jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengtao Gan | Orion L. Kafka | Niranjan D. Parab | Cang Zhao | Olle Heinonen | Tong Sun | Wing Liu
Source: arXiv (Cornell University) (article, 2020-04-30)
Field: Materials Science — Materials Chemistry

Universal low-dimensional scaling laws in 3D printing of metals We leverage dimensional analysis and genetic programming (a type of machine learning) to discover two strikingly simple but universal scaling laws, which remain accurate for different materials, processing conditions, and machines in metal three-dimensional (3D) printing. The first one is extracted from high-fidelity high-speed synchrotron X-ray imaging, and defines a new dimensionless number, Keyhole number, to predict melt-pool vapor depression depth. The second predicts porosity using the Keyhole number and another dimensionless number, normalized energy density. By reducing the dimensions of these longstanding problems, the low-dimensional scaling laws will aid process optimization and defect elimination, and potentially lead to a quantitative predictive framework for the critical issues in metal 3D printing. Moreover, the method itself is broadly applicable to a range of scientific areas. Keyhole ; Dimensionless quantity ; Scaling ; Scaling law ; Leverage (statistics) ; Computer science ; Porosity ; Synchrotron ; Fidelity ; Materials science ; Statistical physics ; Mechanical engineering ; Mechanics ; Computational science ; Physics ; Artificial intelligence ; Engineering ; Mathematics ; Geometry ; Optics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Industrial Vision Systems and Defect Detection
Authors: Zhuo Wang | Jianglin Wei | Jing Feng | Yingxu Wang | Yumin Lai | Shu‐Qian Hou
Source: Journal of Micromechanics and Molecular Physics (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Research on the Construction of the General Control Platform for Yunnan Rare and Precious Metal Materials Genetic Engineering In this paper, we initially construct a master control platform of Yunnan rare and precious metal materials (RAPMMs) genetic engineering framework based on the idea of material gene engineering, which includes a master material data management sub-platform, a high-throughput computing sub-platform and a high-throughput material preparation and characterization sub-platform, and connects the data information in the sub-platform. The database covers the standard performance data of RAPMMs from all over the world, and the initial amount of data is 2.2 million items, which provides data resources for deep learning of material big data in the future. In addition, we have researched the fusion and convergence technology of multi-source heterogeneous RAPMMs data, which support the management, processing and storage of all kinds of materials professional data. In order to reduce the researchers’ need for precise retrieval of large amounts of data, the deep search technology based on rare metal data is investigated to achieve precise search in a large data environment and provide professional visualization and statistical analysis techniques. Through the technical research on the interface of the open high-throughput computing platform, the online generation and submission of the operations of the first-principles computing software VASP are realized, and the automatic data identification and import of the calculation result files are achieved. Finally, it realizes the functions of controlling and controlling the high-throughput calculation platform, high-throughput preparation and characterization platform, and data platform, with the functions of resource coordination and scheduling, information sharing, public database management, and relevant information retrieval and query, etc., and establishes a common database structure for rare and precious metals, which lays a solid foundation for the future development of material genetic engineering. Computer science ; Throughput ; Data management ; Software ; Visualization ; Construct (python library) ; Big data ; Database ; Data mining ; Operating system Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Authors: Sangwon Lee | Baekjun Kim | Jihan Kim
Source: ChemRxiv (preprint, 2020-05-20)
Field: Chemistry — Inorganic Chemistry

Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;−3&lt;/sup&gt; and 96 MOFs with methane working capacity over 208 cm&lt;sup&gt;3&lt;/sup&gt; cm&lt;sup&gt;−3&lt;/sup&gt;, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well. Algorithm ; Metal-organic framework ; Modular design ; Set (abstract data type) ; Computer science ; Variety (cybernetics) ; Methane ; Evolutionary algorithm ; Machine learning ; Nanotechnology ; Artificial intelligence ; Chemistry ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: Yasaman Pourbakhshi | Maryam Farhadian | Abdulrahman Bahrami | Leila Tajik | Farshid Ghorbani Shanha | Mohammad Javad Assari
Source: Jundishapur Journal of Health Sciences (article, 2020-10-20)
Field: Chemistry — Analytical Chemistry

Application of Artificial Neural Network and Response Surface Methodology in Optimization of Cold Fiber Headspace Solid-Phase Microextraction (CF-HS-SPME) for Analysis of 2, 5-Hexandion in Urine Samples Background: Cold-Fiber Solid-Phase Microextraction (CF-SPME) is often used for the extraction of volatile and semi-volatile compounds from complex matrices. Multivariate statistical optimization techniques can save time and chemicals and thus decrease the analytical cost in comparison with the Single Variable Approach (SVA). Over the past few decades, different beneficial mathematical tools have been developed for the optimization of separation processes. Objectives: In this study, the Artificial Neural Network (ANN) and Response Surface Methodology (RSM) applications were compared for CF-SPME optimization to determine 2, 5-hexandion in urine samples. 2, 5-Hexanedione is a colorless liquid and the main metabolite of n-hexane as a result of occupational exposure. Methods: N-hexane is widely used in the rubber industry, food processing, solvents, and medicinal drugs and has adverse effects such as neurotoxicity on humans. Thus, biological monitoring, analytic methods, and mathematical and statistical techniques concerning 2.5-HD are very important. The RSM and ANN are mathematical and statistical techniques applied for the optimization and process modeling. Designing an experiment based on the Historical Data Design (HDD) of RSM was adopted to evaluate the relationship between independent parameters such as extraction temperature, extraction time, sample volume, and extraction efficiency of 2, 5-hexandion. Results: The models were compared for their predictive ability by the coefficient of determination (R2) and Root Mean Square Error (RSME) based on the train and test dataset. Conclusions: The results were highly significant (P &lt; 0.05) for the optimization of variables. The ANN model had more generalizability than the RSM model. Also, the ANN had higher predictive accuracy. Response surface methodology ; Solid-phase microextraction ; Chromatography ; Coefficient of determination ; Extraction (chemistry) ; Artificial neural network ; Computer science ; Chemistry ; Machine learning ; Gas chromatography–mass spectrometry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis
Authors: Alauddin Ahmed | Donald J. Siegel
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Predicting Hydrogen Storage in Mofs &lt;i&gt;via&lt;/i&gt; Machine Learning Hydrogen storage ; Hydrogen ; Chemistry ; Computer science ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Hydrogen Storage and Materials
Authors: Jihang Wang | Evan Krieger | Lucas Zbur | Joanne Gilligan | Rick Beideman | J. A. Beckstead | Oksana Klueva | Maria Conner | Erica Maney | Patrick J. Treado
Source:  (article, 2020-04-24)
Field: Chemistry — Analytical Chemistry

Sprayed or soaked concealed drug detection using SWIR hyperspectral imaging The smuggling of drug into correctional facilities through the mail is a major concern. ChemImage has developed the VeroVision<sup>TM</sup> mail screener system, which highlights drugs from background based on score imagery computed from selected wavelengths based on the chemical signatures. More recently, sophisticated techniques to hide drugs by dissolution into paper are being used. We introduce a combined heterogeneous anomaly detection with a deep learning classifier. Anomaly detection initially extracts suspect stain patterns. A You Only Look Once (YOLO) based classifier then classifies anomalies as drug or non-drug stain patterns. We report its first successful detection on a limited set of meth samples, with 87.4% probability of detection (PD) and 7.0% probability of false alarm (PFA). The results show that widefield, multispectral short-field infrared (SWIR) imaging can allow for dissolved concealed drug screening of mail which has benefits for mail inspection efficiency and accuracy. Hyperspectral imaging ; Multispectral image ; Computer science ; Artificial intelligence ; Anomaly detection ; Drug detection ; Classifier (UML) ; False alarm ; Pattern recognition (psychology) ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source:  (preprint, 2020-06-12)
Field: Materials Science — Materials Chemistry

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy. Maxima and minima ; Density functional theory ; Ab initio ; Conformational isomerism ; Coupled cluster ; Electronic structure ; Single point ; Point (geometry) ; Cluster (spacecraft) ; Statistical physics ; Physics ; Molecule ; Molecular physics ; Computer science ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Maria El Abbassi | Jan Overbeck | Oliver Braun | Michel Calame | Herre S. J. van der Zant | Mickael L. Perrin
Source: arXiv (Cornell University) (article, 2020-04-29)
Field: Chemistry — Electrochemistry

Universal approach for unsupervised classification of univariate data Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particularly useful for applications without a priori knowledge of the data structure. Here, we introduce a universal approach for unsupervised data classification relevant for any dataset consisting of a series of univariate measurements. It is therefore ideally suited for a wide range of measurement types. Here, we apply it to the field of nanoelectronics and spectroscopy to identify meaningful structures in data sets, providing physically relevant information about the system under study. An important step in our approach is the guidelines for the estimation of the optimum number of clusters. In addition, we have performed an extensive benchmark of ML approaches found in literature for the classification of molecular break junction traces. We find that several feature space construction methods we have introduced and clustering algorithms yield accuracies up to 20% higher than methods reported so far, increasing the Fowlkes-Mallows index from 0.77 up to 0.91 Cluster analysis ; Univariate ; Computer science ; Data mining ; A priori and a posteriori ; Benchmark (surveying) ; Unsupervised learning ; Identification (biology) ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Range (aeronautics) ; Feature (linguistics) ; Pattern recognition (psychology) ; Multivariate statistics ; Mathematics ; Engineering Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: M. Vaziri
Source: Microscopy Research and Technique (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

<scp>High‐contrast</scp> optical microscopy of graphene sheets In the way of making graphene an industry-friendly material, it must be mass-produced with high-quality and reduced cost over large areas. Assisted by machine-learning techniques, rapid, nondestructive and accurate determination of large graphene sheets on SiO<sub>2</sub> /Si substrates has been made possible in recent years by the optical microscopy method. Optimization of the substrate to achieve the maximum contrast can further extend the application of the optical microscopy method for quality control of the mass-produced graphene. Graphene/n<sub>2</sub> /n<sub>3</sub> three-layer structures, where n<sub>2</sub> and n<sub>3</sub> are refractive indices, are routinely used for identifying the number of graphene layers by optical reflection microscopy. In this paper, two analytical equations are derived that can be easily used for high-contrast optical imaging of graphene sheets without any need to resort to the cumbersome numerical methods. One of the equations is derived for choosing the best material with refractive index n<sub>2</sub> that when coated on a substrate with refractive index n<sub>3</sub> , maximizes the optical contrast. The other equation is derived for finding the best thickness of the SiO<sub>2</sub> layer in graphene/SiO<sub>2</sub> /Si structures, which are in common use for fabrication of graphene-based devices. The results are implemented in a MATLAB GUI, see Supporting Information, to assist the users in using the equations. Graphene ; Refractive index ; Optical microscope ; Microscopy ; Materials science ; Substrate (aquarium) ; Optics ; Layer (electronics) ; Contrast (vision) ; Optoelectronics ; Nanotechnology ; Composite material ; Physics ; Scanning electron microscope Graphene research and applications ; Photonic Crystals and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Mark P. Oxley | Junqi Yin | Nikolay Borodinov | Suhas Somnath | Maxim Ziatdinov | Andrew R. Lupini | Stephen Jesse | Rama K. Vasudevan | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-02-20)
Field: Materials Science — Materials Chemistry

Deep learning of interface structures from the 4D STEM data: cation intermixing vs. roughening Interface structures in complex oxides remain one of the active areas of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D scanning transmission electron microscopy (STEM) datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO3 and SrTiO3, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We validate the DCNN on simulated data and show that it is possible (with &gt;95% accuracy) to identify a physically rough from a chemically diffuse interface and achieve 85% accuracy in determination of buried step positions within the interface. The method shown here is general and can be applied for any inverse imaging problem where forward models are present. Interface (matter) ; Focus (optics) ; Scanning transmission electron microscopy ; Convolutional neural network ; Computer science ; Diffraction ; Transmission electron microscopy ; Inverse ; Deep learning ; Artificial intelligence ; Transmission (telecommunications) ; Biological system ; Materials science ; Pattern recognition (psychology) ; Physics ; Nanotechnology ; Optics ; Geometry ; Mathematics ; Biology ; Parallel computing Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science
Authors: Nungnit Wattanavichean | Jirasin Boonchai | Sasithon Yodthong | Chakkrit Preuksakarn | Scott C.-H. Huang | Thattapon Surasak
Source:  (article, 2020-10-21)
Field: Chemistry — Analytical Chemistry

Discriminate Raman/GFP Spectra of Yeast Mitochondria Using Convolutional Neural Network Raman spectroscopy is one of the well-known techniques that be able to identify molecular species in the complex system. In the present work, 633 nm Raman laser and GFP excitation light are irradiated at the sample simultaneously. It allows us to know whether Raman spectra are indeed taken from mitochondria. However, the mixture spectra of RamanlGFP are very complicated and impossible to correctly identify manually. Therefore, convolutional neural network is integrated to our work in order to distinguish RamanlGFP spectra with and without signal from GFP as GFP-positive and GFP-negative spectra, respectively. The system architecture is the adapted visual geometry group (VGG) with 14 convolutional layers. Batch normalization is added, and fully-connected layer is increased to 5 layers. Various size of RamanlGFP images were used to train our VGG network. The black and white background color also provided to gain the best result. The highest accuracy of 87.42% was achieved to separate GFP-positive and -negative spectra with black background and 68×52 pixel images. In the final step, the separated GFP-positive and -negative spectra were distributed in two different folders for further Raman spectra analysis of mitochondria. Raman spectroscopy ; Convolutional neural network ; Green fluorescent protein ; Spectral line ; Normalization (sociology) ; Biological system ; Chemistry ; Computer science ; Materials science ; Artificial intelligence ; Optics ; Physics ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Sabrya E. van Heijst | Masaki Mukai | Eiji Okunishi | Hiroki Hashiguchi | Laurien I. Roest | Louis Maduro | Juan Rojo | Sonia Conesa‐Boj
Source: arXiv (Cornell University) (preprint, 2020-09-17)
Field: Materials Science — Materials Chemistry

Illuminating the electronic properties of WS$_2$ polytypism with electron microscopy Tailoring the specific stacking sequences (polytypes) of layered materials represents a powerful strategy to identify and design novel physical properties. While nanostructures built upon transition-metal dichalcogenides (TMDs) with either the 2H or 3R crystalline phases have been routinely studied, our knowledge of those based on mixed 2H/3R polytypes is far more limited. Here we report on the characterization of mixed 2H/3R free-standing WS$_2$ nanostructures displaying a flower-like configuration by means of advanced transmission electron microscopy. We correlate their rich variety of shape-morphology combinations with relevant local electronic properties such as their edge, surface, and bulk plasmons. Electron energy-loss spectroscopy combined with machine learning reveals that the 2H/3R polytype displays an indirect band gap with $E_{\rm BG}=1.6^{+0.3}_{-0.2}~{\rm eV}$. Further, we identify the presence of energy-gain peaks in the EEL spectra characterized by a gain-to-loss ratio $I_G/I_L&gt;1$. Such property could be exploited to develop novel cooling strategies for atomically thin TMD nanostructures and devices built upon them. Our results represent a stepping stone towards an improved understanding of TMDs nanostructures based on mixed crystalline phases. Stacking ; Materials science ; Nanostructure ; Transmission electron microscopy ; Characterization (materials science) ; Electron energy loss spectroscopy ; Plasmon ; Scanning transmission electron microscopy ; Spectroscopy ; Nanotechnology ; Band gap ; Crystallography ; Optoelectronics ; Chemistry ; Physics ; Nuclear magnetic resonance 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films