jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mumeng Wei | Jinghua Yao | Yufan Chen | Bojun Yang | Dichun Chen | Yikun Cai
Source: Materials (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Optimization and Evaluation of Accelerated Corrosion Tests Based on Mechanism Equivalence Principles Conventional indoor corrosion test design methods primarily focus on the rapid evaluation of material corrosion resistance, often neglecting the impact of environmental stress levels on the equivalence of corrosion mechanisms. This study introduces a novel indoor corrosion test design method based on the principle of corrosion mechanism equivalence, aimed at improving the accuracy of indoor accelerated corrosion simulations. We define the characteristic of corrosion mechanism equivalence as the Corrosion Mechanism Equivalence Degree (CMed), which quantifies the similarity between corrosion mechanisms in indoor accelerated tests and field tests. Then, modified conventional link function models are defined, integrating the probability distribution of environmental factors to estimate corrosion model parameters more precisely. Finally, an optimization problem is constructed for accelerated corrosion tests based on CMed, incorporating constraints on environmental stress levels and acceleration factors. A case study demonstrates the proposed method's ability to accurately simulate the actual service environment of materials, determining the appropriate stress levels for indoor accelerated corrosion tests while ensuring the desired acceleration factor and corrosion mechanism equivalence. Corrosion ; Equivalence (formal languages) ; Materials science ; Mechanism (biology) ; Structural engineering ; Computer science ; Metallurgy ; Engineering ; Mathematics ; Physics Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Han-Qing Shi | Hai-Qing Zhang
Source: Communications in Theoretical Physics (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Learning topological defects formation with neural networks in a quantum phase transition Abstract Neural networks possess formidable representational power, rendering them invaluable in solving complex quantum many-body systems. While they excel at analyzing static solutions, nonequilibrium processes, including critical dynamics during a quantum phase transition, pose a greater challenge for neural networks. To address this, we utilize neural networks and machine learning algorithms to investigate time evolutions, universal statistics, and correlations of topological defects in a one-dimensional transverse-field quantum Ising model. Specifically, our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength. The excitation energies satisfy a power-law relation to the quench rate, indicating a proportional relationship between the excitation energy and the kink numbers. Moreover, we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate, indicating a binomial distribution of the kinks. Finally, the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula. Quantum phase transition ; Phase transition ; Transition (genetics) ; Topology (electrical circuits) ; Quantum ; Quantum phases ; Artificial neural network ; Physics ; Computer science ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics ; Chemistry ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Neural Networks and Applications ; Force Microscopy Techniques and Applications
Authors: I. V. Kashin | S. N. Andreev
Source: arXiv (Cornell University) (preprint, 2024-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetocrystalline anisotropy in metallic systems: fast and stable estimation in Green`s functions formalism In this work we suggest a theoretical approach, that allows to study the effects of magnetocrystalline anisotropy (MCA) in metallic systems using the Green`s functions formalism. We demonstrate that employment of the reciprocal space resolution instead of its reduction in the inter-site variant essentially improves the numerical stability of MCA energy by means of Monkhorst-Pack grid density and spatial convergence. The latter problem is able to be completely removed due to rigorous analytical replacement of pairwise atomic summation by simple composition of sublattices contributions, calculated as a whole. The approach is validated on the effective model of single atom, which nevertheless inherits the qualitative MCA picture of Co monolayer and Au/Co/Au sandwiched material. The numerical convergence is confirmed using the model of atomic chain in the strong metallic regime. For cobalt monoxide, described by ab initio calculations using GGA+U, the MCA energy angular profile reveals the prevailing role of ferromagnetically aligned Co sublattices in forming of the easy axis. Formalism (music) ; Magnetocrystalline anisotropy ; Anisotropy ; Green S ; Metal ; Materials science ; Mathematics ; Physics ; Mathematical analysis ; Quantum mechanics ; Metallurgy ; Art ; Magnetic anisotropy ; Literature Magnetic Properties and Applications ; Theoretical and Computational Physics
Authors: Petr Ivashkov | Po‐Wei Huang | Kelvin Koor | Lirandë Pira | Patrick Rebentrost
Source: arXiv (Cornell University) (preprint, 2024-10-06)
Field: Chemistry — Physical and Theoretical Chemistry

QKAN: Quantum Kolmogorov-Arnold Networks The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architecture, called Kolmogorov-Arnold Networks (KAN), has emerged, inspired by the compositional structure of the Kolmogorov-Arnold representation theorem. In this work, we design a quantum version of KAN called QKAN. Our QKAN exploits powerful quantum linear algebra tools, including quantum singular value transformation, to apply parameterized activation functions on the edges of the network. QKAN is based on block-encodings, making it inherently suitable for direct quantum input. Furthermore, we analyze its asymptotic complexity, building recursively from a single layer to an end-to-end neural architecture. The gate complexity of QKAN scales linearly with the cost of constructing block-encodings for input and weights, suggesting broad applicability in tasks with high-dimensional input. QKAN serves as a trainable quantum machine learning model by combining parameterized quantum circuits with established quantum subroutines. Lastly, we propose a multivariate state preparation strategy based on the construction of the QKAN architecture. Quantum ; Computer science ; Statistical physics ; Physics ; Quantum mechanics History and advancements in chemistry ; Quantum Mechanics and Applications ; Cognitive Computing and Networks
Authors: Milica Karadžić Banjac | Strаhinjа Kоvаčеvić | Sanja Podunavac-Kuzmаnоvić | Lidija R. Jevrić
Source: Acta periodica technologica (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Chemometric modeling of bioconcentration factor of 6-chloro-1,3,5-triazine derivatives based on MLR-QSPR approach Pesticides have an important role in agriculture since they can prevent large crop losses which is crucial in order to keep pace with population growth that is estimated to be 9.7 billion people by the year 2050. The use of pesticides is not always in accordance with good agricultural practice so the human and environmental exposure to pesticides are a continuing concern. Some triazine derivatives are well-known commercially available pesticides with proven activity. The researchers have task to design and develop new pesticides with diminished negative influence on environment and humans together with a good crop protection ability. In order to achieve this, many computational and artificial intelligence tools have been used along with extensive experimental work. A group of twenty-one 6-chloro-1,3,5-triazine derivatives was investigated in the domain of bioconcentration factor, as an indicator of the bioaccumulation potential of a substance, and multiple linear regression (MLR)- quantitative structure-property relationship (QSPR) modeling was conducted. The proposed MLR-QSPR model was extensively statistically validated in order to provide reliable model for further re-search work. All conducted procedures, internal and external validation as well as normality test of residuals indicated good fitness, absence of systematic error in model development and high predictive ability of the proposed model. Quantitative structure–activity relationship ; Bioconcentration ; Triazine ; Chemistry ; Mathematics ; Environmental chemistry ; Organic chemistry ; Stereochemistry Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods ; bioluminescence and chemiluminescence research
Authors: Julius Hillenbrand | Jan Obořil | Christian P. Haas | Maximilian Lübbesmeyer | Rachel Nicholls | Thorsten Gressling | Klavs F. Jensen | Giulio Volpin
Source: ChemRxiv (preprint, 2024-07-02)
Field: Chemistry — Spectroscopy

Automated Processing of Chromatograms: A comprehensive Python Package with GUI for Intelligent Peak Identification and Deconvolution in Chemical Reaction Analysis Reaction screening and high-throughput experimentation (HTE) coupled with liquid chromatography (HPLC, UHPLC) are becoming more important than ever in synthetic chemistry. With growing number of experiments, it is increasingly difficult to ensure correct peak identification and integration, especially due to unknown side components which often overlap with the peaks of interest. We developed a comprehensive Python package with web-based graphical user interface (GUI) for automated processing of chromatograms, including baseline correction, intelligent peak picking, peak purity checks, deconvolution of overlapping peaks, and compound tracking. The algorithm accuracy was benchmarked using three datasets and compared to the previous MOCCA implementation and published results. The processing is fully automated with the possibility to include calibration and internal standards. The software supports chromatograms with photo-diode array detector (DAD) data from most commercial HPLC systems, and the Python package and GUI implementation are open-source to allow addition of new features and further development. Python (programming language) ; Deconvolution ; Computer science ; Identification (biology) ; Programming language ; Algorithm Analytical Chemistry and Chromatography
