jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roald Hoffmann | Jean‐Paul Malrieu
Source: Angewandte Chemie International Edition (review, 2019-11-01)
Field: Materials Science — Materials Chemistry

Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse In the second part of this Essay, we leave philosophy, and begin by describing Roald's being trashed by simulation. This leads us to a general sketch of artificial intelligence (AI), Searle's Chinese room, and Strevens' account of what a go-playing program knows. Back to our terrain-we ask "Quantum Chemistry, † ca. 2020?" Then we move to examples of Big Data, machine learning and neural networks in action, first in chemistry and then affecting social matters, trivial to scary. We argue that moral decisions are hardly to be left to a computer. And that posited causes, even if recognized as provisional, represent a much deeper level of understanding than correlations. At this point, we try to pull the reader up, giving voice to the opposing view of an optimistic, limitless future. But we don't do justice to that view-how could we, older mammals on the way to extinction that we are? We try. But then we return to fuss, questioning the ascetic dimension of scientists, their romance with black boxes. And argue for a science of many tongues. Sketch ; Action (physics) ; Romance ; Point (geometry) ; Economic Justice ; Epistemology ; Computer science ; Cognitive science ; Artificial intelligence ; Chemistry ; Sociology ; Psychology ; Philosophy ; Psychoanalysis ; Mathematics ; Quantum mechanics ; Physics ; Law ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: G. Lambard | Ekaterina Gracheva
Source: arXiv (Cornell University) (article, 2019-06-20)
Field: Materials Science — Materials Chemistry

SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors There is more and more evidence that machine learning can be successfully applied in materials science and related fields. However, datasets in these fields are often quite small ($\ll1000$ samples). It makes the most advanced machine learning techniques remain neglected, as they are considered to be applicable to big data only. Moreover, materials informatics methods often rely on human-engineered descriptors, that should be carefully chosen, or even created, to fit the physicochemical property that one intends to predict. In this article, we propose a new method that tackles both the issue of small datasets and the difficulty of task-specific descriptors development. The SMILES-X is an autonomous pipeline for molecular compounds characterisation based on a \{Embed-Encode-Attend-Predict\} neural architecture with a data-specific Bayesian hyper-parameters optimisation. The only input to the architecture -- the SMILES strings -- are de-canonicalised in order to efficiently augment the data. One of the key features of the architecture is the attention mechanism, which enables the interpretation of output predictions without extra computational cost. The SMILES-X shows new state-of-the-art results in the inference of aqueous solubility ($\overline{RMSE}_{test} \simeq 0.57 \pm 0.07$ mols/L), hydration free energy ($\overline{RMSE}_{test} \simeq 0.81 \pm 0.22$ kcal/mol, which is $\sim 24.5\%$ better than molecular dynamics simulations), and octanol/water distribution coefficient ($\overline{RMSE}_{test} \simeq 0.59 \pm 0.02$ for LogD at pH 7.4) of molecular compounds. The SMILES-X is intended to become an important asset in the toolkit of materials scientists and chemists. The source code for the SMILES-X is available at \href{https://github.com/GLambard/SMILES-X}{github.com/GLambard/SMILES-X}. Computer science ; Artificial intelligence ; Machine learning ; Materials informatics ; Mean squared error ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shin Kiyohara | Masashi Tsubaki | Kunyen Liao | Teruyasu Mizoguchi
Source: Journal of Physics Materials (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

Quantitative estimation of properties from core-loss spectrum via neural network Localized structures in nano- and sub-nano-scales strongly affect material properties. Thus, some spectroscopic techniques have been used to characterize local atomic and electronic structures. If material properties can be directly 'measured' via spectral observations, the atomic-scale understanding of the material properties would be dramatically facilitated. In this paper, we have attempted to unveil the hidden information about the material properties directly and quantitatively based on core-loss spectra. We predicted six properties, including three geometrical and three chemical bonding properties, by a simple feedforward neural network, and achieved considerably sufficient accuracy. Moreover, we applied the constructed model to the noisy experimental spectrum and could predict the six properties precisely. This successful prediction implies that this method can pave the way for local measurement of the material properties. Spectral properties ; Artificial neural network ; Biological system ; Material properties ; Core (optical fiber) ; Computer science ; Materials science ; Spectral line ; Atomic units ; Statistical physics ; Simple (philosophy) ; Physics ; Artificial intelligence ; Astrophysics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Noam Bernstein | Bishal Bhattarai | Gábor Cśanyi | D. A. Drabold | Stephen R. Elliott | Volker L. Deringer
Source: Cambridge University Engineering Department Publications Database (article, 2019-04-24)
Field: Materials Science — Materials Chemistry

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We com- bine a quantitative description of the nearest- and next-nearest- neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 10^10 K/s. Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of Amorphous solid ; Atomic units ; Metric (unit) ; Silicon ; Amorphous silicon ; Computer science ; Statistical physics ; Materials science ; Chemical physics ; Chemistry ; Physics ; Crystalline silicon ; Crystallography Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Kemal Özkan | Şahin Işık | Büşra Topsakal Yavuz
Source: Journal of the Science of Food and Agriculture (article, 2019-04-12)
Field: Chemistry — Analytical Chemistry

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry. VNIR ; Hyperspectral imaging ; Artificial intelligence ; RGB color model ; Identification (biology) ; Computer science ; Robustness (evolution) ; Remote sensing ; Machine learning ; Agricultural engineering ; Environmental science ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Shuxing Li | Rong‐Jun Xie
Source: ECS Journal of Solid State Science and Technology (article, 2019-10-29)
Field: Materials Science — Materials Chemistry

Critical Review—Data-Driven Discovery of Novel Phosphors Innovative phosphor materials are continuously pursued with advances in solid-state lighting and display technologies. Traditional methods of searching for new phosphors are usually based on a time-consuming and laborious trail-and-error process. Newly emerging approaches, such as high-throughput calculations and machine learning, provide great potentials to significantly accelerate the design and discovery of new materials. Here, we overview the recent development on searching for phosphor materials assisted by data-driven computations, aiming to (i) clarify the composition-structure-property relationships; (ii) construct materials descriptors by computation methods; and (iii) design novel phosphors by combining computation with experiments. Finally, future prospects for data-driven discovery of novel phosphors are highlighted. Phosphor ; Computation ; Computer science ; Throughput ; Process (computing) ; Construct (python library) ; Property (philosophy) ; Materials science ; Algorithm ; Telecommunications ; Operating system Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Mohammad Navid Kardani | Alireza Baghban | Mohammad Ehsan Hamzehie | Mohammad Baghban
Source: Petroleum Science and Technology (article, 2019-03-19)
Field: Chemistry — Analytical Chemistry

Phase behavior modeling of asphaltene precipitation utilizing RBF-ANN approach Precipitation of heavy hydrocarbons, particularly asphaltenes, is the reason for numerous operational and production problems in the petroleum industry. Hence, knowing the amount of asphaltene precipitation is a critical commission for petroleum engineers to overcome its problems. The aim of this study was to predict the amount of asphaltene precipitation as a function of temperature, dilution ratio, and molecular weight of different n-alkanes utilizing radial basis function artificial neural network (RBF-ANN). Additionally, this model has been compared with previous correlations, and its great accuracy was proved to predict the precipitated asphaltene. The values of R-squared and mean squared error obtained were 0.998 and 0.007, respectively. The efforts confirmed brilliant forecasting skill of RBF-ANN for the approximation of the precipitated asphaltene as a function of temperature, dilution ratio, and molecular weight of different n-alkanes. Asphaltene ; Radial basis function ; Precipitation ; Artificial neural network ; Dilution ; Mean squared error ; Phase (matter) ; Thermodynamics ; Materials science ; Computer science ; Chemistry ; Mathematics ; Statistics ; Artificial intelligence ; Organic chemistry ; Physics ; Meteorology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Han Liu | Zipeng Fu | Yipeng Li | Nazreen Farina Ahmad Sabri | Mathieu Bauchy
Source: MRS Communications (article, 2019-05-23)
Field: Materials Science — Materials Chemistry

Parameterization of empirical forcefields for glassy silica using machine learning Bayesian optimization ; Ab initio ; Materials science ; Interatomic potential ; Molecular dynamics ; Bayesian probability ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Mohsen Mesbah | Arash Fattahi | A.R. Bushroa | Ghader Faraji | Kuan Yew Wong | Wan Jefrey Basirun | Alireza Fallahpour | Bahman Nasiri‐Tabrizi
Source: Metals and Materials International (article, 2019-11-02)
Field: Materials Science — Biomaterials

Experimental and Modelling Study of Ultra-Fine Grained ZK60 Magnesium Alloy with Simultaneously Improved Strength and Ductility Processed by Parallel Tubular Channel Angular Pressing Materials science ; Ultimate tensile strength ; Ductility (Earth science) ; Microstructure ; Grain size ; Pressing ; Alloy ; Elongation ; Magnesium alloy ; Crystallite ; Composite material ; Magnesium ; Metallurgy ; Creep Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Metal and Thin Film Mechanics
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: The Journal of Physical Chemistry C (article, 2019-12-31)
Field: Chemistry — Inorganic Chemistry

Ensemble Learning of Partition Functions for the Prediction of Thermodynamic Properties of Adsorption in Metal–Organic and Covalent Organic Frameworks We develop several ensemble learning models to predict the partition function of fluids adsorbed in nanoporous materials. The partition functions so obtained allow rapid access to all thermodynamic properties of adsorption and provide an assessment of the performance of these materials over a wide range of conditions, without performing additional simulations. We examine different types of applications related to the environment and energy, including carbon capture and storage in IRMOF-1, hydrogen storage in COF-102, as well as the separation of light hydrocarbons, methane, and ethane in COF-102 and COF-108. A diversity ensemble learning approach, in which predictions obtained from neural networks with different architectures are averaged, is found to provide the most accurate predictions. Comparisons with simulation results obtained using grand-canonical Monte Carlo simulations and flat-histogram methods establish the versatility of the ensemble learning model. Most notably, the new insights brought by the ensemble learning predictions include the rapid determination of the selectivity and of the desorption free energy over a wide range of operating conditions (pressure or mole fraction of the gas feed). This, in turn, provides a means to assess rapidly the performance of nanoporous materials for separation purposes, as well as of the energetic cost to regenerate the adsorbent. Nanoporous ; Grand canonical ensemble ; Canonical ensemble ; Ensemble learning ; Adsorption ; Partition (number theory) ; Monte Carlo method ; Artificial neural network ; Materials science ; Covalent organic framework ; Metal-organic framework ; Thermodynamics ; Computer science ; Machine learning ; Chemistry ; Nanotechnology ; Porosity ; Physics ; Mathematics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Samira Ranaei | Arho Suominen | Alan L. Porter | Stephen Carley
Source: Scientometrics (article, 2019-11-02)
Field: Materials Science — Materials Chemistry

Evaluating technological emergence using text analytics: two case technologies and three approaches Abstract Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level. Latent Dirichlet allocation ; Data science ; Term (time) ; Computer science ; Topic model ; Identification (biology) ; Set (abstract data type) ; Scientometrics ; Thematic map ; Analytics ; Information retrieval ; Geography ; World Wide Web ; Cartography Machine Learning in Materials Science ; Economic and Technological Innovation ; Innovation Diffusion and Forecasting
Authors: DaeHan Ahn | Ji-Young Choi | Hee‐Chul Kim | Jeong‐Seok Cho | Kwang‐Deog Moon | Taejoon Park
Source: Sensors (article, 2019-03-31)
Field: Chemistry — Analytical Chemistry

Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks There is an increasing demand for acquiring details of food nutrients especially among those who are sensitive to food intakes and weight changes. To meet this need, we propose a new approach based on deep learning that precisely estimates the composition of carbohydrates, proteins, and fats from hyperspectral signals of foods obtained by using low-cost spectrometers. Specifically, we develop a system consisting of multiple deep neural networks for estimating food nutrients followed by detecting and discarding estimation anomalies. Our comprehensive performance evaluation demonstrates that the proposed system can maximize estimation accuracy by automatically identifying wrong estimations. As such, if consolidated with the capability of reinforcement learning, it will likely be positioned as a promising means for personalized healthcare in terms of food safety. Hyperspectral imaging ; Deep learning ; Artificial neural network ; Computer science ; Estimation ; Artificial intelligence ; Nutrient ; Deep neural networks ; Reinforcement learning ; Machine learning ; Engineering ; Biology ; Ecology ; Systems engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Nutritional Studies and Diet
Authors: Jinru Xue | Sigfredo Fuentes | Carlos Poblete-Echeverría | Claudia Gonzalez Viejo | Eden Tongson | Hejuan Du | Baofeng Su
Source: International journal of agricultural and biological engineering (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts. More recently, machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant species. These new tools make the classification of Chinese medicinal plants easier, more efficient and cost effective. This study showed comparative results between machine learning models obtained from two methods: i) a morpho-colorimetric method and ii) a visible (VIS)/Near Infrared (NIR) spectral analysis from sampled leaves of 20 different Chinese medicinal plants. Specifically, the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network (ANN) models. Results showed that the ANN model developed using the morpho-colorimetric parameters as inputs (Model A) had an accuracy of 98.3% in the classification of leaves for the 20 medicinal plants studied. In the case of the model based on spectral data from leaves (Model B), the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5% accuracy for the classification of all medicinal plants used. Model A has the advantage of being cost effective, requiring only a normal document scanner as measuring instrument. This method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless scanners. Model B combines the fast, non-destructive advantages of VIS/NIR spectroscopy, which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll, anthocyanins and others that are related active compounds from the medicinal plants. Keywords: ampelography, computer vision, artificial neural networks, pattern recognition, Chinese medicinal plants DOI: 10.25165/j.ijabe.20191202.4637 Citation: Xue J R, Fuentes S, Poblete-Echeverria C, Viejo C G, Tongson E, Du H J, et al. Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible/near infrared spectroscopy. Int J Agric & Biol Eng, 2019; 12(2): 123–131. Morpho ; Artificial intelligence ; Pattern recognition (psychology) ; Identification (biology) ; Artificial neural network ; Medicinal plants ; Computer science ; Fractal dimension ; Near-infrared spectroscopy ; Mathematics ; Biological system ; Remote sensing ; Machine learning ; Fractal ; Botany ; Biology ; Physics ; Geography ; Optics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Wenru Liu | Yong Shen | Na Li | Jun Mei | Jing Xie
Source: Journal of Food Quality (article, 2019-09-02)
Field: Materials Science — Biomaterials

Application of Gelatin Incorporated with Red Pitaya Peel Methanol Extract as Edible Coating for Quality Enhancement of Crayfish (<i>Procambarus clarkii</i>) during Refrigerated Storage China is one of the largest producers of red pitaya in the world and responsible for disposal of the huge amount of peel generated as a waste. The objective of this research was to evaluate the effect of the addition of red pitaya peel extract (RPPE, 1.0%, 2.0%, or 3.0% (w/v)) and 0.1% ε -polylysine ( ε -PL) to a fish gelatin edible coating on the preservation of deshelled crayfish ( Procambarus clarkii ) during refrigerated storage. The physicochemical and water migration of the samples were determined during 8-day storage. Deshelled crayfish packaged in edible coatings exhibited significantly (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math>) lower values for total volatile basic nitrogen (TVB-N), K value maintenance, and free amino acids (FAAs). This study shows that application of an edible coating incorporated with RPPE and ε -PL is an effective strategy in retarding the quality deterioration in deshelled crayfish during storage. Procambarus clarkii ; Gelatin ; Crayfish ; Food science ; Coating ; Chemistry ; Fishery ; Biology ; Biochemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Botanical Research and Applications
Authors: Samia Boudergua | Mebarka Alloui | Salah Belaıdı | Muneerah Mogren Al Mogren | U. A. Abd Ellatif Ibrahim | M. Hochlaf
Source: Journal of Molecular Structure (article, 2019-04-11)
Field: Chemistry — Organic Chemistry

QSAR Modeling and Drug-Likeness Screening for Antioxidant Activity of Benzofuran Derivatives Chemistry ; Benzofuran ; Quantitative structure–activity relationship ; Lipophilicity ; HOMO/LUMO ; Partition coefficient ; Molecular descriptor ; Linear regression ; Polarizability ; Computational chemistry ; Hydration energy ; Octanol ; Molecule ; Furan ; Stereochemistry ; Organic chemistry ; Statistics ; Mathematics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Synthesis and biological activity