jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hua Xu
Source: International Journal of Statistical Distributions and Applications (article, 2023-12-11)
Field: Chemistry — Analytical Chemistry

Synthesis of C4 Olefin by Ethanol Coupling Based on Multivariate Statistical Analysis Ethanol coupling to prepare C4 olefin has excellent environmental benefits, economic value and broad application prospects, and has attracted extensive attention from scholars in many research fields at home and abroad. In this paper, based on the collected experimental data generated in the process of ethanol coupling to prepare C4 olefin, statistical analysis method is used to systematically analyze the data information. Firstly, regression models and exponential models of ethanol conversion, selectivity and temperature of C4 olefin were constructed, and the simulation accuracy of the two models under different catalyst combinations was compared by means of goodness of fit, significance probability and residual variance. The results showed that under 16 catalyst combinations, the ethanol conversion rate increased exponentially with the increase of temperature, and under the other 5 catalyst combinations, the ethanol conversion rate increased linearly with the increase of temperature. The selectivity of C4 olefins increased exponentially with the increase of temperature under 11 catalyst combinations, and linearly under the remaining 10 catalyst combinations. Then, based on the statistical data under temperature determination, the statistical distribution of seven different products over time was analyzed, and a multiple regression model was constructed to quantify the relationship between ethanol conversion, C4 olefin selectivity and other five products, and statistical tests were carried out. Finally, a paired sample test model was constructed to quantitatively analyze the effects of catalyst combination and temperature on ethanol conversion and selectivity of C4 olefin. Olefin fiber ; Multivariate statistics ; Ethanol ; Multivariate analysis ; Coupling (piping) ; Statistical analysis ; Chemistry ; Statistics ; Econometrics ; Environmental science ; Chromatography ; Mathematics ; Materials science ; Organic chemistry ; Catalysis Spectroscopy and Chemometric Analyses
Authors: Ziqiao Meng | Peilin Zhao | Yang Yu | Irwin King
Source: arXiv (Cornell University) (preprint, 2023-06-05)
Field: Materials Science — Materials Chemistry

Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of electrons, resulting in state-of-the-art top-1 accuracy, and enabling parallel sampling. However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule. This violation of the physical constraints of chemical reactions impairs model performance. In this work, we propose a new framework called that combines two doubly stochastic self-attention mappings to obtain electron redistribution predictions that follow both constraints. We further extend our solution to a general multi-head attention mechanism with augmented constraints. To achieve this, we apply Sinkhorn's algorithm to iteratively update self-attention mappings, which imposes doubly conservative constraints as additional informative priors on electron redistribution modeling. We theoretically demonstrate that our can simultaneously satisfy both rules, which the current decoder mechanism cannot do. Empirical results show that our approach consistently improves the predictive performance of non-autoregressive models and does not bring an unbearable additional computational cost. Autoregressive model ; Computer science ; Redistribution (election) ; Prior probability ; Graph ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Econometrics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alexandra Volokhova | Michał Koziarski | Álex Hernández-García | Chenghao Liu | Santiago Miret | Pablo Lemos | Luca Thiede | Zichao Yan | Alán Aspuru‐Guzik | Yoshua Bengio
Source: arXiv (Cornell University) (preprint, 2023-10-20)
Field: Materials Science — Materials Chemistry

Towards equilibrium molecular conformation generation with GFlowNets Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution. Molecule ; Sampling (signal processing) ; Molecular dynamics ; Fidelity ; Energy (signal processing) ; Boltzmann constant ; Distribution (mathematics) ; Statistical physics ; Boltzmann distribution ; Set (abstract data type) ; Umbrella sampling ; Energy distribution ; Computational chemistry ; Chemistry ; Biological system ; Chemical physics ; Physics ; Computer science ; Mathematics ; Thermodynamics ; Biology ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Michaela Mühlberg | Hugh Montgomery | Jonathon Watson | Daniella Ferluccio | Edward M. Gardner | Matthew J. Blow | C. P. Alves e Dias | Hemna Fathima | Juan Marcos González | Eleanor Griffiths | Rob Hinde | Ash Hyde | S. M. Howell | Francesca Jacklin | Shruti Karnik | Sophie Koh | Tamara Kosikova | Evie Karkera | Brian Li | Sam Mansell | Carole Martin | Kirsty Mcroberts | Cat Schofield | Charu Storr-Vijay | Manman Wang | Tom Williams | Ella White | Elizabeth So | Lee Colwill | Jie Gao | Yu Zhang | Sam Keltie | Chunli Bai | Cinzia Casiraghi | Gianaurelio Cuniberti | Qing Dai | Christian Klinke | Quan Li | Ling Xing | Xiaogang Liu | Janet E. Macdonald | Teresa Pellegrino | Elena V. Shevchenko | Jonathan G. C. Veinot | Umesh V. Waghmare | Jin Zou | Sandra Ruiz-Gmez | Lucas Prez | Arantzazu Mascaraque | Benito Santos | Farid Gabaly | Andreas | K ; Tao Deng | Jiaxin Li | Rong Guo | Yashi Wang | Ting Li | Shuya Zang | Ling Zhang | Man Li | * | Qin He | ; Partha | Sarathi Padhi | * Ajimsha | S Rai | U Goutam | Aniruddha Bose | Marcello Deluca | Wolfgang Pfeifer | Benjamin Randoing | Chao-Min | Michael Huang | Carlos Poirier
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Nanoscale, 2023, 15, 8033 DOI: 10.1039/D3NR90083E This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Computer science ; Chromatin structure remodeling (RSC) complex ; Attribution ; World Wide Web ; Library science ; Computer security ; Chemistry ; Psychology Quantum Dots Synthesis And Properties
Authors: Timo Pierre Schrader | Teresa Bürkle | Sophie Henning | Sherry Tan | Matteo Finco | Stefan Grünewald | Maira Indrikova | Felix Hildebrand | Annemarie Friedrich
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

MuLMS-AZ: An Argumentative Zoning Dataset for the Materials Science Domain Timo Pierre Schrader, Teresa Bürkle, Sophie Henning, Sherry Tan, Matteo Finco, Stefan Grünewald, Maira Indrikova, Felix Hildebrand, Annemarie Friedrich. Proceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023). 2023. Argumentative ; Domain (mathematical analysis) ; Art history ; Zoning ; Philosophy ; Computer science ; Epistemology ; Art ; Mathematics ; Political science ; Law Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Zhi‐Hui Zhang | Liang Zhang | Xuan Zhang | Xiaoxu Huang
Source: arXiv (Cornell University) (preprint, 2023-05-26)
Field: Materials Science — Materials Chemistry

First-principles study of solute atoms segregation in Al Σ5(210) metastable grain boundaries Grain boundary (GB) segregation of solute atoms plays an important role in the microstructure and macroscopic mechanical properties of materials. The study of GB segregation of solute atoms using computational simulation has become one of the hot spots in recent years. However, most studies mainly focus on ground-state GB structures with the lowest energy, and the impact of GB metastability with higher energy on solute segregation remains poorly understood. In this work, the first-principles method based on the density functional theory was adopted to investigate the effect of solute atoms Mg and Cu segregation on ground-state Σ5(210) GB (GB-I) and metastable GBs(GB-II, GB-III) in Al. GB energy, segregation energy, and theoretical tensile strength of Mg and Cu segregation at three GBs were calculated. The results show that both Mg and Cu have a large driving force to segregate to Al GBs, which reduces the GB energy and improves improve GB stability. The segregation of Mg and Cu on GB-III induces the transformation of the GB structural unit and the GB structural phase transformations. For the above three GBs, Cu segregation increases the theoretical tensile strength of GBs to varying degrees. The segregation of Mg would reduce the resistance of GB-I and GB-II, but enhances the strength of GB-III. The effect of solute atoms segregation on the mechanical properties of GBs was investigated by charge density distribution and density of states. Metastability ; Grain boundary ; Materials science ; Ultimate tensile strength ; Density functional theory ; Molecular dynamics ; Phase (matter) ; Work (physics) ; Microstructure ; Ground state ; Crystallography ; Chemical physics ; Thermodynamics ; Chemistry ; Atomic physics ; Metallurgy ; Computational chemistry ; Physics Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: You Yong | Yanping Zhang | Zhenhua Wang | Jian‐Qiang Zhao | Jun‐Qing Yin | Wei‐Cheng Yuan | Richard Kelly | Harriet Riley | Helen Saxton | Danny Andrews | Ershad Abubacker | Becky Webb | Kirstine Anderson | Matthew J. Bown | Laura Cooper | Hannah Fielding | Clare Fitzgerald | Anoushka Handa | Claire Harding | Alan Holder | Charlie Palmer | Rosie Rothwell | Donna Smith | Laura Smith | Jade Holliday | Natalie Ford | Jeanne Andres | Beifang V ; Zhang | Zhibang Nian | Jianhua Wu | Zhuo Guo | Caifeng Chen | Xuankun Yuan | Yiwen Huang | Hongbin Shen | * Zhang | E Tang | Yu Zhang | Gong-Xiang Qi | Yong-Liang Yu | Meng | Chen Li | Jin Wang | Yan Wang | Yi Feng | Jinbo Li | * | Yan Zhang
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Chem. Commun., 2023, 59, 7475 DOI: 10.1039/D3CC90204H This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Computer science ; Attribution ; World Wide Web ; Library science ; Chemistry ; Computer security ; Psychology ; Social psychology Radioactive element chemistry and processing
Authors: Haotian Wang | Fei Xu | Lu Cai | Daniele Salvato | Fidelma Giulia Di Lemma | Luca Capriotti | Tiankai Yao | Min Xian
Source: Research Square (preprint, 2023-05-17)
Field: Materials Science — Materials Chemistry

A Fine Pore-preserved Deep Neural Network for Porosity Analytics of a High Burnup U-10Zr Metallic Fuel Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a data processing workflow to produce multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely connected units. Two SEM 250× field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design. Convolutional neural network ; Porosity ; Magnification ; Computer science ; Deep learning ; Cladding (metalworking) ; Scanning electron microscope ; Artificial intelligence ; Materials science ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Xiang Li | Yubing Qian | Ji Chen
Source: arXiv (Cornell University) (preprint, 2023-07-05)
Field: Materials Science — Materials Chemistry

Electric Polarization from Many-Body Neural Network Ansatz Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wavefunction ansatz combined with quantum Monte Carlo to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and re-established its thickness dependence. Overall, this approach provides a powerful tool to consider electron correlation in the modern theory of polarization. Ansatz ; Polarization (electrochemistry) ; Dielectric ; Quantum Monte Carlo ; Physics ; Wave function ; Density functional theory ; Artificial neural network ; Plane wave ; Statistical physics ; Ab initio ; Monte Carlo method ; Coulomb ; Electron ; Quantum mechanics ; Mathematics ; Computer science ; Chemistry ; Machine learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Nicolas Folastre | Junhao Cao | Gozde Oney | Sunkyu Park | Arash Jamali | Christian Masquelier | Laurence Croguennec | M. Véron | E.F. Rauch | Arnaud Demortière
Source: arXiv (Cornell University) (preprint, 2023-05-03)
Field: Materials Science — Surfaces, Coatings and Films

Improved ACOM pattern matching in 4D STEM through adaptive sub pixel peak detection and image reconstruction The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixelar adaptative image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters such as mean and gaussian blur, we are able to improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters. Computer science ; Diffraction ; Orientation (vector space) ; Gaussian ; Materials science ; Artificial intelligence ; Optics ; Mathematics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Geophysical and Geoelectrical Methods
Authors: Julien Sayet
Source: theses.fr (ABES) (preprint, 2023-03-03)
Field: Materials Science — Materials Chemistry

Effet de la nature et de la connectivité des joints de grains sur la diffusion de l'hydrogène dans les matériaux hétérogènes : étude numérique Anticipating hydrogen embrittlement requires knowledge of the effect of different time and spatial scales within diffusion and trapping processes in the material's microstructure. In this work, we examine the impact of various types of triple junctions (TJ) and grain boundaries (GB) and their connectivity on the diffusion of hydrogen. First, we propose four algorithms to control the distribution of TJ fractions depending on a given fraction of Random grain boundaries. Then, using finite element modeling, we investigate the effects of triple junction distribution on hydrogen diffusivity. We discover remarkable relationships between grain boundary connectivity and the effective diffusion coefficient by quantifying grain boundary networks in 2D idealized microstructures. In addition, greater diffusivity is reflected in nanocrystalline materials. Our results prove to reproduce GB connectivity paths by compelling the distribution. Furthermore, 2D reconstructed EBSD microstructures were simulated and compared to experimental data. Following that, 3D simulations of idealized microstructures were performed, and the effects of triple junction connectivity were investigated. We show that there is strong correlation between the connectivity of the triple junction and the effective diffusion coefficient, particularly in nanocrystalline materials. 3D simulations approach the experimental results more accurately as the impact of the connectivity of TJs is included. Diffusion ; Grain boundary ; Grain boundary diffusion coefficient ; Hydrogen ; Materials science ; Boundary (topology) ; Statistical physics ; Chemical physics ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Composite material ; Mathematical analysis ; Microstructure Material Properties and Failure Mechanisms ; Superconducting Materials and Applications ; Microstructure and mechanical properties
Authors: Vijayashri Ashok Kandagal | Blaise Lobo
Source: Academia Materials Science (article, 2023-10-30)
Field: Materials Science — General Materials Science

UV induced changes in bismuth oxychloride–filled polycarbonate composite films Bismuth oxychloride (BiOCl)–filled polycarbonate (PC) composite films have been prepared at five different filler levels (FLs), that is, at 0, 5, 15, 25, and 35 wt.% (of BiOCl in PC), by using the solution casting method. These composites were irradiated with UV-C radiation at a wavelength of 254 nm for four hours and were studied before and after UV-C irradiation. The prepared composites showed three fluorescence emission peaks for pristine PC, at wavelengths of 415, 440, and 467 nm, and the emission peak intensity increases with an increase in FLs and exposure time to UV irradiation. There is a structural transformation of the sample, from the amorphous nature of PC to the crystalline nature of BiOCl-filled PC composite samples, which has been confirmed by the XRD technique; the degree of crystallinity has increased with an increase in FL. SEM images confirm that surface morphological changes take place due to an increase in BiOCl content in the PC matrix with an increase in FL. The uniform distribution of BiOCl in PC has been confirmed by observing the AFM images. A single-step decomposition phenomenon is observed from the TGA curves, and pristine PC has greater mass loss and thermal stability among all the filled and irradiated BiOCl-filled PC composite films. Polycarbonate ; Bismuth ; Composite number ; Irradiation ; Materials science ; Composite material ; Chemical engineering ; Metallurgy ; Physics Material Properties and Applications ; Pigment Synthesis and Properties ; Polymer Science and PVC