Authors: Olzhas Ismagambetov | N. U. Aldiyarov | Nurlan Almassov | I. S. Irgibaeva | Zhadyra Y. Baitassova | Sergei Piskunov | Anuar Aldongarov | Omirzak Abdirashev
Source: Molecules (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Atomistic Modeling of Natural Gas Desulfurization Process Using Task-Specific Deep Eutectic Solvents Supported by Graphene Oxide This study employs Density Functional Theory (DFT) calculations and traditional all-atom Molecular Dynamics (MD) simulations to reveal atomistic insights into a task-specific Deep Eutectic Solvent (DES) supported by graphene oxide with the aim of mimicking its application in the natural gas desulfurization process. The DES, composed of N,N,N',N'-tetramthyl-1,6-hexane diamine acetate (TMHDAAc) and methyldiethanolamine (MDEA) supported by graphene oxide, demonstrates improved efficiency in removing hydrogen sulfide from methane. Optimized structure and HOMO-LUMO orbital analyses reveal the distinct spatial arrangements and interactions between hydrogen sulfide, methane, and DES components, highlighting the efficacy of the DES in facilitating the separation of hydrogen sulfide from methane through DFT calculations. The radial distribution function (RDF) and interaction energies, as determined by traditional all-atom MD simulations, provide insights into the specificity and strength of the interactions between the DES components supported by graphene oxide and hydrogen sulfide. Importantly, the stability of the DES structure supported by graphene oxide is maintained after mixing with the fuel, ensuring its robustness and suitability for prolonged desulfurization processes, as evidenced by traditional all-atom MD simulation results. These findings offer crucial insights into the molecular-level mechanisms underlying the desulfurization of natural gas, guiding the design and optimization of task-specific DESs supported by graphene oxide for sustainable and efficient natural gas purification. Graphene ; Flue-gas desulfurization ; Oxide ; Eutectic system ; Task (project management) ; Process (computing) ; Materials science ; Natural gas ; Chemical engineering ; Natural (archaeology) ; Chemistry ; Nanotechnology ; Computer science ; Organic chemistry ; Engineering ; Metallurgy ; Systems engineering Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysts for Methane Reforming
Authors: S. Emad Rezaei | Peter Schindler
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Revealing large room-temperature Nernst coefficients in 2D materials by first-principles modeling Two-dimensional (2D) materials have attracted significant attention owing to their distinctive electronic, thermal, and mechanical characteristics. Recent advancements in both theoretical understanding and experimental methods have greatly contributed to the understanding of thermoelectric properties in 2D materials. However, thermomagnetic properties of 2D materials have not yet received the same amount of attention. In this work, we select promising 2D materials guided by the physics of the Nernst effect and present a thorough first-principles study of their electronic structures, carrier mobilities, and Nernst coefficients as a function of carrier concentration. Specifically, we reveal that trilayer graphene with an ABA stacking exhibits an exceptionally large Nernst coefficient of 112 μV (KT)<sup>-1</sup> at room temperature. We further demonstrate that monolayer graphene, ABC-stacked trilayer graphene, and trilayer phosphorene (AAA stacking) have large Nernst coefficients at room temperature. This study establishes an <i>ab initio</i> framework for the quantitative study of the thermomagnetic effects in 2D materials and demonstrates high fidelity with previous experimental data. Nernst equation ; Nernst effect ; Materials science ; Condensed matter physics ; Graphene ; Thermodynamics ; Nanotechnology ; Physics ; Physical chemistry ; Chemistry ; Electrode Graphene research and applications ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Daniel del-Pozo-Bueno | Demie Kepaptsoglou | Quentin M. Ramasse | F. Peiró | Sònia Estradé
Source: Microscopy and Microanalysis (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100. Throughout this study, we explore the optimal configuration for GANs to produce realistic spectra. Notably, our DAG generates realistic spectra, and the spectra produced by the generator are successfully used in real-world applications to train classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) that have been successful in classifying experimental EEL spectra. Computer science ; Artificial neural network ; Generative grammar ; Generator (circuit theory) ; Support vector machine ; Artificial intelligence ; Machine learning ; Adversarial system ; Energy (signal processing) ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning and Data Classification
Authors: Derek Xu | Olcay Cirit | Reza Asadı | Yizhou Sun | Wei Wang
Source: arXiv (Cornell University) (preprint, 2024-05-25)
Field: Materials Science — Materials Chemistry

Mixture of In-Context Prompters for Tabular PFNs Recent benchmarks found In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular learning cannot run without severely compromising performance, due to its quadratic space and time complexity w.r.t. dataset size. We propose MIXTUREPFN, which both extends nearest-neighbor sampling to the state-of-the-art ICL for tabular learning model and uses bootstrapping to finetune said model on the inference-time dataset. MIXTUREPFN is the Condorcet winner across 36 diverse tabular datasets against 19 strong deep learning and tree-based baselines, achieving the highest mean rank among Top-10 aforementioned algorithms with statistical significance. Context (archaeology) ; Computer science ; Business ; History ; Archaeology High voltage insulation and dielectric phenomena
Authors: Wanjiku Amani Njoki
Source: Idosr Journal of Computer and Applied Sciences. (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Transformative Potential of Quantum Simulation in Chemistry and Materials Science: Advances, Applications, and Future Directions Quantum simulation, utilizing quantum computers to model complex quantum systems, offers transformative capabilities in chemistry and materials science. This review explores both digital and analog quantum simulation methodologies, highlighting their advances, applications, and future directions. Digital methods such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) excel in calculating molecular properties and optimizing material designs, leveraging quantum bits to surpass classical computing limitations. Analog techniques simulate quantum systems through physical analogies, offering insights into emergent quantum behaviors in systems like cold atoms and trapped ions. In East Africa, quantum simulation research is burgeoning, with institutions like the African Institute for Mathematical Sciences (AIMS) and the University of Nairobi leading initiatives. Challenges such as limited quantum hardware access and skill gaps necessitate robust infrastructure and educational programs. Future directions envision regional centers of excellence, collaborative ecosystems, and innovative applications addressing local challenges. Quantum simulation stands poised to revolutionize scientific inquiry, technological innovation, and regional development, positioning East Africa at the forefront of quantum research's transformative impact on society. Keywords: Transformative Potential, Quantum Simulation, Chemistry, Materials Science. Transformative learning ; Nanotechnology ; Quantum chemistry ; Engineering physics ; Chemistry ; Materials science ; Engineering ; Sociology ; Supramolecular chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science
Authors: Jordan A. Welsman | Gunther H. Weber | Oluwamayowa Amusat | Anna Giannakou | Lavanya Ramakrishnan
Source:  (article, 2024-11-17)
Field: Materials Science — Surfaces, Coatings and Films

Enhancing Electron Microscopy Image Classification Using Data Augmentation Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets. Computer science ; Artificial intelligence ; Contextual image classification ; Microscopy ; Electron microscope ; Image (mathematics) ; Pattern recognition (psychology) ; Materials science ; Computer vision ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advanced Neural Network Applications
Authors: Jan Obořil | Christian P. Haas | Maximilian Lübbesmeyer | Rachel Nicholls | Thorsten Gressling | Klavs F. Jensen | Giulio Volpin | Julius Hillenbrand
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Automated processing of chromatograms: a comprehensive python package with a GUI for intelligent peak identification and deconvolution in chemical reaction analysis Reaction screening and high-throughput experimentation (HTE) coupled with liquid chromatography (HPLC and UHPLC) are becoming more important than ever in synthetic chemistry. Python (programming language) ; Deconvolution ; Computer science ; Identification (biology) ; Computer graphics (images) ; Programming language ; Algorithm Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies