jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhe Zhang | Xi Yang | Xiaobiao Huang | Junjie Li | Timur Shaftan | Victor Smaluk | Minghao Song | Weishi Wan | Lijun Wu | Yimei Zhu
Source: Scientific Reports (article, 2021-07-06)
Field: Materials Science — Surfaces, Coatings and Films

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications. Mega- ; Electron ; Volt ; Computer science ; Cathode ray ; Beam (structure) ; Physics ; Electrical engineering ; Optics ; Nuclear physics ; Engineering Electron and X-Ray Spectroscopy Techniques
Authors: Chengyun Zhang | Xiang Cai | Haoran Qiao | Yun Zhang | Yejian Wu | Xinqiao Wang | Xie Hai-ying | Feng Luo | Hongliang Duan
Source:  (preprint, 2021-07-15)
Field: Materials Science — Materials Chemistry

Self-supervised molecular pretraining strategy for reaction prediction in low-resource scenarios In the face of low-resource reaction training samples, we construct a chemical platform for addressing small-scale reaction prediction problem. By using a self-supervised molecular pretraining strategy, the chemical information from 1 billion molecules can be delivered to small-scale reaction prediction. To demonstrate the broad applicability of our approach, we apply our model to three different name reaction prediction tasks. In the Baeyer-Villiger, Heck and Sharpless asymmetric epoxidation reactions, the accuracies increase by 5.7%, 10.8%, 4.8% on average, respectively. Construct (python library) ; Computer science ; Scale (ratio) ; Resource (disambiguation) ; Reaction conditions ; Chemistry ; Artificial intelligence ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Catalysis ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ryosuke Oyanagi | Tatsu Kuwatani | Toshiaki Omori
Source: The European Physical Journal B (article, 2021-02-01)
Field: Chemistry — Analytical Chemistry

Exploration of nonlinear parallel heterogeneous reaction pathways through Bayesian variable selection Abstract Inversion is a key method for extracting nonlinear dynamics governed by heterogeneous reaction that occur in parallel in the natural sciences. Therefore, in this study, we propose a Bayesian statistical framework to determine the active reaction pathways using only the noisy observable spatial distribution of the solid phase. In this method, active reaction pathways were explored using a Widely Applicable Bayesian Information Criterion (WBIC), which is used to select models within the framework of Bayesian inference. Plausible reaction mechanisms were determined by maximizing the posterior distribution. This conditional probability is obtained through Markov chain Monte Carlo simulations. The efficiency of the proposed method is then determined using simulated spatial data of the solid phase. The results show that active reaction pathways can be identified from the redundant candidates of reaction pathways. After these redundant reaction pathways were excluded, the controlling factor of the reaction dynamics was estimated with high accuracy. Graphic Abstract Bayesian probability ; Computer science ; Markov chain Monte Carlo ; Bayesian inference ; Posterior probability ; Monte Carlo method ; Inference ; Variable elimination ; Nonlinear system ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Advanced Statistical Methods and Models
Authors: Yueh-Yu Lin | Felix Schleifer | Markus Holzinger | Na Ta | Birgit Skrotzki | Reza Darvishi Kamachali | Uwe Glatzel | Michael Fleck
Source: Materials (article, 2021-03-12)
Field: Materials Science — Materials Chemistry

Quantitative Shape-Classification of Misfitting Precipitates during Cubic to Tetragonal Transformations: Phase-Field Simulations and Experiments The effectiveness of the mechanism of precipitation strengthening in metallic alloys depends on the shapes of the precipitates. Two different material systems are considered: tetragonal γ'' precipitates in Ni-based alloys and tetragonal θ' precipitates in Al-Cu-alloys. The shape formation and evolution of the tetragonally misfitting precipitates was investigated by means of experiments and phase-field simulations. We employed the method of invariant moments for the consistent shape quantification of precipitates obtained from the simulation as well as those obtained from the experiment. Two well-defined shape-quantities are proposed: (i) a generalized measure for the particles aspect ratio and (ii) the normalized λ<sub>2</sub>, as a measure for shape deviations from an ideal ellipse of the given aspect ratio. Considering the size dependence of the aspect ratio of γ'' precipitates, we find good agreement between the simulation results and the experiment. Further, the precipitates' in-plane shape is defined as the central 2D cut through the 3D particle in a plane normal to the tetragonal c-axes of the precipitate. The experimentally observed in-plane shapes of γ''-precipitates can be quantitatively reproduced by the phase-field model. Tetragonal crystal system ; Materials science ; Phase (matter) ; Cubic crystal system ; Crystallography ; Condensed matter physics ; Chemistry ; Crystal structure ; Physics Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; nanoparticles nucleation surface interactions
Authors: Penggui Huang | Fan Zhao | Zheng Zhu | Yanfeng Zhang | Xiaoping Li | Wu ZhangKang
Source: BioResources (article, 2021-02-18)
Field: Chemistry — Organic Chemistry

