jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyunwoo Son | Sun‐Je Kim | Jongwoo Hong | Jangwoon Sung | Byoungho Lee
Source: Research Square (preprint, 2021-08-20)
Field: Chemistry — Organic Chemistry

Design of Highly Perceptible Dual-resonance All-dielectric Metasurface Colorimetric Sensor via Deep Neural Networks <title>Abstract</title> Colorimetric sensing, which provides effective detection of bio-molecular signals with one’s naked eye, is an exceptionally promising sensing technique in that it enables convenient detection and simplification of entire sensing system. Though colorimetric sensors based on all-dielectric nanostructures have potential to exhibit distinct color variations enabling manageable detection due to their trivial intrinsic loss, there is crucial limitation that the sensitivity to environmental changes lags behind their plasmonic counterparts because of relatively small region of near field-analyte interaction of the dielectric Mie-type resonator. To overcome this challenge, we proposed all-dielectric metasurface colorimetric sensor which exhibits dual-resonance in the visible region. Thereafter, we confirmed with simulation that, in the elaborately designed dual-Lorentzian-type spectra, highly perceptible variations of structural color were manifested even in minute change of peripheral refractive index. In addition to verifying physical effectiveness of the superior colorimetric sensing performance appearing in the dual-resonance type sensor, by combining advanced optimization technique utilizing deep neural networks, we attempted to maximize sensing performance while obtaining dramatic improvement of design efficiency. Through well-trained deep neural network that accurately simulates the input target spectrum, we numerically verified that designed colorimetric sensor shows a remarkable sensing resolution distinguishable up to change of refractive index of 0.0086. Refractive index ; Dielectric ; Materials science ; Sensitivity (control systems) ; Surface plasmon resonance ; Optoelectronics ; Colorimetry ; Plasmon ; Computer science ; Artificial neural network ; Resonator ; Resonance (particle physics) ; Optics ; Nanotechnology ; Electronic engineering ; Artificial intelligence ; Physics ; Nanoparticle ; Computer vision ; Engineering Polydiacetylene-based materials and applications ; Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research
Authors: Ji Guo | Yujia Lou | Wanyi Wang | Xianhua Wu
Source: Journal of Advanced Transportation (article, 2021-05-11)
Field: Chemistry — Analytical Chemistry

Optimization Modeling and Empirical Research on Gasoline Octane Loss Based on Data Analysis Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment. Gasoline ; Octane rating ; Octane ; Petrochemical ; Fluid catalytic cracking ; Cracking ; Petroleum product ; Operating cost ; Process engineering ; Process (computing) ; Petroleum ; Computer science ; Engineering ; Waste management ; Chemistry ; Organic chemistry Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Heat transfer and supercritical fluids
Authors: Pierre Wüthrich | Jun Jin Choong | Shinya Yuki
Source: ChemRxiv (preprint, 2021-09-12)
Field: Materials Science — Materials Chemistry

InFrag: Using Attribution-based Explainability to Guide Deep Molecular Optimization The recently proposed Genetic expert guided learning (GEGL) framework has demonstrated impressive performances on several \textit{de novo} molecular design tasks. Despite the displayed state-of-the art results, the proposed system relies on an expert-designed Genetic expert. Although hand-crafted experts allow to navigate the chemical space efficiently, designing such experts requires a significant amount of effort and might contain inherent biases which can potentially slow down convergence or even lead to sub-optimal solutions. In this research, we propose a novel genetic expert named \textit{InFrag} which is free of design rules and can generate new molecules by combining promising molecular fragments. Fragments are obtained by using an additional graph convolutional neural network which computes attributions for each atom for a given molecule. Molecular substructures which contribute positively to the task score are kept and combined to propose novel molecules. We experimentally demonstrate that, within the GEGL framework, our proposed attribution-based genetic expert is either competitive or outperforms the original expert-designed genetic expert on goal-directed optimization tasks. When limiting the number of optimization rounds to one and three rounds, a performance increase of approximately 43% and 20% respectively is observed compared to the baseline genetic expert. Furthermore, we empirically show that combining several experts that share a fixed sampling budget at each optimization round generally improves or maintains the overall performance of the framework. Computer science ; Task (project management) ; Machine learning ; Artificial intelligence ; Convergence (economics) ; Expert system ; Genetic algorithm ; Chemical space ; Data mining ; Theoretical computer science ; Bioinformatics ; Biology ; Drug discovery ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Huai Sun
Source:  (preprint, 2021-07-21)
Field: Materials Science — Materials Chemistry

A Comparative Study of Marginalized Graph Kernel and Message Passing Neural Network &lt;p&gt;This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combining with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the Directed Message Passing Neural Network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operates on molecular graphs and global molecular features, respectively. Bayesian optimization was used to get the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that the predictions of both models are correlated with similar performance, and the ensemble prediction of both models performs better than either of them. Through principal component analysis, we found that the features extracted by the hybrid kernel are similar to those extracted by D-MPNN. The advantage of D-MPNN lies in computational efficiency, while the advantage of the graph kernel models lies in the inherent uncertainty quantification and accurate uncertainty quantification. All codes for graph kernel machines used in this work can be found at &lt;a href="https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine"&gt;https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine&lt;/a&gt;.&lt;/p&gt; Kernel (algebra) ; Computer science ; Kernel principal component analysis ; Graph kernel ; Kernel method ; Hyperparameter ; Graph ; Artificial intelligence ; Artificial neural network ; Algorithm ; Kernel embedding of distributions ; Machine learning ; Pattern recognition (psychology) ; Theoretical computer science ; Mathematics ; Discrete mathematics ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Alfredo Serrano-Jiménez | Alberto S. Muzas | Yaolong Zhang | Bin Jiang | Ivor Lončarić | Iñaki Juaristi | M. Alducin
Source: Communities in ADDI (University of the Basque Country) (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Photoinduced desorption dynamics of CO from Pd(111): a neural network approach Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (Te,Tl)-AIMDEF [Alducin, M.; Phys. Rev. Lett. 2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y.; J. Phys. Chem. Lett. 2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (Te,Tl)-AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90–1000 K); a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms; and the varying CO coverage caused by the abundant desorption events. Desorption ; Dynamics (music) ; Artificial neural network ; Materials science ; Physics ; Chemistry ; Physical chemistry ; Computer science ; Artificial intelligence ; Acoustics ; Adsorption Catalytic Processes in Materials Science
Authors: Dong Hyeon Mok | Seoin Back
Source: ChemRxiv (preprint, 2021-05-12)
Field: Materials Science — Materials Chemistry

Atomic Structure-free Representation of Active Motifs for Expedited Catalyst Discovery For CO* and H* binding energy prediction, we develop new representation of catalyst surface which split surface into three types of site, first nearest neighbor of adsorbates and second nearest neighbor in same layer and sublayer. From this representation and machine learning regression model, we achieve reasonable accuracy (0.120 eV for CO* and 0.105 eV for H*) with quick training (~200 sec using CPU). Because our representation does not require density functional calculation and atomic structure modelling, it can predict binding energies of possible active motifs without time-consuming steps. Representation (politics) ; k-nearest neighbors algorithm ; Catalysis ; Computer science ; Active site ; Chemistry ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Catalysis and Oxidation Reactions
Authors: Jaber Rezaei Mianroodi | Shahed Rezaei | Nima H. Siboni | Bai‐Xiang Xu | Dierk Raabe
Source: TUbilio (Technical University of Darmstadt) (article, 2021-08-05)
Field: Materials Science — Materials Chemistry

Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence Constitutive equation ; Finite element method ; Convolutional neural network ; Continuum mechanics ; Lossless compression ; Computer science ; Statistical physics ; Artificial neural network ; Elasticity (physics) ; Algorithm ; Artificial intelligence ; Mechanics ; Physics ; Data compression ; Thermodynamics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Composite Material Mechanics
Authors: Yuanyi Wu
Source: Scholarship@Western (Western University) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Study of The Deep Learning-based Monitoring and Efficient Numerical Modeling Methodologies for Crystallization Processes Driven by the increasing demands of producing consistent and high-quality crystals for high value-added products such as pharmaceutical ingredients, the operation and design of a crystallization process have phased from an empirical trial-and-error approach to the modern frameworks powered by the online process analytical technologies (PATs) and model-based process optimization techniques.\nThe one-dimensional crystal size distribution (CSD) measured by the well-established PATs is inadequate due to the missing particle morphology information. A major contribution of this thesis is to develop an image analysis-based PAT powered by the deep learning image processing techniques, whose accuracy and functionality outperformed the traditional PATs and other image analysis techniques. The PAT was deployed to monitor and study the slurry mixture of glass beads and catalyst particles as well as a taurine-water batch crystallization process. The results confirmed the superb accuracy of two-dimensional size and shape characterization in a challengingly high solids concentration. The classification capability enabled unparalleled functionalities including quantification of agglomeration level and characterization of different polymorphs based on their distinct appearances. A computerized crystallization platform was built with the developed PAT, which could automate the time-consuming experiments for determining the metastable zone width (MSZW) and induction time of a crystallization system. The application of the PAT revealed the potential to simplify and speed up the research and development stage of a crystallization process.\nThe rich two-dimensional crystal size and shape information provided by our PAT enabled more descriptive multi-dimensional modeling for the better prediction of the crystallization process. The novel population array (PA) solver developed in this thesis could solve the multi-dimensional crystallization population balance equation (PBE) more computationally efficient than the existing discretization-based numerical methods without compromising the accuracy. The PA solver could accurately model the complex phenomena including agglomeration, breakage, and size-dependent growth. The efficient computation enables solving the complex multi-dimensional PBE for crystal morphology modeling. The combination of the innovative PAT and modeling technique is a significant contribution to the crystallization field that enables better understanding and more effective control of a crystallization process. Computer science ; Crystallization ; Artificial intelligence ; Engineering Crystallization and Solubility Studies ; Mineral Processing and Grinding ; Industrial Vision Systems and Defect Detection
Authors: Soojung Baek | Kristofer G. Reyes
Source: Computational Materials Science (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Problem-fluent models for complex decision-making in autonomous materials research Computer science ; Work (physics) ; Coupling (piping) ; Management science ; Machine learning ; Artificial intelligence ; Data science ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Machine Learning and Algorithms ; Fault Detection and Control Systems
Authors: Andrey V. Orekhov
Source: Mathematics (article, 2021-09-17)
Field: Chemistry — Spectroscopy

Quasi-Deterministic Processes with Monotonic Trajectories and Unsupervised Machine Learning This paper aims to consider approximation-estimation tests for decision-making by machine-learning methods, and integral-estimation tests are defined, which is a generalization for the continuous case. Approximation-estimation tests are measurable sampling functions (statistics) that estimate the approximation error of monotonically increasing number sequences in different classes of functions. These tests make it possible to determine the Markov moments of a qualitative change in the increase in such sequences, from linear to nonlinear type. If these sequences are trajectories of discrete quasi-deterministic random processes, then moments of change in the nature of their growth and qualitative change in the process match up. For example, in cluster analysis, approximation-estimation tests are a formal generalization of the “elbow method” heuristic. In solid mechanics, they can be used to determine the proportionality limit for the stress strain curve (boundaries of application of Hooke’s law). In molecular biology methods, approximation-estimation tests make it possible to determine the beginning of the exponential phase and the transition to the plateau phase for the curves of fluorescence accumulation of the real-time polymerase chain reaction, etc. Generalization ; Mathematics ; Monotonic function ; Applied mathematics ; Heuristic ; Markov chain ; Nonlinear system ; Mathematical optimization ; Mathematical analysis ; Statistics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Anomaly Detection Techniques and Applications
Authors: Chia‐Hao Lee | Abid Khan | Di Luo | Tatiane Pereira dos Santos | Chuqiao Shi | Blanka Janicek | Sangmin Kang | Nahil Sobh | Wenjuan Zhu | André Schleife | Bryan K. Clark | Pinshane Y. Huang
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Probing the Strain Fields of Single-Atom Defects in 2D materials with Sub-Picometer Precision Analyzing large volumes of data using machine learning can make it possible to access hidden information in electron microscopy data. In our work, we use this approach to detect picometer-scale displacements that result from single atom defects, such as vacancies and substitutional dopants in 2D materials This work utilizes deep learning techniques based on fully convolutional networks (FCN) to locate, classify, and segment defects in aberration-corrected scanning transmission electron microscopy (STEM) images [2]. First, we use simulated data to train the FCNs to identify each defect type with high accuracy in experimental data -comparable to or even exceeding human labeling. Next, we acquired large atomic resolution dataset on 2D materials samples transferred using large-area, clean transfer techniques. Then, we identify hundreds or thousands of individual point defects, screen them for the presence of long-range strain fields, and then align and sum images from nominally equivalent defects. This approach allows us to overcome the fundamental limits set by electron beam damage and measure the 2D coordinates of atoms with up to 0.3 pm-comparable to the precision of density functional theory (DFT) simulations, and sufficient to measure the strain field of a single atom more than a nanometer away from the defect core. Using this combination of atomic resolution and sub-picometer precision, we are able to detect radially oscillating strain fields that deviate clearly from a continuum elastic model, which we attribute to defectinduced charge redistribution. Moving beyond single atoms, we study defect clusters to study how the strain fields from different defects couple with one another. By carefully benchmarking uncertainties and potential sources for error in our data, we find that mechanical drifts and the gaussian fitting process we use to determine atomic positions are current limiters for the precision we achieve, indicating potential routes to even further improve the ultimate precision at which atomic coordinates can be measured in the STEM. Our methods can readily be extended to other beam-sensitive materials and other types of atomic resolution datasets. Strain (injury) ; Atom (system on chip) ; Content (measure theory) ; Materials science ; Action (physics) ; Physics ; Computer science ; Quantum mechanics ; Mathematics ; Embedded system ; Biology ; Mathematical analysis Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Il‐Hyung Lee | Sam Passaro | Selin Ozturk | Weitian Wang
Source: Research Square (preprint, 2021-03-01)
Field: Chemistry — Spectroscopy

Intelligent Fluorescence Image Analysis of Giant Unilamellar Vesicles and Protein Liquid Phase Droplets by Whole Z-stack Analysis Abstract Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired information from the intensity traces. Analyzing giant unilamellar vesicles is one of these tasks. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability. We developed an intelligent analysis routine based on the 3D information of whole z-stack images. The program identifies the valid vesicles to analyze and calculates the desired data automatically. The program can examine the amount of protein binding on the membranes and determine the state of domain phase separation by calculating the fluorescence intensity trace along the membranes. We also show that the method can easily be applied to similar analyses, such as intensity analysis of phase-separated protein droplets. A deep learning-based classification approach enables the identification of vesicles even from relatively complex samples. We demonstrate that the proposed artificial intelligence-assisted classification can further enhance the accuracy of the analysis close to the performance of manual examination. Vesicle ; Computer science ; Membrane ; Identification (biology) ; Biological system ; Reliability (semiconductor) ; Phase (matter) ; Artificial intelligence ; Lipid vesicle ; Stack (abstract data type) ; Domain (mathematical analysis) ; Image (mathematics) ; Fluorescence ; Chemistry ; Pattern recognition (psychology) ; Physics ; Mathematics ; Optics ; Biology Advanced Proteomics Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics