jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siddharth Gupta | Sunayana Gupta | Arushi Gupta
Source: Materials (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Reimagining Carbon Nanomaterial Analysis: Empowering Transfer Learning and Machine Vision in Scanning Electron Microscopy for High-Fidelity Identification In this report, we propose a novel technique for identifying and analyzing diverse nanoscale carbon allotropes using scanning electron micrographs. By precisely controlling the quenching rates of undercooled molten carbon through laser irradiation, we achieved the formation of microdiamonds, nanodiamonds, and Q-carbon films. However, standard laser irradiation without proper undercooling control leads to the formation of sparsely located diverse carbon polymorphs, hindering their discovery and classification through manual analyses. To address this challenge, we applied transfer-learning approaches using convolutional neural networks and computer vision techniques to achieve allotrope discovery even with sparse spatial presence. Our method achieved high accuracy rates of 92% for Q-carbon identification and 94% for distinguishing it from nanodiamonds. By leveraging scanning electron micrographs and precise undercooling control, our technique enables the efficient identification and characterization of nanoscale carbon structures. This research significantly contributes to the advancement of the field, providing automated tools for Q-materials and carbon polymorph identification. It opens up new opportunities for the further exploration of these materials in various applications. Materials science ; Carbon fibers ; Nanotechnology ; Scanning electron microscope ; Nanoscopic scale ; Characterization (materials science) ; Computer science ; Artificial intelligence ; Composite number ; Composite material Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Kang‐Hyun Lee | Hyoung Jun Lim | Gun Jin Yun
Source: arXiv (Cornell University) (preprint, 2023-01-22)
Field: Materials Science — Materials Chemistry

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models This paper puts forward an integrated microstructure design methodology that replaces the common existing design approaches: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials using deep-learned generative and surrogate models. The long-standing issue of microstructure reconstruction is well addressed in this study using a new class of state-of-the-art generative model, the diffusion-based generative model (DGM). Moreover, the conditional formulation of DGM for guidance to the embedded desired material properties with a transformer-based attention mechanism enables the inverse design of multifunctional composites. A convolutional neural network (CNN)-based surrogate model is utilized to analyze the nonlinear material behavior to facilitate the prediction of material properties for building microstructure-property linkages. Combined, these generative and surrogate models enable large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) and iterative reconstruction methods. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications. Surrogate model ; Computer science ; Finite element method ; Generative model ; Artificial intelligence ; Machine learning ; Materials science ; Generative grammar ; Structural engineering ; Engineering Machine Learning in Materials Science ; Topology Optimization in Engineering
Authors: Michael D. Himes | Joseph Harrington | Atılım Güneş Baydin
Source: arXiv (Cornell University) (preprint, 2023-03-31)
Field: Chemistry — Spectroscopy

Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enables more complex chemistry models within retrieval frameworks. Yet, many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here, we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders of magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error &lt;0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach. Parameterized complexity ; Exoplanet ; Computer science ; Gibbs sampling ; Bayesian inference ; Algorithm ; Bayesian probability ; Chemistry ; Artificial intelligence ; Stars Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies
Authors: A. Ananda Rao | Stephen Carr | Charles Snider | D. E. Feldman | Chandrasekhar Ramanathan | V. F. Mitrović
Source: Physical Review Research (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted determination of electronic correlations from magnetic resonance In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here we study how machine learning can extract material parameters and help interpret magnetic response experiments. A low-dimensional representation that classifies the strength and range of the interaction is discovered by unsupervised learning. Supervised learning generates models that predict the spatial extent of electronic correlations and the total interaction strength. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials. Spins ; Representation (politics) ; Artificial intelligence ; Coupling (piping) ; Hyperfine structure ; Computer science ; Spin (aerodynamics) ; Range (aeronautics) ; Coupling strength ; Physics ; Machine learning ; Condensed matter physics ; Statistical physics ; Pattern recognition (psychology) ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Advanced NMR Techniques and Applications
Authors: Tianyu Fang | Zhenyu Li | Jialin Zhang | Dawei Qi | Lei Zhang
Source: Journal of Imaging (article, 2023-07-30)
Field: Chemistry — Organic Chemistry

Open-Set Recognition of Wood Species Based on Deep Learning Feature Extraction Using Leaves An open-set recognition scheme for tree leaves based on deep learning feature extraction is presented in this study. Deep learning algorithms are used to extract leaf features for different wood species, and the leaf set of a wood species is divided into two datasets: the leaf set of a known wood species and the leaf set of an unknown species. The deep learning network (CNN) is trained on the leaves of selected known wood species, and the features of the remaining known wood species and all unknown wood species are extracted using the trained CNN. Then, the single-class classification is performed using the weighted SVDD algorithm to recognize the leaves of known and unknown wood species. The features of leaves recognized as known wood species are fed back to the trained CNN to recognize the leaves of known wood species. The recognition results of a single-class classifier for known and unknown wood species are combined with the recognition results of a multi-class CNN to finally complete the open recognition of wood species. We tested the proposed method on the publicly available Swedish Leaf Dataset, which includes 15 wood species (5 species used as known and 10 species used as unknown). The test results showed that, with F1 scores of 0.7797 and 0.8644, mixed recognition rates of 95.15% and 93.14%, and Kappa coefficients of 0.7674 and 0.8644 under two different data distributions, the proposed method outperformed the state-of-the-art open-set recognition algorithms in all three aspects. And, the more wood species that are known, the better the recognition. This approach can extract effective features from tree leaf images for open-set recognition and achieve wood species recognition without compromising tree material. Artificial intelligence ; Pattern recognition (psychology) ; Classifier (UML) ; Feature extraction ; Test set ; Computer science ; Mathematics Wood and Agarwood Research ; Plant and animal studies ; Plant Pathogens and Fungal Diseases
Authors: Cheng-Ye Lü | Yingwei Chen | Mu-Ting Xie | Xue-Yang Li | Hongyu Yu | Yang Zhong | Hongjun Xiang
Source: Acta Physica Sinica (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First-principles calculation method for periodic system under external electromagnetic field The influence of electromagnetic field on material characteristics remains a pivotal concern in scientific researches. Nonetheless, in the realm of computational condensed matter physics, the extension of traditional density functional theory to scenarios inclusive of external electromagentic fields poses considerable challenges. These issues largely stem from the disruption of translational symmetry by external fields inherent in periodic systems, rendering Bloch's theorem inoperative. Consequently, the using the first-principles method to calculate material properties in the presence of external fields becomes an intricate task, especially in circumstances where the external field cannot be approximated as a minor perturbation. Over the past two decades, a significant number of scholars within the field of computational condensed matter physics have dedicated their efforts to the formulation and refinement of first-principles computational method adopted in handling periodic systems subjected to finite external fields. This work attempts to systematically summarize these theoretical methods and their applications in the broad spectrum, including but not limited to ferroelectric, piezoelectric, ferromagnetic, and multiferroic domains. In the first part of this paper, we provide a succinct exposition of modern theory of polarization and delineate the process of constructing two computation methods in finite electric fields predicated by this theory in conjunction with density functional theory. The succeeding segment focuses on the integration of external magnetic fields into density functional theory and examining the accompanying computational procedures alongside the challenges they present. In the third part, we firstly review the first-principles effective Hamiltonian method, which is widely used in the study of magnetic, ferroelectric and multiferroic systems, and its adaptability to the case involving external fields. Finally, we discuss the exciting developments of constructing effective Hamiltonian models by using machine learning neural network methods , and their extensions according to the external fields. Physics ; Theoretical physics ; Electromagnetism ; Computer science ; Magnetic field ; Statistical physics ; Quantum mechanics Multiferroics and related materials ; Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials
Authors: Francesca Venturini | Michela Sperti | Umberto Michelucci | Arnaud Gucciardi | Vanessa M. Martos | Marco A. Deriu
Source: arXiv (Cornell University) (preprint, 2023-01-10)
Field: Chemistry — Analytical Chemistry

Dataset of Fluorescence Spectra and Chemical Parameters of Olive Oils This dataset encompasses fluorescence spectra and chemical parameters of 24 olive oil samples from the 2019-2020 harvest provided by the producer Conde de Benalua, Granada, Spain. The oils are characterized by different qualities: 10 extra virgin olive oil (EVOO), 8 virgin olive oil (VOO), and 6 lampante olive oil (LOO) samples. For each sample, the dataset includes fluorescence spectra obtained with two excitation wavelengths, oil quality, and five chemical parameters necessary for the quality assessment of olive oil. The fluorescence spectra were obtained by exciting the samples at 365 nm and 395 nm under identical conditions. The dataset includes the values of the following chemical parameters for each olive oil sample: acidity, peroxide value, K270, K232, ethyl esters, and the quality of the samples (EVOO, VOO, or LOO). The dataset offers a unique possibility for researchers in food technology to develop machine learning models based on fluorescence data for the quality assessment of olive oil due to the availability of both spectroscopic and chemical data. The dataset can be used, for example, to predict one or multiple chemical parameters or to classify samples based on their quality from fluorescence spectra. Olive oil ; Fluorescence ; Chemistry ; Spectral line ; Analytical Chemistry (journal) ; Quality (philosophy) ; Environmental chemistry ; Food science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: Yunsheng Liu | Xingfeng He | Yifei Mo
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Materials Science — Materials Chemistry

Discrepancies and Error Evaluation Metrics for Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLPs) are a promising technique for atomic modeling. While high accuracy and small errors are widely reported for MLPs, an open concern is whether MLPs can accurately reproduce atomistic dynamics and related physical properties in their applications in molecular dynamics (MD) simulations. In this study, we examine the current state-of-the-art MLPs and uncover a number of discrepancies related to atom dynamics, defects, and rare events (REs), in their MD simulations compared to ab initio methods. Our findings reveal that low averaged errors by current MLP testing are insufficient, leading us to develop novel quantitative metrics that better indicate the accurate prediction of related properties by MLPs in MD simulations. The MLPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the developed evaluation metrics, and the proposed process of developing such metrics are general to MLPs, thus providing valuable guidance for future testing, development, and improvements of accurate, robust, and reliable MLPs for atomistic modeling. Computer science ; Machine learning ; Molecular dynamics ; Artificial intelligence ; Process (computing) ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Omar Abou El Kheir | Luigi Bonati | Michele Parrinello | Marco Bernasconi
Source: arXiv (Cornell University) (preprint, 2023-04-06)
Field: Materials Science — Materials Chemistry

Unraveling the Crystallization Kinetics of the Ge$_2$Sb$_2$Te$_5$ Phase Change Compound with a Machine-Learned Interatomic Potential The phase change compound Ge$_2$Sb$_2$Te$_5$ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices. Crystallization ; Neuromorphic engineering ; Materials science ; Phase (matter) ; Phase-change memory ; Kinetics ; Amorphous solid ; Chemical physics ; Molecular dynamics ; Interatomic potential ; Computer science ; Nanotechnology ; Thermodynamics ; Artificial neural network ; Chemistry ; Physics ; Computational chemistry ; Crystallography ; Artificial intelligence Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Functional Brain Connectivity Studies
Authors: Yasuhiro Yoshida | Masayoshi Ishikawa | Fumihiro Sasajima | Shigeo Ohkoshi | Masami Takano
Source:  (article, 2023-04-18)
Field: Materials Science — Surfaces, Coatings and Films

Machine-learning-based error detection modeling and feature scoring for error cause analysis of CD-SEMs The measurement process is important in managing semiconductor device yield, which is affected by the availability of measurement equipment such as critical dimension scanning electron microscopes (CD-SEMs). Here, decreasing CD-SEM availability is caused by measurement errors when inappropriate measurement recipes are used. To improve CD-SEM availability, we developed a machine-learning-based error analysis method to identify error causes and x measurement conditions by using accumulated CD-SEM data. However, a single error analysis model can be applied to only a single semiconductor product because different semiconductor products have different data distribution even if they use the same recipe. Additionally, manufacturers often modify recipes every few months. As a result, an error-cause analysis method needs to be able to easily adapt to new recipes. Therefore, we developed a three-stage method that consists of error detection modeling, feature scoring, and error cause estimation on the basis of high-scoring features. Because we found the top scoring features do NOT change as the feature distribution changes when the error causes are the same, the error cause estimation on the basis of high-scoring features enable to be applied to different semiconductor products and new recipes. We evaluated our method with actual operational data, and estimated error causes that often correspond with the results of manual analysis by skilled engineers. Computer science ; Process (computing) ; Feature (linguistics) ; Error analysis ; Observational error ; Error detection and correction ; Data mining ; Approximation error ; Reliability engineering ; Pattern recognition (psychology) ; Artificial intelligence ; Machine learning ; Statistics ; Algorithm ; Engineering ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection
Authors: Steven Lindner | Ivor Lončarić | Lovro Vrček | M. Alducin | J. I. Juaristi | Peter Saalfrank
Source: ChemRxiv (preprint, 2023-05-05)
Field: Chemistry — Spectroscopy

Femtosecond laser-induced desorption of hydrogen molecules from Ru(0001): A systematic study based on machine-learned potentials Femtosecond laser-induced dynamics of molecules on metal surfaces can be seamlessly simulated with all nuclear degrees of freedom using ab-initio molecular dynamics with electronic friction (AIMDEF) and stochastic forces which are a function of a time-dependent electronic temperature. This has recently been demonstrated for hot-electron mediated desorption of hydrogen molecules from a Ru(0001) surface covered with H and D atoms [Juaristi et al., Phys. Rev. B 2017, 95, 125439]. Unfortunately, AIMDEF simulations come with a very large computational expense that severely limits statistics and propagation times. To keep ab-initio accuracy and allow for better statistical sampling, we have developed a neural network interatomic potential of hydrogen on the Ru(0001) surface based on data from ab-initio molecular dynamics simulations of recombinative desorption. Using this potential we simulated femtosecond laser-induced recombinative desorption using varying unit cells, coverages, laser fluences, and isotope ratios with reliable statistics. As a result, we can systematically study a wide range of these parameters and follow dynamics over longer times than hitherto possible, demonstrating that our methodology is a promising way to realistically simulate femtosecond laser-induced dynamics of molecules on metals. Moreover, we show that previously used cell sizes and propagation times were too small to obtain converged results. Femtosecond ; Ab initio ; Desorption ; Laser ; Molecular dynamics ; Ab initio quantum chemistry methods ; Atomic physics ; Hydrogen ; Materials science ; Molecule ; Chemical physics ; Molecular physics ; Chemistry ; Physics ; Computational chemistry ; Optics ; Physical chemistry Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Cumhur Yıldırım | Nilgün Baydoğan
Source: Materials Letters (article, 2023-01-21)
Field: Materials Science — Materials Chemistry

Machine learning analysis on critical structural factors of Al:ZnO (AZO) films Materials science ; Crystallinity ; Doping ; Electrical resistivity and conductivity ; Band gap ; Dislocation ; Thin film ; Engineering physics ; Condensed matter physics ; Composite material ; Nanotechnology ; Optoelectronics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Electronic and Structural Properties of Oxides