Application of variant transfer learning in wood recognition Wood is a material commonly found in nature and is widely used in all professions and industries. Because wood has varied growth cycles and physical properties, there are large differences in its usage and commercial price. In addition, some woods are nationally protected species. Therefore, it is of great importance to accurately identify the type of wood. Traditional wood recognition methods rely on experts and specialized equipment. To facilitate wood recognition, this paper proposes an approach for wood recognition using images. Next, a transfer learning technology was used to extract the textural features of wood, and a global average pooling (GAP) layer was used to reduce the number of features. Finally, the extreme learning machine (ELM) was used for classification. The recognition accuracy of this approach for the Wood Species Dataset was 93.07%, which was higher than the method used by the data provider. This approach had a higher recognition accuracy and a more stable recognition performance than previous approaches. Pooling ; Transfer of learning ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Extreme learning machine ; Machine learning ; Wood industry ; Pulp and paper industry ; Engineering ; Artificial neural network ; Forestry ; Geography Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and Land Use
Authors: Joshua Michalenko | Christopher Murzyn | Joshua Zollweg | Lydia Wermer | Alan John Van Omen | Michael Clemenson
Source: Atoms (article, 2021-01-06)
Field: Materials Science — Materials Chemistry

Machine Learning Predictions of Transition Probabilities in Atomic Spectra Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline. Computer science ; NIST ; Statistical physics ; Einstein ; Scalability ; Artificial neural network ; Spectral line ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Chemical Physics Studies
Authors: Zolaikha Rasouli | Mehdi Irani | Sonia Jafari | Raouf Ghavami
Source: Scientific Reports (article, 2021-03-19)
Field: Chemistry — Electrochemistry

Study of interaction of metal ions with methylthymol blue by chemometrics and quantum chemical calculations In this study, we determine the acidity constants of methylthymol blue (MTB) and association constants of its complexes with the Zn<sup>II</sup>, Cu<sup>II</sup>, and Fe<sup>II</sup> metal ions (MIs), through theoretical and experimental means. The complexes were characterized using UV-Visible absorption spectroscopy combined with soft/hard chemometrics methods and quantum chemical calculations. Quantum chemical calculations revealed that electronic transitions in the UV-Visible spectra of MTB have mixed n → π* and π → π* characters. The results of molar ratio and multivariate curve resolution alternating least squares (MCR-ALS) revealed the formation of successive 1:2 and 1:1 complexes (MI:MTB) for the Zn<sup>II</sup> and Cu<sup>II</sup> systems. However, the formation of successive 1:1 and 2:1 complexes are suggested for Fe<sup>II</sup> by the molar ratio and MCR-ALS. The majority of transitions observed in the UV-Visible spectra of the Zn(MTB) and Cu(MTB) complexes have ligand-to-ligand charge transfer (LLCT) characters. However, the transitions in the UV-Visible spectrum of the Fe(MTB) complex have LLCT and metal-to-ligand charge transfer (MLCT) characters. For the Fe<sub>2</sub>(MTB) complex, the lowest energy transition of has an LLCT character. However, its higher energy transitions are a mixture of LLCT, MLCT, and metal-to-metal charge transfer (MMCT) characters. The correlation between experimental and computed wavelengths revealed that the 1:1 complexes of Zn<sup>II</sup> and Cu<sup>II</sup> prefer square pyramidal geometries. However, the Fe<sup>II</sup> complexes always show octahedral geometry. Chemistry ; Metal ; Spectroscopy ; Transition metal ; Spectral line ; Absorption spectroscopy ; Ligand (biochemistry) ; Ion ; Metal ions in aqueous solution ; Crystallography ; Physical chemistry ; Analytical Chemistry (journal) ; Physics Electrochemical Analysis and Applications ; Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants
Authors: Yang Young Lu | Jeff Bilmes | Ricard A. Rodríguez‐Mias | Judit Villén | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-01-31)
Field: Chemistry — Spectroscopy

DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data Abstract Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge ( m/z ) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a “spectral library”), but this approach is expensive because the libraries do not typically generalize well across laboratories. Here we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Unlike other library-free DIA analysis methods, DIAmeter supports data generated using both wide and narrow isolation windows, can readily detect peptides containing post-translational modifications, can analyze data from a variety of instrument platforms, and is capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan. Data acquisition ; Matching (statistics) ; Mass spectrometry ; Computer science ; Tandem mass spectrometry ; Data type ; Data mining ; Chemistry ; Chromatography ; Mathematics Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Haiyan Chen | Yue Zeng | Yi Li
Source: Acta Mechanica Sinica (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning models for the secondary Bjerknes force between two insonated bubbles Abstract The secondary Bjerknes force plays a significant role in the evolution of bubble clusters. However, due to the complex dependence of the force on multiple parameters, it is highly non-trivial to include its effects in the simulations of bubble clusters. In this paper, machine learning is used to develop a data-driven model for the secondary Bjerknes force between two insonated bubbles as a function of the equilibrium radii of the bubbles, the distance between the bubbles, the amplitude and the center frequency of the ultrasound wave. The sign of the force may change with the phase difference between the oscillating bubbles. Meanwhile, the magnitude of the force varies over several orders of magnitude, which poses a serious challenge for the usual machine learning models. To overcome this difficulty, the magnitudes and the signs of the force are separated and modelled separately. A nonlinear regression is obtained with a feed-forward network model for the logarithm of the magnitude, whereas the sign is modelled by a support-vector machine model. The principle, the practical aspects related to the training and validation of the machine models are introduced. The predictions from the models are checked against the values computed from the Keller–Miksis equations. The results show that the models are extremely efficient while providing accurate estimate of the force. The models make it computationally feasible for the future simulations of the bubble clusters to include the effects of the secondary Bjerknes force. Graphic abstract Bubble ; Logarithm ; Amplitude ; Sign (mathematics) ; Magnitude (astronomy) ; Physics ; Nonlinear system ; Function (biology) ; Statistical physics ; Mechanics ; Computer science ; Mathematics ; Mathematical analysis ; Optics Ultrasound and Cavitation Phenomena ; Ultrasound and Hyperthermia Applications ; Fluid Dynamics and Mixing
Authors: Ethan Tseng | Shane Colburn | James Whitehead | Luocheng Huang | Seung‐Hwan Baek | Arka Majumdar | Felix Heide
Source: Nature Communications (preprint, 2021-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural nano-optics for high-quality thin lens imaging Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2. Optics ; Computer science ; Lens (geology) ; Feature (linguistics) ; Aperture (computer memory) ; Artificial neural network ; Artificial intelligence ; Image quality ; Quality (philosophy) ; Ranging ; Robotics ; Nano- ; Computer vision ; Physics ; Image (mathematics) ; Robot ; Telecommunications Metamaterials and Metasurfaces Applications ; Liquid Crystal Research Advancements ; Plasmonic and Surface Plasmon Research
Authors: Lijie Zhang | Bing He | Shengnan Wang | Guangcun Wang | Xiaoming Yuan
Source: Coatings (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Ecofriendly Ultrasonic Rust Removal: An Empirical Optimization Based on Response Surface Methodology This study shows that the hard-to-remove rust layer on the guide sleeve surface of a used cylinder can be removed using a specially developed, environmentally friendly formula for cleaning rust. Furthermore, we studied the rust removal technology that is based on ultrasonic cavitation and chemical etching. The surface morphology and structural components of the rust layer were observed using an electron microscope and an X-ray powder diffractometer. These tools were used to explore the mechanism of combined rust removal. Using response surface methodology (RSM) and central composite design (CCD), with the rust removal rate as our index of evaluation, data were analyzed to establish a response surface model that can determine the effect of cleaning temperature and ultrasonic power interaction on the rate of rust removal. Results showed that the main components of the rust layer on a 45 steel guide sleeve were α-FeOOH, γ-FeOOH, and Fe3O4. The rust was unevenly distributed with a loose structure, which was easily corroded by chemical reagents and peeled off under ultrasonic cavitation. With the increase in the cleaning temperature, the chemical reaction effect was intensified, and the cleaning ability was enhanced. With the increase in ultrasonic power, the cavitation effect was aggravated, the ultrasonic agitation was enhanced, and the rust removal rate was improved. According to response surface analysis and the application scope of the rust remover, we determined that the optimal cleaning temperature is 55 °C, and that the optimal ultrasonic power is 2880 W. The descaling rate under these parameters is 0.15 g·min−1·m−2. Rust (programming language) ; Response surface methodology ; Ultrasonic sensor ; Materials science ; Diffractometer ; Reagent ; Isotropic etching ; Layer (electronics) ; Etching (microfabrication) ; Composite material ; Metallurgy ; Scanning electron microscope ; Chemistry ; Chromatography ; Acoustics ; Computer science Ultrasound and Cavitation Phenomena ; Surface Treatment and Residual Stress ; Erosion and Abrasive Machining
Authors: Fardin Ghorbani | Sina Beyraghi | Javad Shabanpour | Homayoon Oraizi | Hossein Soleimani | Mohammad Soleimani
Source: Scientific Reports (preprint, 2021-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep neural network-based automatic metasurface design with a wide frequency range Beyond the scope of conventional metasurface, which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurface design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented in which the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restrict the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network's accuracy above 91%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory. Computer science ; Artificial neural network ; Range (aeronautics) ; Inverse ; Deep learning ; Computer engineering ; Artificial intelligence ; Radio spectrum ; Network architecture ; Electronic engineering ; Computer architecture ; Algorithm ; Engineering ; Telecommunications ; Mathematics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies