jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ping Li | Bang Liu | Weixi Zhang | Zhi‐Xin Guo
Source: arXiv (Cornell University) (preprint, 2023-09-04)
Field: Materials Science — Materials Chemistry

Progress on two-dimensional ferrovalley materials The electron's charge and spin degrees of freedom are at the core of modern electronic devices. With the in-depth investigation of two-dimensional materials, another degree of freedom, valley, has also attracted tremendous research interest. The intrinsic spontaneous valley polarization in two-dimensional magnetic systems, ferrovalley material, provides convenience for detecting and modulating the valley. In this review, we first introduce the development of valleytronics. Then, the valley polarization forms by the p, d, and f-orbit that are discussed. Following, we discuss the investigation progress of modulating the valley polarization of two-dimensional ferrovalley materials by multiple physical fields, such as electric, stacking mode, strain, and interface. Finally, we look forward to the future developments of valleytronics. Valleytronics ; Polarization (electrochemistry) ; Stacking ; Physics ; Condensed matter physics ; Engineering physics ; Chemistry ; Nuclear magnetic resonance ; Spintronics ; Ferromagnetism 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Ga2O3 and related materials
Authors: Yuan Li | Zhenyu Yang | Pierre‐André Cazade | Jin‐Liang Lin | Zhou Cao | Ningyue Chen | Dongdong Zhang | Lian Duan | Christian A. Nijhuis | Damien Thompson
Source: Research Square (preprint, 2023-03-30)
Field: Materials Science — Materials Chemistry

Mechanically Controlled High-Performance Molecular Photoswitch Photoswitch ; Chemistry ; Photochemistry Photochromic and Fluorescence Chemistry ; Molecular Junctions and Nanostructures ; Advanced biosensing and bioanalysis techniques
Authors: Jeremy Cleeman | Kian Agrawala | Rajiv Malhotra
Source: arXiv (Cornell University) (preprint, 2023-04-29)
Field: Materials Science — Materials Chemistry

Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge Machine Learning (ML) is of increasing interest for modeling parametric effects in manufacturing processes. But this approach is limited to established processes for which a deep physics-based understanding has been developed over time, since state-of-the-art approaches focus on reducing the experimental and/or computational costs of generating the training data but ignore the inherent and significant cost of developing qualitatively accurate physics-based models for new processes . This paper proposes a transfer learning based approach to address this issue, in which a ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target). The novelty lies in pushing the boundaries of the qualitative accuracy demanded of the source model, which is assumed to be high in the literature, and is the root of the high model development cost. Our approach is evaluated for modeling the printed line width in Fused Filament Fabrication. Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%. Computer science ; Process (computing) ; Novelty ; Machine learning ; Artificial intelligence ; Parametric statistics ; Focus (optics) ; Industrial engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Electron and X-Ray Spectroscopy Techniques
Authors: Yiming Yao | Luyang Jie | Yilong Guo | Yongkang Liu
Source: Advances in Engineering Technology Research (article, 2023-12-28)
Field: Chemistry — Analytical Chemistry

SPLConv: Channel-Separated Convolution Using Large Convolution Kernels In modern society, chips play a crucial role in various aspects of everyday life. Nevertheless, the diverse chip manufacturing processes can introduce defects, making the detection of faulty chips imperative. Convolutional neural networks (CNNs) are extensively employed in computer vision due to their ability to deliver high-performance results. However, this comes at the expense of significant computational resources. Current research focuses on employing pruning or quantization techniques to minimize the size of model parameters and investigate the development of lightweight models. This paper proposes a large depth-wise convolutional module called split-based large convolutional (SPLConv), which is devised and optimized from the perspective of split-based convolutional operations (SPConv). SPLConv not only disregards redundant features in the convolutional layer but also enhances spatial information capture, thereby mitigating excessive computational overhead. Experiment demonstrate that SPLConv effectively reduces complexity and computational cost while delivering commendable performance. Convolution (computer science) ; Channel (broadcasting) ; Computer science ; Overlap–add method ; Circular convolution ; Mathematics ; Artificial intelligence ; Mathematical analysis ; Fourier transform ; Telecommunications ; Fourier analysis Spectroscopy and Chemometric Analyses ; Advanced Data Compression Techniques ; NMR spectroscopy and applications
Authors: Tomojit Ghosh | M. Kirby
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Chemistry — Analytical Chemistry

Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder We propose a new supervised dimensionality reduction technique called Supervised Linear Centroid-Encoder (SLCE), a linear counterpart of the nonlinear Centroid-Encoder (CE) \citep{ghosh2022supervised}. SLCE works by mapping the samples of a class to its class centroid using a linear transformation. The transformation is a projection that reconstructs a point such that its distance from the corresponding class centroid, i.e., centroid-reconstruction loss, is minimized in the ambient space. We derive a closed-form solution using an eigendecomposition of a symmetric matrix. We did a detailed analysis and presented some crucial mathematical properties of the proposed approach. %We also provide an iterative solution approach based solving the optimization problem using a descent method. We establish a connection between the eigenvalues and the centroid-reconstruction loss. In contrast to Principal Component Analysis (PCA) which reconstructs a sample in the ambient space, the transformation of SLCE uses the instances of a class to rebuild the corresponding class centroid. Therefore the proposed method can be considered a form of supervised PCA. Experimental results show the performance advantage of SLCE over other supervised methods. Centroid ; Principal component analysis ; Mathematics ; Eigenvalues and eigenvectors ; Dimensionality reduction ; Pattern recognition (psychology) ; Algorithm ; Encoder ; Transformation (genetics) ; Projection (relational algebra) ; Artificial intelligence ; Supervised learning ; Eigendecomposition of a matrix ; Computer science ; Statistics ; Artificial neural network Spectroscopy and Chemometric Analyses ; Face and Expression Recognition ; Optical Imaging and Spectroscopy Techniques
Authors: Carter Rhea | Laurie Rousseau-Nepton | Ismaël Moumen | S. Prunet | Julie Hlavacek-Larrondo | Kathryn Grasha | Carmelle Roberts | C. Morisset | G. Stasińska | N. Vale Asari | Justine Giroux | Anna F. McLeod | Marie-Lou Gendron-Marsolais | Junfeng Wang | J. Lyman | L. Chemin
Source: arXiv (Cornell University) (preprint, 2023-06-20)
Field: Chemistry — Analytical Chemistry

A Machine Learning Approach to Galactic Emission-Line Region Classification Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using \texttt{cloudy}, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic \hii{} regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [{\sc O\,iii}]$\lambda5007$/H$β$, [{\sc N\,ii}]$\lambda6583$/H$α$, ([{\sc S\,ii}]$\lambda6717$+[{\sc S\,ii}]$\lambda6731$)/H$α$. We also tested the impact of the addition of the [{\sc O\,ii}]$\lambda3726,3729$/[{\sc O\,iii}]$\lambda5007$ line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints. Astrophysics ; Physics ; Line (geometry) ; Luminosity ; Emission spectrum ; Artificial neural network ; Photoionization ; Doubly ionized oxygen ; Planetary nebula ; Artificial intelligence ; Machine learning ; Supernova ; Computer science ; Spectral line ; Galaxy ; Astronomy ; Mathematics ; Stars ; Ionization Spectroscopy and Chemometric Analyses ; Spectroscopy and Laser Applications ; SAS software applications and methods
Authors: Bo Chen
Source: Research Square (preprint, 2023-11-28)
Field: Chemistry — Spectroscopy

ASAP: an automatic sequential assignment program for congested multidimensional solid state NMR spectra <title>Abstract</title> Accurate signal assignments can be challenging for congested solid-state NMR (ssNMR) spectra. We describe an automatic sequential assignment program (ASAP) to partially overcome this challenge. ASAP takes three input files: the residue type assignments (RTAs) determined from the better-resolved NCACX spectrum, the full peak list of the NCOCX spectrum, and the protein sequence. It integrates our auto-residue type assignment strategy (ARTIST) with the Monte Carlo simulated annealing (MCSA) algorithm to overcome the hurdle for accurate signal assignments caused by incomplete side-chain resonances and spectral congestion. Combined, ASAP demonstrates robust performance and reduces the time of signal assignments of large proteins (&gt; 200 residues) from years to days. Solid-state ; Simulated annealing ; Residue (chemistry) ; Spectral line ; Computer science ; Monte Carlo method ; Algorithm ; Sequence (biology) ; Solid-state nuclear magnetic resonance ; SIGNAL (programming language) ; Chemistry ; Nuclear magnetic resonance ; Mathematics ; Physics ; Physical chemistry ; Statistics Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Eungjune Shim | Eunjung Ju | Myung Geol Choi
Source: Journal of the Korea Computer Graphics Society (article, 2023-07-01)
Field: Materials Science — Polymers and Plastics

Drape Simulation Estimation for Non-Linear Stiffness Model In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for nonlinear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation. Computer science ; Stiffness ; Process (computing) ; Nonlinear system ; Simulation ; Similarity (geometry) ; Task (project management) ; Artificial neural network ; Linear model ; Artificial intelligence ; Machine learning ; Engineering ; Structural engineering Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Computer Graphics and Visualization Techniques
Authors: David Robinson | Joshua D. Sugar | Xiaowang Zhou
Source:  (article, 2023-07-01)
Field: Materials Science — Surfaces, Coatings and Films

4D STEM Simulation of Defects in Palladium Nanoparticles Palladium ; Nanoparticle ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Catalysis Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Ганна Безпрозванних | Євгеній Москвітін
Source:  (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

СТРАТЕГІЯ УПРАВЛІННЯ СТАРІННЯМ КАБЕЛІВ АТОМНИХ ЕЛЕКТРИЧНИХ СТАНЦІЙ Cable aging management is an integral and mandatory requirement for continuous safety and reliable operation of nuclear power plants due to the extension of their service life. The research of the scientific community is focused on the adaptation of electrical non-destructive methods of monitoring cable samples in laboratory conditions to determine the state of their electrical insulation. The performed analysis of NPP cable aging management programs proves the need for new representative results of cable research to determine characteristics using special control methods and appropriate equipment. Emphasis is placed on the strategy of cable aging management with the involvement of scientists with professional experience in conducting complex diagnostic examinations with qualified analysis of the obtained results based on the most modern technologies. Effective methods, techniques, and modern techniques for the assessment of the technical condition of NPP cables are presented. Nuclear power plant ; Nuclear power ; Service (business) ; Control (management) ; Technical management ; Life extension ; Management system High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Advanced Scientific Techniques and Applications
Authors: Dattatraya R Hipparkar | Mukesh Singh Baghel | Sunil Chandel
Source: International Journal of Engineering Trends and Technology (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

Characterization of Acceleration Shock Pulse by Finite Element Analysis and Validation by Shock Testing on Heavy Mass Test Objects for Defense Applications Shock Testing is an essential evaluation criterion to assess the performance of newly designed systems for their dynamic performance. There are various methods to generate a shock pulse for shock testing, like free fall drop test, horizontal impact, kick, and explosion. The primary purpose of this research is to design and characterize the pulse shaper of neoprene rubber for Shock testing of heavy objects. This paper presents an implicit Finite-Element (FE) model of the impact of a test table on a rubber disc. The FE model is used to study the influence of drop height, the mass of the object, and the thickness of the pulse shaper on shock pulse characteristics. Shock tests were also conducted on the test stand, and a comparison of the results from the FE and experimental data is presented in this paper. Shock (circulatory) ; Acceleration ; Characterization (materials science) ; Finite element method ; Structural engineering ; Physics ; Nuclear engineering ; Computer science ; Engineering ; Classical mechanics ; Optics ; Medicine High-Velocity Impact and Material Behavior ; Transportation Safety and Impact Analysis ; Electromagnetic Launch and Propulsion Technology
Authors: Rizky Prabowo | Dewi Lestari
Source: Teknologi (article, 2023-04-11)
Field: Chemistry — Analytical Chemistry

Klasifikasi Image Tumbuhan Obat Sirih Hijau dan Sirih Merah Menggunakan Metode Decision Tree Tumbuhan obat atau lebih disingkat dengan sebutan TOGA merupakan tanaman hasil budidaya rumahan maupun tanaman yang tumbuh secara liar pada pekarangan rumah yang berguna sebagai pengganti obat-obatan kimia. Daun sirih merupakan salah satu jenis tumbuhan obat yang memiliki banyak jenis. Dengan perkembangan teknologi yang semakin pesat, tentunya dapat digunakan untuk mengklasifikasikan daun tumbuhan obat sirih hijau dan sirih merah. Data yang digunakan yaitu 1000 data image sirih hijau dan 1000 data image sirih merah dengan total keseluruhan data sebanyak 2000 data image. Model klasifikasi Decision Tree dilakukan dengan menggunakan 6 parameter feature ekstraksi tekstur Gray Level Co-Occurance Matric (GLCM) yaitu dissimiliarity, correlation, homogeneity, contrast, ASM, dan energy. Perbandingan persentasi data latih dan data uji yang digunakan ialah 60%:40%, 65%:35%, 70%:30%, 75%:25%, dan 80%:20%. Confusion matriks digunakan untuk mengukur hasil pengujian klasifikasi. Hasil pengujian dengan metode Decision Tree diperoleh nilai rata-rata akurasi sebesar 98%, presisi 98,3%. recall 97,1% dan f1-score 97,5%. Adapun hasil dari pengujian dengan metode Naïve Bayes didapatkan hasil rata-rata accuracy sebesar 92,1%, precission 87,5%. recall 97,8% dan F1 Score 92,3%. Dari hasil perbandingan tersebut dapat disimpulkan bahwa metode Decision Tree lebih unggul dibandingkan dengan metode Naïve Bayes. Decision tree ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Gabriel Marton | Mark A. J. Koenis | Hongbing Liu | Carole A. Bewley | Wybren Jan Buma | Valentin Paul Nicu
Source: Angewandte Chemie (article, 2023-06-19)
Field: Chemistry — Spectroscopy

An Artificial Intelligence Approach for Tackling Conformational Energy Uncertainties in Chiroptical Spectroscopies Abstract Determination of the absolute configuration of chiral molecules is a prerequisite for obtaining a fundamental understanding in any chirality‐related field. The interaction with polarised light has proven to be a powerful means to determine this absolute configuration, but its application rests on the comparison between experimental and computed spectra for which the inherent uncertainty in conformational Boltzmann factors has proven to be extremely hard to tackle. Here we present a novel approach that overcomes this issue by combining a genetic algorithm that identifies the relevant conformers by accounting for the uncertainties in DFT relative energies, and a hierarchical clustering algorithm that analyses the trends in the spectra of the considered conformers and identifies on‐the‐fly when a given chiroptical technique is not able to make reliable predictions. The effectiveness of this approach is demonstrated by considering the challenging cases of papuamine and haliclonadiamine, two bis‐indane natural products with eight chiral centres and considerable conformational heterogeneity that could not be assigned unambiguously with current approaches. Conformational isomerism ; Chirality (physics) ; Absolute configuration ; Field (mathematics) ; Chemistry ; Computer science ; Statistical physics ; Computational chemistry ; Molecule ; Physics ; Stereochemistry ; Mathematics ; Quantum mechanics Molecular spectroscopy and chirality ; Axial and Atropisomeric Chirality Synthesis ; Spectroscopy and Quantum Chemical Studies
Authors: Yuan Qian | Wenlong Li | Xiaopei Wang | Deng’an Cai
Source: Polymers (article, 2023-08-04)
Field: Materials Science — Materials Chemistry

Design and Analysis of a New Multi-Part Composite Frangible Cover In this paper, a new multi-part composite frangible cover (MCFC) was designed and fabricated. The frangible cover, manufactured with a traditional manual lay-up method, is designed to conduct a simulated missile launch test using a specially developed test device. A weak zone structure of the composite multi-part frangible cover was designed, and the separation process of the cover was studied by numerical simulation. Based on the strength envelope of the weak zone and the equal-strength design principle, a design method for the weak zone structure of the composite multi-part frangible cover was proposed. A finite element model of the composite multi-part frangible cover was established, and the separation process was numerically simulated and analyzed. Afterward, the verification experiments were carried out. Close agreements between the numerical and experimental results are observed. Cover (algebra) ; Missile ; Composite number ; Finite element method ; Process (computing) ; Envelope (radar) ; Materials science ; Central composite design ; Structural engineering ; Computer science ; Mechanical engineering ; Composite material ; Engineering ; Response surface methodology ; Aerospace engineering High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Mechanical Behavior of Composites