jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James T. Grist | Nikolaj Bøgh | Esben Søvsø Szocska Hansen | Anna M. Schneider | Richard Healicon | Vicky Ball | Jack J. Miller | Sean Smart | Yvonne Couch | Alastair M. Buchan | Damian J. Tyler | Christoffer Laustsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-11-04)
Field: Chemistry — Spectroscopy

Metabolic clearance rate modeling: A translational approach to quantifying cerebral metabolism using hyperpolarized [1-13C]pyruvate Abstract Hyperpolarized carbon-13 MRI is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a model-free formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel model-free perfusion and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T 1 , transmit B 1 , and k PL . A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100g/min, p &lt; 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2 %, p &lt; 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02 s -1 , p = 0.03, respectively) and a decrease in mean transit time (MTT) (31 ± 8 vs 60 ± 2, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging. Cerebral blood flow ; Mean transit time ; In vivo ; Magnetic resonance imaging ; Chemistry ; Perfusion ; Metabolism ; Perfusion scanning ; Medicine ; Internal medicine ; Nuclear medicine ; Endocrinology ; Biology Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Francisco J. Díaz‐Fernández | J. Martí | Carlos García‐Meca
Source: Laser & Photonics Review (article, 2022-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Imaging Cloaked Objects: Diffraction Tomography of Realistic Invisibility Devices Abstract Invisibility cloaks have become one of the most outstanding developments among the wide range of applications in the field of metamaterials. So far, most efforts in invisibility science have been devoted to achieving practically realizable cloak designs and to improving the effectiveness of these devices in reducing their scattering cross‐section (SCS), a scalar quantity accounting for the total electromagnetic energy scattered by an object. However, little attention has been paid to the opposite side of the technology: the development of more efficient techniques for the detection of invisibility devices. For instance, the SCS ignores the phase change introduced by the cloak, as well as the angular dependence of the incident and scattered waves. Here, a different path is proposed, which takes advantage of the smarter way in which diffraction tomography processes all this overlooked information to improve the efficiency in unveiling the presence of invisibility devices. This approach not only results in a considerable sensitivity enhancement in the detection of different kinds of cloaks based on both scattering cancellation and transformation optics, but also enables us to obtain images depicting the approximate shape and size of the cloak. The proposed method can be extended to the detection of sound cloaks. Invisibility ; Cloak ; Metamaterial ; Cloaking ; Optics ; Transformation optics ; Diffraction ; Computer science ; Physics ; Scattering Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Underwater Acoustics Research
Authors: Dariush Eslami | Hamidreza Izadbakhsh | Orod Ahmadi | Marzieh Zarinbal
Source: Scientia Iranica (article, 2022-06-27)
Field: Chemistry — Analytical Chemistry

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations In this paper, the regression-based approach was developed to monitor image data under two-scale analysis. In the first scale, wavelet transformation was used to extract the main features of geometric profile created from the images. The next scale was to monitor the small-scale components which could be expressed by correlation in error terms. To monitor correlation in error terms, one parametric and one non-parametric methods were developed. Parameters of the parametric model including spatial correlation coefficient and error term variance were estimated using Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) estimators, respectively. In non-parametric method, no assumption was made about the structure of correlation in error terms. To extract useful information about the nature of correlation in error terms, Functional Principal Component Analysis (FPCA) was used. After extracting features for both scales, some appropriate test statistics were computed. Then, monitoring the process was performed by plotting these test statistics on corresponding control charts. Simulation and industrial case studies were also performed to evaluate the proposed method’s performance in detecting different shifts. The results indicated the proper performance of the proposed method in monitoring industrial processes to detect out-of-control conditions and identify the source of variability. Parametric statistics ; Estimator ; Statistics ; Principal component analysis ; Transformation (genetics) ; Scale (ratio) ; Mathematics ; Wavelet ; Correlation coefficient ; Correlation ; Computer science ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Rong Xia | Yang Zhao | Benhong Ouyang | Yuli Wang | Chunhua Fang | Yao Xu
Source: Frontiers in Energy Research (article, 2022-02-15)
Field: Materials Science — Materials Chemistry

Study of Capacitive Coupling Sensor Fused With High Voltage XLPE Cable Joint A capacitive coupling sensor for partial discharge detection with the fusion of high voltage XLPE cable joint is designed in this paper. The sensor is to address partial discharge signals leading to transmission attenuation and external interference causing poor field detection sensitivity. First, a coaxial waveguide transmission model was established of high-frequency electrical signals in the body and joint. The result showed that the signal transmission attenuation was minimized while the sensor electrodes were closely attached to the outer semi-conductive layer of the body. Second, the equivalent circuit model was constructed of the capacitive coupling sensor fused with the 110 kV straight passing joint. The specific installation location, main structure size, detection bandwidth, and sensitivity of the sensor in the joint area were determined, which was to maximize the coupling output signal amplitude and transfer function amplitude. Finally, a lightning surge voltage test was carried out with the integration of the fusion of the joint voltage thermal cycling. Simulation and measurement show the following: while the sensor is installed in the cable metal sleeve break and the electrodes are closed to the overall semi-conductive layer, there is excellent performance for partial discharge detection in the frequency range of 1–300 MHz, with a sensitivity of 5 pC. Capacitive sensing ; Partial discharge ; Capacitive coupling ; Materials science ; SIGNAL (programming language) ; Voltage ; Electrical conductor ; Coaxial cable ; Attenuation ; Sensitivity (control systems) ; Coupling loss ; Acoustics ; Coaxial ; Coupling (piping) ; Electrical engineering ; Joint (building) ; Electronic engineering ; Engineering ; Conductor ; Optics ; Physics ; Telecommunications ; Structural engineering ; Computer science ; Composite material High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Thermal Analysis in Power Transmission
Authors: Bhupesh Bishnoi | Marius Buerkle | Hisao Nakamura
Source: Scientific Reports (article, 2022-07-02)
Field: Materials Science — Materials Chemistry

Multi-scale electronics transport properties in non-ideal CVD graphene sheet In this work, we benchmark non-idealities and variations in the two-dimensional graphene sheet. We have simulated more than two hundred graphene-based devices structure. We have simulated distorted graphene sheets and have included random, inhomogeneous, asymmetric out-of-plane surface corrugation and in-plane deformation corrugation in the sheet through autocorrelation function in the non-equilibrium Green's function (NEGF) framework to introduce random distortion in flat graphene. These corrugation effects inevitably appear in the graphene sheet due to background substrate roughness or the passivation encapsulation material morphology in the transfer step. We have examined the variation in density of state, propagating density of transmission modes, electronic band structure, electronic density, and hole density in those device structures. We have observed that the surface corrugation increases the electronic and hole density distribution variation across the device and creates electron-hole charge puddles in the sheet. This redistribution of microscopic charge in the sheet is due to the lattice fields' quantum fluctuation and symmetry breaking. Furthermore, to understand the impact of scattered charge distribution on the sheet, we simulated various impurity effects within the NEGF framework. The study's objective is to numerically simulate and benchmark numerous device design morphology with different background materials compositions to elucidate the electrical property of the sheet device. Graphene ; Ideal (ethics) ; Electronics ; Scale (ratio) ; Nanotechnology ; Materials science ; Computer science ; Physics ; Electrical engineering ; Engineering Graphene research and applications ; Quantum and electron transport phenomena ; Advancements in Semiconductor Devices and Circuit Design
Authors: Sanghamitra Sengupta | Rahul Gera | Colin K. Egan | Uriel N. Morzan | Jan Versluis | Ali Hassanali | Huib J. Bakker
Source: Research Square (preprint, 2022-08-04)
Field: Chemistry — Organic Chemistry

Observation of strong synergy in the interfacial water response of binary ionic and non-ionic surfactant mixtures Abstract We have used heterodyne detected vibrational sum-frequency generation spectroscopy (HDVSFG) to probe the interfacial structure of binary mixtures of sodium dodecyl sulfate (SDS) and hexaethylene glycol monododecyl ether (C 12 E 6 ) surfactants. Our results show that in the presence of C 12 E 6 at CMC (70 µM) the effect of SDS on the orientation of interfacial water molecules is strongly enhanced. Molecular dynamics simulations show that the adsorption free energy of DS − ions to a water surface covered with C 12 E 6 is significantly more attractive by ~ 10 k B T compared to the adsorption energy of DS − to the surface of pure water. The simulations also show that the adsorption of DS − to a water surface covered with C 12 E 6 induces a strong restructuring and enhanced orientation of the near-surface water layers, explaining the HDVSFG results. Pulmonary surfactant ; Ionic bonding ; Binary number ; Chemical engineering ; Chemistry ; Materials science ; Ion ; Mathematics ; Organic chemistry ; Engineering Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: Andrea Amadei | Andrea Ciccioli | Antonello Filippi | Caterina Fraschetti | Massimiliano Aschi
Source: Molecules (article, 2022-11-14)
Field: Chemistry — Organic Chemistry

Theoretical-Computational Modeling of Gas-State Thermodynamics in Flexible Molecular Systems: Ionic Liquids in the Gas Phase as a Case Study A theoretical-computational procedure based on the quasi-Gaussian entropy (QGE) theory and molecular dynamics (MD) simulations is proposed for the calculation of thermodynamic properties for molecular and supra-molecular species in the gas phase. The peculiarity of the methodology reported in this study is its ability to construct an analytical model of all the most relevant thermodynamic properties, even within a wide temperature range, based on a practically automatic sampling of the entire conformational repertoire of highly flexible systems, thereby bypassing the need for an explicit search for all possible conformers/rotamers deemed relevant. In this respect, the reliability of the presented method mainly depends on the quality of the force field used in the MD simulations and on the ability to discriminate in a physically coherent way between semi-classical and quantum degrees of freedom. The method was tested on six model systems (n-butane, n-butane, n-octanol, octadecane, 1-butyl-3-methylimidazolium hexafluorophosphate and 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic pairs), which, being experimentally characterized and already addressed by other theoretical-computational methods, were considered as particularly suitable to allow us to evaluate the method's accuracy and efficiency, bringing out advantages and possible drawbacks. The results demonstrate that such a physically coherent yet relatively simple method can represent a further valid computational tool that is alternative and complementary to other extremely efficient computational methods, as it is particularly suited for addressing the thermodynamics of gaseous systems with a high conformational complexity over a large range of temperature. Molecular dynamics ; Ionic liquid ; Chemistry ; Thermodynamics ; Statistical physics ; Combining rules ; Gaussian ; Conformational isomerism ; COSMO-RS ; Computational chemistry ; Physics ; Molecule ; Binary number ; Mathematics Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Thermodynamic properties of mixtures
Authors: Jiuyang Liang | Zhenli Xu | Qi Zhou
Source: arXiv (Cornell University) (preprint, 2022-05-27)
Field: Chemistry — Physical and Theoretical Chemistry

Random batch sum-of-Gaussians method for molecular dynamics simulations of particle systems We develop an accurate, highly efficient and scalable random batch sum-of-Gaussians (RBSOG) method for molecular dynamics simulations of systems with long-range interactions. The idea of the RBSOG method is based on a sum-of-Gaussians decomposition of the Coulomb kernel, and then a random batch importance sampling on the Fourier space is employed for approximating the summation of the Fourier expansion of the Gaussians with large bandwidths (the long-range components). The importance sampling significantly reduces the computational cost, resulting in a scalable algorithm by avoiding the use of communication-intensive fast Fourier transform. Theoretical analysis is present to demonstrate the unbiasedness of the approximate force, the controllability of variance and the weak convergence of the algorithm. The resulting method has $\mathcal{O}(N)$ complexity with low communication latency. Accurate simulation results on both dynamical and equilibrium properties of benchmark problems are reported to illustrate the attractive performance of the method. Simulations on parallel computing are also performed to show the high parallel efficiency. The RBSOG method can be straightforwardly extended to more general interactions with long ranged kernels, and thus is promising to construct fast algorithms of a series of molecular dynamics methods for various interacting kernels. Computer science ; Scalability ; Fourier transform ; Range (aeronautics) ; Kernel (algebra) ; Algorithm ; Statistical physics ; Mathematics ; Physics Electrostatics and Colloid Interactions ; Gaussian Processes and Bayesian Inference ; Nanopore and Nanochannel Transport Studies
Authors: James M. Goff | C. Sievers | Mitchell Wood | Aidan P. Thompson
Source: arXiv (Cornell University) (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

Permutation-adapted complete and independent basis for atomic cluster expansion descriptors Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. Existing methodologies yield sets of symmetrized functions that are over-complete. These methodologies thus require an additional numerical procedure, such as singular value decomposition (SVD), to eliminate redundant functions. In this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized Wigner symbols. From these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. It is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (PA-RPI) functions forms a complete, independent basis for ACE. Along with the first analytical proofs of block-wise linear dependence of ACE cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. The utility of the method is demonstrated in the development of an ACE interatomic potential for tantalum. Using the new basis functions in combination with Bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models. Basis function ; Mathematics ; Permutation (music) ; Invariant (physics) ; Basis (linear algebra) ; Algorithm ; Linear subspace ; Cluster (spacecraft) ; Applied mathematics ; Computer science ; Pure mathematics ; Mathematical analysis ; Geometry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Geochemistry and Geologic Mapping
Authors: Peder Lyngby | Kristian S. Thygesen
Source: arXiv (Cornell University) (preprint, 2022-06-24)
Field: Materials Science — Materials Chemistry

Data-driven discovery of novel 2D materials by deep generative models Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. Specifically, we train the CDVAE on 2615 2D materials with energy above the convex hull $ΔH_{\mathrm{hull}}&lt; 0.3$ eV/atom, and generate 5003 materials that we relax using density functional theory (DFT). We also generate 14192 new crystals by systematic element substitution of the training structures. We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions. In total we find 11630 predicted new 2D materials, where 8599 of these have $ΔH_{\mathrm{hull}}&lt; 0.3$ eV/atom as the seed structures, while 2004 are within 50 meV of the convex hull and could potentially be synthesized. The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient and reliable crystal generation machine, and significantly expands the space of 2D materials. Crystal structure prediction ; Convex hull ; Density functional theory ; Crystal structure ; Materials science ; Stability (learning theory) ; Autoencoder ; Crystallography ; Computer science ; Computational chemistry ; Chemistry ; Regular polygon ; Geometry ; Mathematics ; Artificial intelligence ; Machine learning ; Deep learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Yinglong Guo | Dongmian Zou | Gilad Lerman
Source: arXiv (Cornell University) (preprint, 2022-06-04)
Field: Materials Science — Materials Chemistry

An Unpooling Layer for Graph Generation We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired new structure and features. Since this unpooling layer is trainable, it can be applied to graph generation either in the decoder of a variational autoencoder or in the generator of a generative adversarial network (GAN). We prove that the unpooled graph remains connected and any connected graph can be sequentially unpooled from a 3-nodes graph. We apply the unpooling layer within the GAN generator. Since the most studied instance of graph generation is molecular generation, we test our ideas in this context. Using the QM9 and ZINC datasets, we demonstrate the improvement obtained by using the unpooling layer instead of an adjacency-matrix-based approach. Graph ; Computer science ; Adjacency matrix ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Bioinformatics and Genomic Networks
Authors: Mani Valleti | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2022-06-24)
Field: Materials Science — Materials Chemistry

Bayesian Optimization in Continuous Spaces via Virtual Process Embeddings Automated chemical synthesis, materials fabrication, and spectroscopic physical measurements often bring forth the challenge of process trajectory optimization, i.e., discovering the time dependence of temperature, electric field, or pressure that gives rise to optimal properties. Due to the high dimensionality of the corresponding vectors, these problems are not directly amenable to Bayesian Optimization (BO). Here we propose an approach based on the combination of the generative statistical models, specifically variational autoencoders, and Bayesian optimization. Here, the set of potential trajectories is formed based on best practices in the field, domain intuition, or human expertise. The variational autoencoder is used to encode the thus generated trajectories as a latent vector, and also allows for the generation of trajectories via sampling from latent space. In this manner, Bayesian Optimization of the process is realized in the latent space of the system, reducing the problem to a low-dimensional one. Here we apply this approach to a ferroelectric lattice model and demonstrate that this approach allows discovering the field trajectories that maximize curl in the system. The analysis of the corresponding polarization and curl distributions allows the relevant physical mechanisms to be decoded. Bayesian optimization ; Autoencoder ; Curse of dimensionality ; Computer science ; Optimization problem ; Artificial intelligence ; Curl (programming language) ; Bayesian probability ; Algorithm ; Mathematical optimization ; Mathematics ; Deep learning Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Neural Networks and Applications