jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Màrio Reis | Pedro Moisés de Sousa
Source:  (article, 2024-11-06)
Field: Chemistry — Analytical Chemistry

Quality Assessment of Coffee Beans Using Convolutional Neural Networks with Wavelet and CLAHE Techniques This paper presents an analytical study comparing different filtering techniques applied to a Convolutional Neural Network (CNN) for coffee bean classification. The results demonstrated that the CLAHE (Contrast Limited Adaptive Histogram Equalization) filter achieved the highest performance, with an accuracy of 0.8875 on the test set. The findings indicate that applying filtering techniques can enhance the performance of the ResNet18 network. CLAHE’s effectiveness is attributed to its ability to improve image details and contrast, leading to superior classification results. This study underscores the potential of advanced filtering methods to boost CNN performance in image classification tasks. Convolutional neural network ; Computer science ; Artificial intelligence ; Wavelet ; Quality (philosophy) ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Yuji Shirakami | Ken-ichi IKEDA | Seiji Miura | Koji Morita | T. Suzuki | Yoshio SAKKA
Source: Institutional Repositories DataBase (IRDB) (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Orientation Dependence of High Temperature Compressive Behavior of Textured Ti<sub>3</sub>SiC<sub>2</sub> In order to clarify the orientation dependent deformation behavior of the MAX phase ceramics, compressive deformation behavior was examined in a textured Ti3SiC2 (TSC) at a high temperature of 1200 °C. Depending on the relationship between the texture and loading directions, both the deformation behavior and microstructure were strongly influenced, and the resultant basal slip, kink formation and delamination affected the compression behavior of the textured TSC. When the stress was loaded parallel or perpendicular to the basal plane (0TSC and 90TSC), the stress-strain (S-S) curves showed higher peak stresses followed by the reduction in the flow stress. When the stress was loaded 45° to the basal plane (45TSC), the S-S curve showed strain hardening after yielding, but did not show peak stress. Although the strength was higher both in 0TSC and 90TSC than in 45TSC, both 0TSC and 90TSC showed the formation of cracks and delamination, resulting to the large drop in the flow stress. In contrast to 0TSC and 90TSC, Orientation (vector space) ; Compression (physics) ; Texture (cosmology) ; Deformation (meteorology) ; Compressive strength MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Pavlo Penziev | Liliia Frolova | Vitalii Lysenkov | Yevhen Lavryk
Source: Technology audit and production reserves (article, 2024-08-27)
Field: Materials Science — General Materials Science

Determination of the influence of the surplus construction on the parameters of the shrinkage shells in the «Body»-type steel castings when casting in single sand molds The object of research is the technology of manufacturing «Body»-type shaped castings of the from medium carbon steel in one-time sand molds. The existing problem is that the design of the casting and foundry equipment significantly affect the formation of internal defects in castings. This especially applies to steel castings, the technology of which is more complicated than the technology of cast iron castings due to much worse casting properties of steel. To determine the influence of the location of surpluses on the «Body»-type steel castings of the on the formation of shrinkage shells, computer modeling was used, in the process of which 5 computer experiments were conducted with different sizes and geometries of surpluses. According to the simulation results, it was found that with some technological options, there is a risk of the shrinkage shell penetrating into the casting body. The use of a cylindrical surplus of a rectangular cross-section with fillets ensures complete absorption of the shrink shell in the place of surplus installation. Using an excess round section at the installation location does not guarantee absorption of the shrink shell. The determining factor affecting the coefficient of increase in the depth of the shrinking shell is the excess volume. This influence can be described by a functional dependence of the logarithmic type with the coefficient of determination R2=0.82. It was determined that the ratio of the diameter of the inlet to its height does not affect the coefficient of increase in the depth of the shrinking shell. The resulting functional dependence allows to set the excess volume that provides a minimum growth factor while simultaneously preventing excess metal consumption. The presented study will be useful for machine-building enterprises that have foundries in their structure, where shaped castings are made in one-time sand molds. Foundry ; Shrinkage ; Casting ; Shell (structure) ; Materials science ; Metallurgy ; Sand casting ; Cross section (physics) ; Mold ; Composite material Material Properties and Applications ; Stonefly species taxonomy and ecology ; Materials Engineering and Processing
Authors: Shuo Sun | Chandan Kumar | Kevin Z. Shen | Elvira Shishenina | Christian B. Mendl
Source: arXiv (Cornell University) (preprint, 2024-02-21)
Field: Materials Science — Materials Chemistry

Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy Solving electronic structure problems is considered one of the most promising applications of quantum computing. However, due to limitations imposed by the coherence time of qubits in the Noisy Intermediate Scale Quantum (NISQ) era or the capabilities of early fault-tolerant quantum devices, it is vital to design algorithms with low-depth circuits. In this work, we develop an enhanced Variational Quantum Eigensolver (VQE) ansatz based on the Qubit Coupled Cluster (QCC) approach, which demands optimization over only $n$ parameters rather than the usual $n+2m$ parameters, where $n$ represents the number of Pauli string time evolution gates $e^{-itP}$, and $m$ is the number of qubits involved. We evaluate the ground state energies of $\mathrm{O_3}$, $\mathrm{Li_4}$, and $\mathrm{Cr_2}$, using CAS(2,2), (4,4) and (6,6) respectively in conjunction with our enhanced QCC ansatz, UCCSD (Unitary Coupled Cluster Single Double) ansatz, and canonical CCSD method as the active space solver, and compare with CASCI results. Finally, we assess our enhanced QCC ansatz on two distinct quantum hardware, IBM Kolkata and Quantinuum H1-1. Ground state ; Electronic circuit ; State (computer science) ; Quantum ; Physics ; Computer science ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: A. Francisco G. Tenreiro | Ricardo J. C. Carbas | Eduardo A. S. Marques | Carlos M. Da Silva | António M. Lopes | Lucas F. M. da Silva
Source: Journal of Machine Design and Automation Intelligence (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

On the design of unconventional testing machines for engineering testing – the case study of advanced joining processes unit Abstract Structural adhesive joining has become a widely used joining method for various types of structures, thus avoiding more conventional joining methods. In this manner, adhesive materials may be present in structures which suffer loading conditions that may not be normally considered in the design phase, such as high-strain rate conditions or creep and torsional loading. In these situations, adhesive properties and mechanical behaviour are sometimes not well understood, thus requiring special machine testing setups that can only be built for the specific application. Therefore, this paper provides a brief overview of proposed machine architectures for a torsion testing apparatus, a tensile &amp; compressive Split Hopkinson Pressure Bar (SHPB), a drop-weight apparatus and a three station creep testing machine with a climactic chamber. Each testing apparatus has been designed by graduate students during their master thesis projects, and facilitated in the production of interesting and relevant scientific output in the field of structural adhesive behaviour. Split-Hopkinson pressure bar ; Adhesive ; Torsion (gastropod) ; Universal testing machine ; Creep ; Computer science ; Structural engineering ; Mechanical engineering ; Unit testing ; Ultimate tensile strength ; Materials science ; Engineering ; Strain rate ; Composite material ; Software High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Structural Response to Dynamic Loads
Authors: Kansei Kanayama | Kazuaki Toyoura
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

First-principles study of phase transition in cadmium titanate by molecular dynamics incorporating nuclear quantum effects First-principles molecular dynamics (FPMD) simulations were applied for the paraelectric-ferroelectric phase transition in the perovskite-type cadmium titanate, CdTiO3. Since the phase transition is reported to occur at the low temperature around 80 K, the quantum thermal bath (QTB) method was utilized in this study, which incorporates the nuclear quantum effects (NQEs). The structural evolutions in the QTB-FPMD simulations are in reasonable agreement with the experimental results, by contrast in the conventional FPMD simulations using the classical thermal bath (CTB-FPMD). According to our phonon calculations, volume expansion is the key in the stabilization of the ferroelectric phase at low temperatures, which was well reproduced in the QTB-FPMD with the NQEs. Thus, the NQEs are of importance in phase transitions at low temperatures, particularly below the room temperature, and the QTB is useful in that it incorporates the NQEs in MD simulations with low computational costs comparable to the conventional CTB. Dynamics (music) ; Phase transition ; Molecular dynamics ; Quantum ; Phase (matter) ; Quantum phase transition ; Phase control ; Condensed matter physics ; Chemical physics ; Physics ; Quantum mechanics Nuclear materials and radiation effects ; Crystal Structures and Properties ; Nuclear Materials and Properties
Authors: Daniel McBride | Ioannis Sgouralis
Source: arXiv (Cornell University) (preprint, 2024-11-05)
Field: Chemistry — Spectroscopy

Hamiltonian Monte Carlo methods for spectroscopy data analysis We present a scalable Bayesian framework for the analysis of confocal fluorescence spectroscopy data, addressing key limitations in traditional fluorescence correlation spectroscopy methods. Our framework captures molecular motion, microscope optics, and photon detection with high fidelity, enabling statistical inference of molecule trajectories from raw photon count data, introducing a superresolution parameter which further enhances trajectory estimation beyond the native time resolution of data acquisition. To handle the high dimensionality of the arising posterior distribution, we develop a family of Hamiltonian Monte Carlo (HMC) algorithms that leverages the unique characteristics inherent to spectroscopy data analysis. Here, due to the highly-coupled correlation structure of the target posterior distribution, HMC requires the numerical solution of a stiff ordinary differential equation containing a two-scale discrete Laplacian. By considering the spectral properties of this operator, we produce a CFL-type integrator stability condition for the standard Störmer-Verlet integrator used in HMC. To circumvent this instability we introduce a semi-implicit (IMEX) method which treats the stiff and non-stiff parts differently, while leveraging the sparse structure of the discrete Laplacian for computational efficiency. Detailed numerical experiments demonstrate that this method improves upon fully explicit approaches, allowing larger HMC step sizes and maintaining second-order accuracy in position and energy. Our framework provides a foundation for extensions to more complex models such as surface constrained molecular motion or motion with multiple diffusion modes. Monte Carlo method ; Statistical physics ; Hybrid Monte Carlo ; Spectroscopy ; Hamiltonian (control theory) ; Dynamic Monte Carlo method ; Physics ; Computational physics ; Markov chain Monte Carlo ; Mathematics ; Statistics ; Mathematical optimization ; Quantum mechanics Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Atomic and Subatomic Physics Research
Authors: Brad Crawford | Co D. Quach | Nicholas C. Craven | Christopher R. Iacovella | Clare McCabe | Peter T. Cummings | Jeffrey J. Potoff
Source: The Journal of Open Source Software (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

MoSDeF-dihedral-fit: A lightweight software for fitting dihedrals within MoSDeF Molecular Mechanics ( Dihedral angle ; Software ; Computer science ; Physics ; Programming language Enzyme Structure and Function ; Advanced Data Processing Techniques ; Machine Learning in Materials Science
Authors: Owen R. Dunton | Tom Arbaugh | Francis W. Starr
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Materials Science — Materials Chemistry

Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning Phase change materials such as Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases, and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Recently, a Gaussian Approximation Potential (GAP) was trained for GST to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost [Zhou et al. Nature Electronics $\mathbf{6}$, 746-754 (2023)]; however, simulations of large length and time scales are still challenging using this GAP model. Here we present a machine-learned (ML) potential for GST implemented using the Atomic Cluster Expansion (ACE) framework. This ACE potential shows comparable accuracy to the GAP potential but performs orders of magnitude faster. We train the ACE potentials both directly from DFT, as well as using a recently introduced indirect learning approach where the potential is trained instead from an intermediate ML potential, in this case, GAP. Indirect learning allows us to consider a significantly larger training set than could be generated using DFT alone. We compare the directly and indirectly learned potentials and find that both reproduce the structure and thermodynamics predicted by the GAP, and also match experimental measures of GST structure. The speed of the ACE model, particularly when using GPU acceleration, allows us to examine repeated transitions between crystal and amorphous phases in device-scale systems with only modest computational resources. Phase (matter) ; Computer science ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Amarjeet
Source: Metallurgical and Materials Engineering (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Topological Tunability Of Low-Dimensional Quantum Materials Under External Fields The tunability of topological phases in low-dimensional quantum materials under external fields has emerged as a transformative strategy for designing next-generation quantum and nanoelectronic devices. This study systematically investigates the influence of electric fields, magnetic fields, mechanical strain, and multi-field coupling on the topological properties of two-dimensional (2D) quantum systems. Using a combination of ab initio density functional theory (DFT), tight-binding models, and experimental probes such as angle-resolved photoemission spectroscopy (ARPES), scanning tunneling microscopy (STM), and SQUID magnetometry, we demonstrate the controllable transition between trivial and topological phases, modulation of bandgaps, and induction of Weyl and Dirac nodes under symmetry-breaking perturbations. Special emphasis is placed on the synergistic effects arising from dual or hybrid external fields. We also highlight how strain and pressure serve as non-destructive tools for phase tuning. Our results not only underscore the fundamental physics behind field-induced topological transitions but also offer a design blueprint for quantum field-effect transistors, spintronic and valleytronic devices, and topologically protected logic units. This tunability opens exciting avenues in reconfigurable quantum technologies where material properties can be dynamically tailored in real time. Materials science ; Topology (electrical circuits) ; Quantum ; Condensed matter physics ; Field (mathematics) ; Quantum mechanics ; Physics ; Pure mathematics ; Electrical engineering Diamond and Carbon-based Materials Research ; Graphene research and applications ; Force Microscopy Techniques and Applications
Authors: Zheng An | Jiahui Wu | Zidong Lin | Xiaobo Yang | Keren Li | Bei Zeng
Source: arXiv (Cornell University) (preprint, 2024-05-22)
Field: Materials Science — Materials Chemistry

Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction Recent advancements in quantum hardware and classical computing simulations have significantly enhanced the accessibility of quantum system data, leading to an increased demand for precise descriptions and predictions of these systems. Accurate prediction of quantum Hamiltonian dynamics and identification of Hamiltonian parameters are crucial for advancements in quantum simulations, error correction, and control protocols. This study introduces a machine learning model with dual capabilities: it can deduce time-dependent Hamiltonian parameters from observed changes in local observables within quantum many-body systems, and it can predict the evolution of these observables based on Hamiltonian parameters. Our model's validity was confirmed through theoretical simulations across various scenarios and further validated by two experiments. Initially, the model was applied to a Nuclear Magnetic Resonance quantum computer, where it accurately predicted the dynamics of local observables. The model was then tested on a superconducting quantum computer with initially unknown Hamiltonian parameters, successfully inferring them. Our approach aims to enhance various quantum computing tasks, including parameter estimation, noise characterization, feedback processes, and quantum control optimization. Hamiltonian (control theory) ; Dual (grammatical number) ; Quantum ; Computer science ; Estimation theory ; Estimation ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Mathematics ; Mathematical optimization ; Quantum mechanics ; Algorithm ; Engineering ; Philosophy Machine Learning in Materials Science
Authors: Lunzhi Shi
Source: Applied and Computational Engineering (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Optimizing molecular design through Multi-Armed Bandits and adaptive discretization: A computational benchmark investigation In the present benchmark study, a novel strategy is unveiled for the optimization of molecular design by integrating Multi-Armed Bandits (MAB) with cutting-edge adaptive discretization techniques. Central to this approach is the employment of the Ultrafast Shape Recognition (USR) method a proven technique for assessing molecular similarity. Moreover, the integration of the Zooming Algorithm is noteworthy. This innovative algorithm demonstrates dynamism, adjusting in real-time to adeptly navigate the vast expanse of chemical space. One of the standout revelations from this investigation is the significant influence of a scaling factor. It serves as the fulcrum for striking an optimal balance between computational agility and peak performance. Such insights profoundly challenge the limitations inherent in conventional discrete MAB methodologies, especially when operating within the bounds of finite computational bandwidth. Beyond merely delineating a blueprint for future interdisciplinary endeavors, this research illuminates the intricacies of molecular design optimization. Additionally, it suggests that a marriage between network and cluster analysis could be the key to enhancing and fine-tuning the reinforcement learning journey. Blueprint ; Computer science ; Benchmark (surveying) ; Discretization ; Artificial intelligence ; Reinforcement learning ; Key (lock) ; Machine learning ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Mr. Karamsetty Shouryadhar | Seelam Namitha | R Lohitha | T. S. Rani
Source: International Journal For Innovative Engineering and Management Research (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

CROP PREDICTION BASED ON CHARACTERISTICS OF THE AGRICULTURAL ENVIRONMENT USING VARIOUS FEATURE SELECTION TECHNIQUES AND CLASSIFIERS Research in agriculture is expanding. Specifically, crop prediction is important in agriculture and mostly depends on soil and environmental factors including temperature, humidity, and rainfall. Previously, farmers could choose the crop to be grown, track its development, and select when it was ready for harvest. However, the agricultural community finds it difficult to continue doing so in the modern day due to the quick changes in environmental circumstances. As a result, machine learning approaches have dominated prediction tasks in recent years. This study has employed many of these techniques to estimate agricultural production. In order to guarantee that a particular machine learning (ML) model operates with a high degree of accuracy, effective feature selection techniques must be used to transform the raw data into a dataset that is machine learning-friendly and readily computed. Only data characteristics that are very relevant in predicting the model's final output should be included, since this will cut down on redundancies and improve the accuracy of the ML model. In order to guarantee that only the most relevant characteristics are included in the model, the concept of optimum feature selection is created. Compiling all features from the raw data and then lumping them together without considering their significance to the modelbuilding process would make our model too complex. Moreover, adding characteristics that don't add much to the ML model will make it more complicated in terms of time and space, which would reduce the output accuracy of the model. The findings show that an ensemble method outperforms the current classification strategy in terms of prediction accuracy. Feature selection ; Artificial intelligence ; Selection (genetic algorithm) ; Agriculture ; Computer science ; Feature (linguistics) ; Machine learning ; Pattern recognition (psychology) ; Crop ; Agricultural engineering ; Engineering ; Agronomy ; Biology ; Ecology Spectroscopy and Chemometric Analyses