jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yasuharu Okamoto
Source: ChemRxiv (preprint, 2019-03-18)
Field: Chemistry — Organic Chemistry

Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System &lt;p&gt;To comply with the Kigali amendment to the Montreal Protocol in 2016, it becomes an urgent matter to develop new refrigerants with low global warming potential with simultaneously meeting conventional requirements of cooling performance, safety, and non-destructive to the ozone layer. Because each requirement links to different chemical property, proper control of various chemical properties is necessary to achieve the requirements. However, it seems to be extremely difficult to satisfy all the requirements simultaneously due to the tradeoffs among the properties. For this reason, we need to correctly recognize how these chemical properties behave when the composition of molecule is changed. Here we have done in silico screening that combines quantum chemical calculation, machine learning, and database search, where 10,163 molecules were exhaustively investigated within the properly imposed constraints, then we have found several candidates for new refrigerants. It should be noted that the synthesis of refrigerants is more difficult than that of ordinary organic molecules because glassware cannot be used for the synthesis of fluorine-containing molecules that most refrigerants apply. This makes in silico screening a more useful approach in the design of refrigerants. &lt;/p&gt; Refrigerant ; Quantum chemical ; Global-warming potential ; Montreal Protocol ; Computer science ; In silico ; Molecule ; Process engineering ; Biochemical engineering ; Ozone layer ; Chemistry ; Environmental science ; Ozone ; Organic chemistry ; Engineering ; Thermodynamics ; Physics ; Greenhouse gas Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Atmospheric Ozone and Climate
Authors: Mads Koerstz | Anders S. Christensen | Kurt V. Mikkelsen | Mogens Brøndsted Nielsen | Jan H. Jensen
Source: ChemRxiv (preprint, 2019-04-24)
Field: Materials Science — Materials Chemistry

High Throughput Virtual Screening of 230 Billion Molecular Solar Heat Battery Candidates &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e. the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density func- tional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent com- pound, which should correspond to a half-life of about 10 days - 4 months. This is considerably longer than the 3-39 hours (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: 1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and 2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to &gt;0.5 kJ/g by combining simple substituents. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;&lt;br&gt;&lt;/div&gt; Energy density ; Reactivity (psychology) ; Molecule ; Density functional theory ; Thermal energy storage ; Energy storage ; Chemistry ; Battery (electricity) ; Thermal energy ; Materials science ; Thermodynamics ; Physics ; Computational chemistry ; Engineering physics ; Organic chemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Mengfei Yuan
Source: OhioLink ETD Center (Ohio Library and Information Network) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications The design framework for complex materials property and processing models within the Integrated Computational Material Engineering (ICME) is often hindered by the expensive computational cost.The ultimate goal of ICME is to develop data-driven, materials-based tools for the concurrent optimization of material systems while, improving the deployment of innovative materials in real-world products.Reduced-order, fast-acting tools are essential for both bottom-up property prediction and top-down model calibrations employed for modern material design applications.Additionally, reduced-order modeling requires formal uncertainty quantification (UQ) from the processing stages all the way down to the manufacturing and component design.The goal of this thesis is to introduce a machine learning-based, reduced-order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials.This implementation was founded upon Open Citrination, an open-sourced materials informatics platform.Case studies for both the bottom-up property prediction and topdown optimization of model parameters are demonstrated within this work.The proposed reduced-order model is used to correctly approximate the plastic stress-strain curves and the texture evolution under a range of deformation conditions and strain rates specific to a material.The inverted pathway is applied to quickly calibrate the Song En Low, and soon-to-be Drs.Nick Galbincea, Denielle Ricciardi, and Supriyo Chakraborty who give me many supports and helps.Most importantly, I thank my family in China during the past 6 years.I would not be able to successfully finish my Ph.D. Program without their endless supports and encouragement.vi Property (philosophy) ; Order (exchange) ; Computer science ; Artificial intelligence ; Econometrics ; Machine learning ; Mathematics ; Economics ; Epistemology ; Philosophy Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms ; Industrial Technology and Control Systems
Authors: Alexander S. Gzyl | Anton O. Oliynyk | Lawrence A. Adutwum | Arthur Mar
Source: ChemRxiv (preprint, 2019-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation The site preferences within the structures of half-Heusler compounds have been evaluated through a machine-learning approach. A support-vector machine algorithm was applied to develop a model which was trained on 179 experimentally reported structures and 23 descriptors based solely on the chemical composition. The model gave excellent performance, with sensitivity of 93%, selectivity of 96%, and accuracy of 95%. As an illustration of data sanitization, two compounds (GdPtSb, HoPdBi) flagged by the model to have potentially incorrect site assignments were resynthesized and structurally characterized. The predictions of the correct site assignments from the machine-learning model were confirmed by single-crystal and powder X-ray diffraction analysis. These site assignments also corresponded to the lowest total energy configurations as revealed from first-principles calculations. Sensitivity (control systems) ; Support vector machine ; Selectivity ; Diffraction ; Artificial intelligence ; Computer science ; Crystal structure ; Experimental data ; Machine learning ; Algorithm ; Materials science ; Chemistry ; Crystallography ; Physics ; Mathematics ; Optics ; Engineering ; Statistics ; Electronic engineering Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Alexander S. Gzyl | Anton O. Oliynyk | Lawrence A. Adutwum | Arthur Mar
Source: ChemRxiv (preprint, 2019-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation The site preferences within the structures of half-Heusler compounds have been evaluated through a machine-learning approach. A support-vector machine algorithm was applied to develop a model which was trained on 179 experimentally reported structures and 23 descriptors based solely on the chemical composition. The model gave excellent performance, with sensitivity of 93%, selectivity of 96%, and accuracy of 95%. As an illustration of data sanitization, two compounds (GdPtSb, HoPdBi) flagged by the model to have potentially incorrect site assignments were resynthesized and structurally characterized. The predictions of the correct site assignments from the machine-learning model were confirmed by single-crystal and powder X-ray diffraction analysis. These site assignments also corresponded to the lowest total energy configurations as revealed from first-principles calculations. Sensitivity (control systems) ; Support vector machine ; Selectivity ; Diffraction ; Computer science ; Experimental data ; Artificial intelligence ; Crystal structure ; Machine learning ; Materials science ; Algorithm ; Chemistry ; Crystallography ; Physics ; Optics ; Mathematics ; Engineering ; Statistics ; Electronic engineering Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Uxía Rivero | Oliver T. Unke | Markus Meuwly | Stefan Willitsch
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-07-30)
Field: Chemistry — Spectroscopy

Reactive atomistic simulations of Diels-Alder reactions: The importance of molecular rotations The Diels-Alder reaction between 2,3-dibromo-1,3-butadiene and maleic anhydride has been studied by means of multisurface adiabatic reactive molecular dynamics and the PhysNet neural network architecture. This system is used as a prototype to explore the concertedness, synchronicity, and possible ways of promotion of Diels-Alder reactions. Analysis of the minimum dynamic path indicates that rotational energy is crucial (∼65%) to drive the system toward the transition state in addition to collision energy (∼20%). Comparison with the reaction of butadiene and maleic anhydride shows that the presence of bromine substituents in the diene accentuates the importance of rotational excitation to promote the reaction. At the high total energies at which reactive events are recorded, the reaction is found to be direct and mostly synchronous. Diels–Alder reaction ; Alder ; Molecular dynamics ; Computational chemistry ; Chemistry ; Organic chemistry ; Catalysis ; Biology ; Ecology Molecular spectroscopy and chirality ; Various Chemistry Research Topics ; Catalysis and Oxidation Reactions
Authors: Gayatri Viswanathan | Anton O. Oliynyk | Erin Antono | Julia Ling | Bryce Meredig | Jakoah Brgoch
Source: ChemRxiv (preprint, 2019-01-14)
Field: Materials Science — Materials Chemistry

Single Crystal Automated Refinement (SCAR): A Data-Driven Method for Solving Inorganic Structures Single crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure solution and refinement is not always straightforward. Methods to simplify and rationalize the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure solution. In this work, we propose a new method that uses single crystal data to solve the crystal structures of inorganic, extended solids called “Single Crystal Automated Refinement (SCAR).” The approach was developed using data mining and machine-learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to solve two newly synthesized and previously unreported phases, ZrAu0.5Os0.5 and Nd4Mn2AuGe4. The structure solutions are found to be comparable with manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thusly verified to be a fast and reliable assistant in solving even complex single crystal diffraction data for extended inorganic solids. Crystal (programming language) ; Crystal structure ; Algorithm ; Computer science ; Crystal structure prediction ; Set (abstract data type) ; Crystallography ; Diffraction ; Single crystal ; Mixing (physics) ; Chemistry ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Mohammad Mehdi Ghahremanpour
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2019-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Alexandria: A General Drude Polarizable Force Field with Spherical Charge Density Molecular-mechanical (MM) force fields are mathematical functions that map the geometry of a molecule to its associated energy. MM force fields have been extensively used for an atomistic view into the dynamic and thermodynamics of large molecular systems in their condensed phase. Nevertheless, the grand challenge in force field development—which remains to be addressed—is to predict­­­­ properties of materials with different chemistries and in all their physical phases. Force fields are, in principle, derived through supervised machine learning methods. Therefore, the first step toward more accurate force fields is to provide high-quality reference data from which the force fields can learn. Thus, we benchmarked quantum-mechanical methods—at different levels of theory—in predicting of molecular energetics and electrostatic properties. As the result, the Alexandria library was released as an open access database of molecular properties. The second step is to use potential functions describing interactions between molecules accurately. For this, we incorporated electronic polarization and charge penetration effects into the Alexandria force field. The Drude model was used for the explicit inclusion of electronic polarization. The distribution of the atomic charges was described by either a 1s-Gaussian or an ns-Slater density function to account for charge penetration effects. Moreover, the 12-6 Lennard-Jones (LJ) potential function, commonly used in force fields, was replaced by the Wang-Buckingham (WBK) function to describe the interaction of two particles at very short distances. In contrast to the 12-6 LJ function, the WBK function is well behaved at short distances because it has a finite limit as the distance between two particles approaches zero. The third step is free and open source software (FOSS) for systematic optimization of the built-in force field parameters. For this, we developed the Alexandria chemistry toolkit that is currently part of the GROMACS software package. With these three steps, the Alexandria force field was developed for alkali halides and for organic compounds consisting of (H, C, N, O, S, P) and halogens (F, Cl, Br, I). We demonstrated that the Alexandria force field described alkali halides in gas, liquid, and solid phases with an overall performance better than the benchmarked reference force fields. We also showed that the Alexandria force field predicted the electrostatics of isolated molecules and molecular complexes in agreement with the density functional theory at the B3LYP/aug-cc-pVTZ level of theory. Force field (fiction) ; Polarizability ; Classical mechanics ; Physics ; Field (mathematics) ; Force density ; Charge (physics) ; Molecule ; Quantum mechanics ; Mathematics Various Chemistry Research Topics ; Advanced Physical and Chemical Molecular Interactions
Authors: Diana Pebriani Daulay | Andasuryani | Khandra Fahmy | Melvi Viyona
Source: IOP Conference Series Earth and Environmental Science (article, 2019-09-01)
Field: Chemistry — Analytical Chemistry

Prediction Carbohydrate Content of Sweet Passion Fruit Seeds (140 Days After Flowering) Using NIR Spectra with Artificial Neural Network Abstract This study purposed to develop estimation methods using NIR spectroscopy in determining carbohydrate content of sweet passion fruit seeds at the level maturity at 140 days after flowering and developing an NIR calibration model using an ANN (Neural Network) chemo metric technique. The research method used several stages, labeling the material preparation stage, the measurement stage using NIR, the chemical analysis stage and the data processing stage with artificial neural networks. At the reflectance input the highest R-Total value which the number of neurons in the hidden layer is 7 (R-Total is 0.870) and R-SNV pretreatment as the input of the highest R-Total value is found in the number of neurons with hidden layers which is 9 (R- Total is 0.949). Based on these results the development model is suitable as a predictive value, this can be seen from the regression value approaching 1. Passion fruit ; Artificial neural network ; Content (measure theory) ; Metric (unit) ; Stage (stratigraphy) ; Artificial intelligence ; Maturity (psychological) ; Mathematics ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Food science ; Computer science ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Food composition and properties
Authors: Samantha Rudinsky | Yu Yuan | Francis B. Lavoie | Raynald Gauvin | Ryan Gosselin | Nadi Braidy | Nicolas Piché | Mike Marsh
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Using Deep Learning to Deconvolute Complex Spectra for Hyperspectral Imaging Applications Journal Article Using Deep Learning to Deconvolute Complex Spectra for Hyperspectral Imaging Applications Get access Samantha Rudinsky, Samantha Rudinsky Department of Materials Engineering, McGill University. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Yu Yuan, Yu Yuan Department of Materials Engineering, McGill University. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Francis B Lavoie, Francis B Lavoie Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada Search for other works by this author on: Oxford Academic Google Scholar Raynald Gauvin, Raynald Gauvin Department of Materials Engineering, McGill University. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Ryan Gosselin, Ryan Gosselin Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada Search for other works by this author on: Oxford Academic Google Scholar Nadi Braidy, Nadi Braidy Department of Chemical Engineering and Biotechnological Engineering, Université de Sherbrooke. Sherbrooke, Canada Search for other works by this author on: Oxford Academic Google Scholar Nicolas Piché, Nicolas Piché Object Research Systems. Montreal, Canada Search for other works by this author on: Oxford Academic Google Scholar Mike Marsh Mike Marsh Object Research Systems. Denver, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 178–179, https://doi.org/10.1017/S1431927619001624 Published: 01 August 2019 Library science ; Media studies ; History ; Art history ; Sociology ; Computer science Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Si An Tang
Source: DR-NTU (Nanyang Technological University) (article, 2019-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A step towards automated TEM and SEM characterisation using deep learning : nano-tetrahedrons Convolutional neural networks (CNNs) have attracted huge amount of attentions since the emergence of deep learning, because of their ability to learn and adapt features directly from the input data, and obtain accurate classification. Although CNNs have resulted in a variety of advances in fields particular for image recognition, microscopic images such as TEM and SEM images in materials science are essentially different problem compared to other applications of deep learning. In addition, limited research studies exist to direct the design of CNN architectures for such microscopic images, and it is unclear that whether CNN can be used for this application. In this work, we have investigated the design of a CNN model for images of common nanostructures, in particular, tetrahedron. We have shown that CNN is capable of classifying tetrahedron images out of similar shapes, which is cube and octahedron. Through the project, the important and commonly tuned parameters, such as batch size, number of convolutional layers, pooling layers, activation layers were exploited and discussed with the aim of getting an optimised model. Additionally, we have concluded the optimal number of iterations or epochs to train a CNN model for the application. Apart from that, we have proven that a dropout layer is necessary at the end of the model. We have also concluded that RMSprop is a better choice of optimiser compared to Stochastic Gradient Descend (SGD). This work suggested a CNN model for the classification of tetrahedrons, which serves as an extremely crucial starting point of realisation of automated characterisation in TEM and SEM equipment in the near future. Nanotechnology ; Nano- ; Materials science ; Artificial intelligence ; Computer science Electron and X-Ray Spectroscopy Techniques
Authors: Devin Adams
Source: ScholarsArchive  (Brigham Young University) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

An Atomistic Approach for the Survey of Dislocation-Grain Boundary Interactions in FCC Nickel It is well known that grain boundaries (GBs) have a strong influence on mechanical properties of polycrystalline materials. Not as well-known is how different GBs interact with dislocations to influence dislocation movement. This work presents a molecular dynamics study of 33 different FCC Ni bicrystals subjected to mechanical loading to induce incident dislocation-GB interactions. The resulting simulations are analyzed to determine properties of the interaction that affect the likelihood of transmission of the dislocation through the GB in an effort to better inform mesoscale models of dislocation movement within polycrystals. It is found that the ability to predict the slip system of a transmitted dislocation using common geometric criteria is confirmed. Furthermore, machine learning processes are implemented to find that geometric properties, such as the minimum potential residual burgers vector and the disorientation between the two grains, are stronger indicators of whether or not a dislocation would transmit than the other properties such as the resolved shear stress. Grain boundary ; Dislocation ; Nickel ; Materials science ; Grain boundary strengthening ; Statistical physics ; Condensed matter physics ; Metallurgy ; Crystallography ; Microstructure ; Physics ; Chemistry ; Composite material Microstructure and mechanical properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels