jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James L. McDonagh | William C. Swope | Richard Lloyd Anderson | Michael Johnston | David J. Bray
Source: ChemRxiv (preprint, 2020-01-31)
Field: Materials Science — Materials Chemistry

What Can Digitization Do For Formulated Product Innovation and Development Digitization oﬀers signiﬁcant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the beneﬁts could be signiﬁcant. Recent developments of base level technologies such as artiﬁcial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could oﬀer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators. Digitization ; Transformative learning ; Exploit ; Computer science ; Creativity ; New product development ; Product (mathematics) ; Quality (philosophy) ; Emerging technologies ; Data science ; Artificial intelligence ; Domain (mathematical analysis) ; Knowledge management ; Business ; Telecommunications ; Marketing ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yury Suleymanov
Source: Science (article, 2020-03-19)
Field: Chemistry — Organic Chemistry

Machine learning for natural extracts Cheminformatics Natural products and their derivatives continue to be an important source of drug candidates because of their structural diversity and wide-ranging biological activities, which are unmatched by synthetic compounds. Natural products are generally complex mixtures with chemical constituents that are not well characterized. Reher et al. report a nuclear magnetic resonance–based machine-learning tool, SMART, for rapid structural analysis of major constituents from crude natural extracts and for the discovery of new natural products. For example, SMART automatically characterized a cyanobacterial extract mixture and isolated a new chimeric macrolide, symplocolide A; it also dereplicated several known natural products. The proposed cheminformatic tool paves the way for new computer-aided approaches to natural product drug discovery. J. Am. Chem. Soc. 142 , 4114 (2020). Natural (archaeology) ; Computer science ; Artificial intelligence ; Biology ; Paleontology Chemical synthesis and alkaloids ; Cancer Treatment and Pharmacology ; Metabolomics and Mass Spectrometry Studies
Authors: Jonathan Palmquist
Source: DiVA at Umeå University (Umeå University) (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Detecting defects on cheese using hyperspectral image analysis Defects such as mold and bacterial stains can appear on cheese. Manually detecting defects is a time-consuming, cost-ineffective, and ergonomically unsatisfactory process for a dairy because the quality technicians must inspect each cheese before packaging. Instead, dairies would prefer an automatic detection system, but it is unclear whether reliable options are available. One potential approach is hyperspectral image analysis, which can interpret and classify chemical information from a sample. We collected hyperspectral images from a dairy using a short-wave infrared (SWIR) camera and compared three prediction models: a PLS-discriminant analysis with the software Breeze from the analysis company Prediktera and two classifiers based on a convolutional neural network and a support vector machine, both coded in Python. We found mold and lactobacilli stains using all methods, but dirt was more challenging to detect. Also, the methods had issues with false positives of lactobacilli stains. To improve accuracy, we recommend collecting more data, especially samples with lactobacilli stains and dirt, and using more non-defect cheese for validation. To find smaller defects, we propose that future work should test a visible and near-infrared (VNIR) camera with higher resolution. Though PLS-discriminant analysis did not achieve the highest accuracy, it was not far off and had the most time-effective predictions. Since Breeze already integrates PLS-discriminant analysis, it should remain in focus, but for higher accuracy Prediktera should continue to explore other methods such as neural networks. Hyperspectral imaging ; Artificial intelligence ; Computer vision ; Remote sensing ; Computer science ; Environmental science ; Geography ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: James McDonagh | William C. Swope | Richard L. Anderson | Michael Johnston | David J. Bray
Source: ChemRxiv (preprint, 2020-01-31)
Field: Materials Science — Materials Chemistry

What Can Digitization Do For Formulated Product Innovation and Development Digitization oﬀers signiﬁcant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&amp;D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the beneﬁts could be signiﬁcant. Recent developments of base level technologies such as artiﬁcial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could oﬀer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&amp;D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&amp;D. We discuss the challenges in building and deploying these demonstrators.&lt;br&gt; Digitization ; Transformative learning ; Exploit ; Computer science ; New product development ; Creativity ; Product (mathematics) ; Quality (philosophy) ; Emerging technologies ; Artificial intelligence ; Data science ; Knowledge management ; Business ; Telecommunications ; Marketing ; Mathematics Machine Learning in Materials Science
Authors: Shi Chen | Kazuyuki Demachi
Source:  (article, 2020-08-04)
Field: Materials Science — Materials Chemistry

A Novel System for Automated Proper Use Identification of Personal Protective Equipment in Decommissioning Site of Fukushima Daiichi Nuclear Power Station Abstract Decommissioning of the Fukushima Daiichi nuclear power station is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are indicated to wear Personal Protective Equipment (PPE) appropriately for radiation protection. In response to the difficulties of on-site PPE management in decommissioning site of Fukushima Daiichi NPS, this paper proposes the combination of deep learning-based object detection and individual detection using geometry relationships analysis to automatically facilitate the safety monitoring task of decommissioning workers to ensure hardhats, full-face masks and dust masks are correctly used for each zone. The experimental results demonstrate that the high precision, high recall and fast speed of the proposed approach can effectively detect decommissioning workers’ incorrect use of PPE. Nuclear decommissioning ; Nuclear power ; Personal protective equipment ; Prioritization ; Nuclear power plant ; Radiation monitoring ; Environmental science ; Radiological weapon ; Engineering ; Waste management ; Coronavirus disease 2019 (COVID-19) ; Nuclear medicine Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Rhys E. A. Goodall | Alpha A. Lee
Source: arXiv (Cornell University) (preprint, 2020-04-20)
Field: Materials Science — Materials Chemistry

Machine learnt approximations to the bridge function yield improved closures for the Ornstein-Zernike equation A key challenge for soft materials design and coarse-graining simulations is determining interaction potentials between components that give rise to desired condensed-phase structures. In theory, the Ornstein-Zernike equation provides an elegant framework for solving this inverse problem. Pioneering work in liquid state theory derived analytical closures for the framework. However, these analytical closures are approximations, valid only for specific classes of interaction potentials. In this work, we combine the physics of liquid state theory with machine learning to infer a closure directly from simulation data. The resulting closure is more accurate than commonly used closures across a broad range of interaction potentials. We show for two examples of a prototypical inverse design problem, fitting a coarse-grained simulation potential, that our approach leads to improved one-step inversion. Ornstein–Zernike equation ; Closure (psychology) ; Zernike polynomials ; Inverse ; Statistical physics ; Computer science ; Range (aeronautics) ; Inversion (geology) ; Function (biology) ; Applied mathematics ; Mathematics ; Integral equation ; Mathematical analysis ; Physics ; Geometry ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics
Authors: Iaroslav Gaponenko | Loïc Musy | Neus Domingo | N. Stucki | Albert Verdaguer | Nazanin Bassiri‐Gharb | Patrycja Paruch
Source: arXiv (Cornell University) (preprint, 2020-11-12)
Field: Materials Science — Materials Chemistry

Local and correlated studies of humidity-mediated ferroelectric thin\n film surface charge dynamics Electrochemical phenomena in ferroelectrics are of particular interest for\ncatalysis and sensing applications, with recent studies highlighting the\ncombined role of the ferroelectric polarisation, applied surface voltage and\noverall switching history. Here, we present a systematic Kelvin probe\nmicroscopy study of the effect of relative humidity and polarisation switching\nhistory on the surface charge dissipation in ferroelectric\nPb(Zr$_{0.2}$Ti$_{0.8}$)O$_3$ thin films. We analyze the interaction of surface\ncharges with ferroelectric domains through the framework of physically\nconstrained unsupervised machine learning matrix factorization, Dictionary\nLearning, and reveal a complex interplay of voltage-mediated physical processes\nunderlying the observed signal decays. Additional insight into the observed\nbehaviours is given by a Fitzhugh-Nagumo reaction-diffusion model, highlighting\nthe lateral spread and charge passivation process contributors within the\nDictionary Learning analysis.\n Ferroelectricity ; Materials science ; Passivation ; Thin film ; Charge (physics) ; Surface charge ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Qiang Yao | Yulong Miao | Xintian Li | Yan‐Wu Wang | Shukai He | Qianqian Wu
Source:  (article, 2020-09-06)
Field: Chemistry — Spectroscopy

Development and compensation experiment of HF detection system for GIS gas chamber In order to realize on-line real-time detection of HF gas in gas chamber of Gas Insulated Switchgear (GIS) of high-voltage composite electric appliances, and to perfect defect diagnosis of high-voltage electrical equipment based on gas chemical composition analysis, HF gas was detected online by tunable diode laser absorption spectroscopy (TDLAS) technology of 1321nm DFB laser. Simulation results show that changes in temperature and pressure will affect the broadened center wavelength and intensity of the HF gas absorption line. In order to make up for the detection error caused by the change of the second harmonic signal with the ambient temperature and pressure, HF/SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> standard gas with volume fraction of 50×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> and BP neural network model algorithm were used to compensate the temperature and pressure. Calibration experiments show that the amplitude of the second harmonic signal is proportional to the standard concentration of HF gas, the fitting coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is 0.9995, and the detection limit is 5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . The results show that the BP neural network temperature and pressure compensation algorithm can effectively improve the measurement accuracy, anti-interference ability and repeatability of the detection system. Analytical Chemistry (journal) ; Laser ; Calibration ; Materials science ; Physics ; Chemistry ; Optics ; Chromatography Spectroscopy and Laser Applications ; Photoacoustic and Ultrasonic Imaging ; Laser Design and Applications
Authors: Mani Valleti | Qiang Zou | Rui Xue | Lukáš Vlček | Maxim Ziatdinov | Rama K. Vasudevan | Mingming Fu | Jiaqiang Yan | David Mandrus | Zheng Gai | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-12-23)
Field: Materials Science — Materials Chemistry

Bayesian learning of adatom interactions from atomically-resolved imaging data Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow is universal and can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions. Workflow ; Statistical physics ; Bayesian inference ; Bayes' theorem ; Bayesian probability ; Generative model ; Computer science ; Bayesian optimization ; Ising model ; Parameter space ; Code (set theory) ; Biological system ; Algorithm ; Materials science ; Artificial intelligence ; Generative grammar ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Electronic and Structural Properties of Oxides
Authors: Lirong Xu | Gangcheng Wu | Xin Ji | Xingguo Wang
Source:  (preprint, 2020-10-26)
Field: Chemistry — Analytical Chemistry

Rapid assessment of quality changes in French fries during deep-frying based on FTIR spectroscopy combined with artificial neural network Fourier transform infrared (FTIR) spectroscopy combined with back propagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidation products, and triacylglycerol hydrolysis products, acid values and peroxide values), and FTIR absorbance intensity in French fries were determined during deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard method. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R2 > 0.926, RMSEC < 0.614, RMSEP< 0.550) compared with partial least-squares model (R2 > 0.876, RMSEC < 1.144, RMSEP< 1.257). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying. French fries ; Fourier transform infrared spectroscopy ; Partial least squares regression ; Chemistry ; Deep frying ; Absorbance ; Sunflower oil ; Vegetable oil ; Mathematics ; Food science ; Chromatography ; Chemical engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Atish Agarwala | Abhimanyu Das | Rina Panigrahy‎ | Qiuyi Zhang
Source: arXiv (Cornell University) (preprint, 2020-05-15)
Field: Materials Science — Materials Chemistry

Learning the gravitational force law and other analytic functions Large neural network models have been successful in learning functions of importance in many branches of science, including physics, chemistry and biology. Recent theoretical work has shown explicit learning bounds for wide networks and kernel methods on some simple classes of functions, but not on more complex functions which arise in practice. We extend these techniques to provide learning bounds for analytic functions on the sphere for any kernel method or equivalent infinitely-wide network with the corresponding activation function trained with SGD. We show that a wide, one-hidden layer ReLU network can learn analytic functions with a number of samples proportional to the derivative of a related function. Many functions important in the sciences are therefore efficiently learnable. As an example, we prove explicit bounds on learning the many-body gravitational force function given by Newton's law of gravitation. Our theoretical bounds suggest that very wide ReLU networks (and the corresponding NTK kernel) are better at learning analytic functions as compared to kernel learning with Gaussian kernels. We present experimental evidence that the many-body gravitational force function is easier to learn with ReLU networks as compared to networks with exponential activations. Newton's law of universal gravitation ; Gravitation ; Function (biology) ; Artificial neural network ; Kernel (algebra) ; Exponential function ; Gravitational force ; Gaussian ; Analytic function ; Gaussian function ; Applied mathematics ; Computer science ; Mathematics ; Theoretical computer science ; Artificial intelligence ; Physics ; Pure mathematics ; Mathematical analysis ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Domain Adaptation and Few-Shot Learning
Authors: Koji OTAWA | Kenji YAKATA | Hiroshi TANI | Lu Rengo | Shinji Koganezawa | Norio TAGAWA
Source: The Proceedings of the Conference on Information Intelligence and Precision Equipment IIP (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Raman spectroscopy analysis of magnetic head smear in environmental gas In recent years, the demand for hard disk drives (HDDs) has been growing in the midst of a huge amount of information handled with the development of AI and deep-learning. In particular, a demand for HDD for data centers is growing, and capacity of HDDs is required. Currently, the phenomenon of head smear is concerned for the heat assisted magnetic recording (HAMR), which is the proposed recording method. This problem is caused by the lubricant and diamond-like carbon (DLC) overcoat being heated to a high temperature, which reduces the reliability of the hard drive. In this study, we analyzed the materials of head smear in He or air environment using Raman spectroscopy and estimated the mechanism of the head smear generation from its spectra. As a result, it was found that the head smear was caused by not only with lubricants but also in DLC. It was found that head smear has hydrocarbon and lubricant structures. These are often in the air environment, the decomposition of DLC and lubricant is promoted by the influence of oxygen, it is expected to be those generated. Lubricant ; Raman spectroscopy ; Head (geology) ; Materials science ; Carbon fibers ; Spectroscopy ; Heat-assisted magnetic recording ; Environmental science ; Composite material ; Chemical engineering ; Acoustics ; Optics ; Geology ; Engineering ; Physics Spectroscopy and Laser Applications ; Optical Polarization and Ellipsometry
Authors: Daniel J. Higley | Hirohito Ogasawara | S. Zohar | Georgi L. Dakovski
Source: arXiv (Cornell University) (preprint, 2020-06-19)
Field: Materials Science — Surfaces, Coatings and Films

Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: A computational investigation Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution RIXS measurements (&lt;100 meV), especially in the soft x-ray range, require large and low-throughput grating spectrometers that limits measurement accuracy and simplicity. Here, we computationally investigate the performance of a different method for measuring RIXS, Photoelectron Spectrometry for Analysis of X-rays (PAX). This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of compact, high-throughput electron spectrometers. In PAX, X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. The incident X-ray spectrum is then estimated through a deconvolution algorithm that leverages concepts from machine learning. We investigate a few example PAX cases. Using the 3d levels of Ag as a converter material, and with 10$^5$ detected electrons, we accurately estimate features with 100s of meV width in a model RIXS spectrum. Using a sharp Fermi edge to encode RIXS spectra, we accurately distinguish 100 meV FWHM peaks separated by 45 meV with 10$^7$ electrons detected that were photoemitted from within 0.4 eV of the Fermi level. Spectrometer ; Resonant inelastic X-ray scattering ; Physics ; Inelastic scattering ; Scattering ; Electron ; Full width at half maximum ; X-ray spectroscopy ; Optics ; Fermi level ; Fermi Gamma-ray Space Telescope ; Spectroscopy ; Atomic physics ; Inelastic neutron scattering ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications