range of astrophysical applications, and discuss limitations and future extensions. Python (programming language) ; Physics ; Deconvolution ; Computer science ; Synthetic data ; Artificial intelligence ; Computational science ; Algorithm ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; Stellar, planetary, and galactic studies
Authors: Nirvana Delgado Otalvaro | Pembe Gül Bilir | Karla Herrera Delgado | Stephan Pitter | Jörg Sauer
Source: Catalysts (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

Kinetics of the Direct DME Synthesis: State of the Art and Comprehensive Comparison of Semi-Mechanistic, Data-Based and Hybrid Modeling Approaches Hybrid kinetic models represent a promising alternative to describe and evaluate the effect of multiple variables in the performance of complex chemical processes, since they combine system knowledge and extrapolability of the (semi-)mechanistic models in a wide range of reaction conditions with the adaptability and fast convergence of data-based approaches (e.g., artificial neural networks—ANNs). For the first time, a hybrid kinetic model for the direct DME synthesis was developed consisting of a reactor model, i.e., balance equations, and an ANN for the reaction kinetics. The accuracy, computational time, interpolation and extrapolation ability of the new hybrid model were compared to those of a lumped and a data-based model with the same validity range, using both simulations and experiments. The convergence of parameter estimation and simulations with the hybrid model is much faster than with the lumped model, and the predictions show a greater degree of accuracy within the models’ validity range. A satisfactory dimension and range extrapolation was reached when the extrapolated variable was included in the knowledge module of the model. This feature is particularly dependent on the network architecture and phenomena covered by the underlying model, and less on the experimental conditions evaluated during model development. Extrapolation ; Convergence (economics) ; Computer science ; Interpolation (computer graphics) ; Artificial neural network ; Range (aeronautics) ; Variable (mathematics) ; Experimental data ; State variable ; Dimension (graph theory) ; Consistency (knowledge bases) ; Applied mathematics ; Biological system ; Algorithm ; Mathematics ; Machine learning ; Artificial intelligence ; Thermodynamics ; Materials science ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Wentao Li | Chenxiu Yang
Source: AIP Advances (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Thermal transport properties of monolayer GeS and SnS: A comparative study based on machine learning and SW interatomic potential models Phonon transport properties of two-dimensional materials can play a crucial role in the thermal management of low-dimensional electronic devices and thermoelectric applications. In this study, both the empirical Stillinger–Weber (SW) and machine learning interatomic potentials are employed to investigate the lattice thermal conductivity of monolayer GeS and SnS through solving the phonon Boltzmann transport equation. The accuracy of the two types of interatomic potentials and their performance for the evaluation of thermal conductivity are verified by analyzing phonon harmonic and anharmonic properties. Our results indicate that the thermal conductivity can be predicted more accurately with a machine learning approach, while the SW potential gives rise to an overestimated value for both monolayers. In addition, the in-plane anisotropy of thermal transport properties existing in these monolayers can be confirmed by both potential models. Moreover, the origins of the deviation existing in calculated thermal conductivities, including both the effects of interatomic potential models and monolayer compositions, are elucidated through uncovering the underlying phonon transport mechanisms. This study highlights that in contrast to the machine learning approach, more careful verification is required for the simulation of thermal transport properties when empirical interatomic potential models are employed. Thermal conductivity ; Phonon ; Interatomic potential ; Boltzmann equation ; Monolayer ; Anharmonicity ; Anisotropy ; Condensed matter physics ; Materials science ; Thermal ; Thermoelectric materials ; Chemistry ; Thermodynamics ; Molecular dynamics ; Nanotechnology ; Physics ; Computational chemistry ; Optics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Yukinori Koyama | Hidekazu Ikeno | Masamichi Harada | Shiro Funahashi | Takashi Takeda | Naoto Hirosaki
Source: Materials Advances (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Rapid discovery of new Eu<sup>2+</sup>-activated phosphors with a designed luminescence color using a data-driven approach Machine learning in conjunction with validation experiments uncovers new Eu 2+ -activated phosphor materials with a designed green-color luminescence. Phosphor ; Luminescence ; Conjunction (astronomy) ; Materials science ; Computer science ; Optoelectronics ; Physics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Perovskite Materials and Applications
Authors: Molly Kate Kreider | Abdul Qadeer Rehan | Robert M. Kent | Anibal Bezerra | Mariama Rebello Sousa Dias
Source: The Journal of Physical Chemistry C (article, 2022-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Al–Au Thin Films for Thermally Stable and Highly Sensitive Plasmonic Sensors Plasmonic sensors provide label-free detection of bio and chemical targets with ultrahigh sensitivity and accuracy. However, they usually lack the ability to operate at high temperatures, producing large measurement errors due to perturbations. Here, we report a surface plasmon resonance sensor based on Al–Au thin films, which outperforms its conventional Au counterpart by providing a temperature-stable response. We fabricate six metallic samples via the co-sputtering depositing method, obtaining four bimetallic thin films and two pure metals. Through spectroscopic ellipsometry, transmission measurements, and scanning electron microscopy images, we obtain their dielectric function and film morphology from room temperature to 200 °C, showing that the films containing Al do not undergo significant changes with increasing temperature. We experimentally and theoretically establish the dispersion relation of Al–Au alloys by varying the film chemical composition. Using the transfer matrix method, we evaluate the performance of the sensors by studying their response in the refractive index measurement of air, water, and a biological environment. We show that all alloys outperform their pure counterparts, achieving maximum theoretical sensitivities of 42411 nm/RIU and 162.7 °/RIU for a Au0.62Al0.38-based wavelength-dependent sensor and a Au0.85Al0.15-based angular-dependent sensor, respectively. We find that Au0.85Al0.15 is a particularly promising candidate for both wavelength- and angular-dependent sensors due to its high sensitivity (18967 nm/RIU and 162.7 °/RIU) and good peak definition. Furthermore, using partial density-of-state calculations assisted by machine learning, we obtain the dielectric function of the films, showing an excellent agreement with our experimental results. The alloying approach assisted by computational prediction of the samples’ physical properties has the potential to accelerate the discovery of novel materials for plasmonic sensors with high sensitivity and excellent functioning capabilities at elevated temperatures. Materials science ; Surface plasmon resonance ; Thin film ; Plasmon ; Refractive index ; Bimetallic strip ; Dielectric ; Ellipsometry ; Wavelength ; Sputter deposition ; Sputtering ; Optoelectronics ; Analytical Chemistry (journal) ; Nanoparticle ; Nanotechnology ; Metal ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Photonic and Optical Devices
Authors: Qian Chen | Ke Zhou | Yunhai Xiong | Xiang Chen | Zhi Li
Source: Chemistry of Materials (article, 2022-05-13)
Field: Materials Science — Materials Chemistry

High-Throughput Discovery and Investigation of Auxetic Two-Dimensional Crystals Auxetic two-dimensional (2D) crystals that yield a negative Poisson’s ratio (NPR) exhibit unusual transverse expansion when stretched. Such materials have great potential for electronic skins, energy harvesting, mechatronic shape display devices, and strain sensors. However, most 2D crystals have positive Poisson’s ratios, and the number of existing 2D materials with NPR is limited. Accelerating the discovery of these rare auxetic materials remains a long-term challenge. Utilizing high-throughput computations, this work discovers 108 stable 2D crystals with negative in-plane Poisson’s ratios that could be successfully fabricated from the 4047 2D crystals in the Computational 2D Materials Database (C2DB). We find 4 crystal types with the potential for significant auxetic effect regardless of the orientations, that is, MX2O8, MX2O6, MX3, and MXX′. The auxetic effects for the former two are simply from crystal geometry, while further first-principles calculations based on MX3 (M = Sc, Ti, V, Cr, ... and X = Cl, Br, I) and MXX′ (M = Mn, Fe, Co, Ni; X = Li, Na, Mg, Ca, Sr; and X′ = Si, Ge, P, As, Sb) reveal that the NPR originates from both the crystal geometry and electronic structures. Because some databases lack the information of elasticity and stability, deep learning models are trained using C2DB and employed to find 92 other stable 2D crystals with NPR. The band gaps of the 200 stable auxetic 2D crystals discovered in our work cover a wide range from 0 to 5.7 eV, which provide abundant candidates for nanoscale electronics with intriguing physicochemical properties. Auxetics ; Materials science ; Poisson's ratio ; Crystal (programming language) ; Work (physics) ; Nanoscopic scale ; Crystallography ; Poisson distribution ; Nanotechnology ; Geometry ; Condensed matter physics ; Composite material ; Thermodynamics ; Computer science ; Physics ; Chemistry ; Mathematics MXene and MAX Phase Materials ; Bone Tissue Engineering Materials ; Cellular and Composite Structures
Authors: Benedikt Prifling | Matthias Neumann | Simon Hein | Timo Danner | Emanuel Heider | Alice Hoffmann | Philipp Rieder | André Hilger | Markus Osenberg | Ingo Manke | Margret Wohlfahrt‐Mehrens | Arnulf Latz | Volker Schmidt
Source: Energy Technology (article, 2022-08-27)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative Comparison of Different Approaches for Reconstructing the Carbon‐Binder Domain from Tomographic Image Data of Cathodes in Lithium‐Ion Batteries and Its Influence on Electrochemical Properties It is well known that the spatial distribution of the carbon‐binder domain (CBD) offers a large potential to further optimize lithium‐ion batteries. However, it is challenging to reconstruct the CBD from tomographic image data obtained by synchrotron tomography. Herein, several approaches are considered to segment 3D image data of two different cathodes into three phases, namely, active material, CBD, and pores. More precisely, it is focused on global thresholding, a local closing approach based on energy‐dispersive X‐ray spectroscopy data, a k ‐means clustering method, and a procedure based on a neural network that has been trained by correlative microscopy, i.e., based on data gained by synchrotron tomography and focused ion beam scanning electron microscopy data representing the same electrode. The impact of the considered segmentation approaches on morphological characteristics as well as on the resulting performance by spatially resolved transport simulations is quantified. Furthermore, experimentally determined electrochemical properties are used to identify an appropriate range for the effective transport parameter of the CBD. The developed methodology is applied to two differently manufactured cathodes, namely, an ultrathick unstructured cathode and a two‐layer cathode with varying CBD content in both layers. This comparison elucidates the impact of a specific structuring concept on the 3D microstructure of cathodes. Cathode ; Materials science ; Lithium (medication) ; Synchrotron ; Optics ; Chemistry ; Physics Electron and X-Ray Spectroscopy Techniques ; Electrical and Bioimpedance Tomography ; Digital Image Processing Techniques
Authors: Dylan S. Cousins | William G. Otto | Asif Hasan Rony | Kristian P. Pedersen | John E. Aston | David B. Hodge
Source: Frontiers in Energy Research (article, 2022-02-17)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R 2 v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R 2 v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations. Corn stover ; Biomass (ecology) ; Partial least squares regression ; Lignocellulosic biomass ; Biological system ; Biorefinery ; Abundance (ecology) ; Chemical composition ; Kriging ; Environmental science ; Raw material ; Stover ; Chemistry ; Lignin ; Agronomy ; Computer science ; Ecology ; Machine learning ; Biology ; Hydrolysis Spectroscopy and Chemometric Analyses ; Biofuel production and bioconversion ; Food composition and properties
Authors: Ankit Shrivastava | Jingxiao Liu | Kaushik Dayal | Hae Young Noh
Source: Mathematics and Mechanics of Solids (article, 2022-01-03)
Field: Materials Science — Materials Chemistry

Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning This work presents a machine-learning approach to predict peak-stress clusters in heterogeneous polycrystalline materials. Prior work on using machine learning in the context of mechanics has largely focused on predicting the effective response and overall structure of stress fields. However, their ability to predict peak – which are of critical importance to failure – is unexplored, because the peak-stress clusters occupy a small spatial volume relative to the entire domain, and hence require computationally expensive training. This work develops a deep-learning-based convolutional encoder–decoder method that focuses on predicting peak-stress clusters, specifically on the size and other characteristics of the clusters in the framework of heterogeneous linear elasticity. This method is based on convolutional filters that model local spatial relations between microstructures and stress fields using spatially weighted averaging operations. The model is first trained against linear elastic calculations of stress under applied macroscopic strain in synthetically generated microstructures, which serves as the ground truth. The trained model is then applied to predict the stress field given a (synthetically generated) microstructure and then to detect peak-stress clusters within the predicted stress field. The accuracy of the peak-stress predictions is analyzed using the cosine similarity metric and by comparing the geometric characteristics of the peak-stress clusters against the ground-truth calculations. It is observed that the model is able to learn and predict the geometric details of the peak-stress clusters and, in particular, performed better for higher (normalized) values of the peak stress as compared to lower values of the peak stress. These comparisons showed that the proposed method is well-suited to predict the characteristics of peak-stress clusters. Stress (linguistics) ; Context (archaeology) ; Stress field ; Encoder ; Computer science ; Ground truth ; Linear elasticity ; Field (mathematics) ; Algorithm ; Similarity (geometry) ; Artificial intelligence ; Mathematics ; Finite element method ; Structural engineering ; Engineering ; Geology Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bo Ma | Chuanzeng Liu | Jifang Hu | Kai Liu | Fuyang Zhao | Junqiang Wang | Xin Zhao | Zhenhua Guo | Lijuan Song | Yongcai Lai | Kefei Tan
Source: Plants (article, 2022-04-29)
Field: Chemistry — Analytical Chemistry

Intelligent Identification and Features Attribution of Saline–Alkali-Tolerant Rice Varieties Based on Raman Spectroscopy Planting rice in saline-alkali land can effectively improve saline-alkali soil and increase grain yield, but traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the Python machine deep learning method was used to analyze the Raman molecular spectroscopy of rice and assist in feature attribution, in order to study a fast and efficient identification method of saline-alkali-tolerant rice varieties. A total of 156 Raman spectra of four rice varieties (two saline-alkali-tolerant rice varieties and two saline-alkali-sensitive rice varieties) were analyzed, and the wave crests were extracted by an improved signal filtering difference method and the feature information of the wave crest was automatically extracted by scipy.signal.find_peaks. Select K Best (SKB), Recursive Feature Elimination (RFE) and Select F Model (SFM) were used to select useful molecular features. Based on these feature selection methods, a Logistic Regression Model (LRM) and Random Forests Model (RFM) were established for discriminant analysis. The experimental results showed that the RFM identification model based on the RFE method reached a higher recognition rate of 89.36%. According to the identification results of RFM and the identification of feature attribution materials, amylum was the most significant substance in the identification of saline-alkali-tolerant rice varieties. Therefore, an intelligent method for the identification of saline-alkali-tolerant rice varieties based on Raman molecular spectroscopy is proposed. Artificial intelligence ; Saline ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; GABA and Rice Research
Authors: Juan Pedro Torres | Andrés Caro | Mar Ávila | Trinidad Pérez‐Palacios | Teresa Antequera | Pablo G. Rodríguez
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

A Computer-Aided Inspection System to Predict Quality Characteristics in Food Technology Physicochemical and sensory analyses are commonly used to determine the quality characteristics of food samples in Food Industries. These methods are tedious, laborious, produce chemical residues, and involve the destruction of the samples. For the meat industries, this work proposes a non-invasive and non-destructive computer-aided inspection system, based on computer vision and ensemble machine learning techniques. The paper presents all the possibilities for the development of the system, making an exhaustive comparison of different algorithms used to extract features from the images of the samples, and various machine learning approaches, studying up to 6160 different models, and selecting the top 110 for the ensemble proposal. The system determines all the physicochemical, textural, and sensory quality characteristics of pork and beef loins in four meat states (fresh, thawed, cooked, and cured) with good precision, being a real alternative to the usual methods for the Food Industry. Computer science ; Quality (philosophy) ; Computer-aided ; Artificial intelligence ; Food industry ; Machine learning ; Pattern recognition (psychology) ; Food science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies