jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nile S. Abularrage | Brian J. Levandowski | Ronald T. Raines
Source: International Journal of Molecular Sciences (article, 2020-05-31)
Field: Chemistry — Organic Chemistry

Synthesis and Diels–Alder Reactivity of 4-Fluoro-4-Methyl-4H-Pyrazoles 4<i>H</i>-Pyrazoles are emerging scaffolds for "click" chemistry. Late-stage fluorination with Selectfluor<sup>®</sup> is found to provide a reliable route to 4-fluoro-4-methyl-4<i>H</i>-pyrazoles. 4-Fluoro-4-methyl-3,5-diphenyl-4<i>H</i>-pyrazole (MFP) manifested 7-fold lower Diels-Alder reactivity than did 4,4-difluoro-3,5-diphenyl-4<i>H</i>-pyrazole (DFP), but higher stability in the presence of biological nucleophiles. Calculations indicate that a large decrease in the hyperconjugative antiaromaticity in MFP relative to DFP does not lead to a large loss in Diels-Alder reactivity because the ground-state structure of MFP avoids hyperconjugative antiaromaticity by distorting into an envelope-like conformation like that in the Diels-Alder transition state. This predistortion enhances the reactivity of MFP and offsets the decrease in reactivity from the diminished hyperconjugative antiaromaticity. Antiaromaticity ; Reactivity (psychology) ; Chemistry ; Pyrazole ; Diels–Alder reaction ; Nucleophile ; Computational chemistry ; Medicinal chemistry ; Organic chemistry ; Molecule ; Aromaticity ; Catalysis Click Chemistry and Applications ; Fluorine in Organic Chemistry ; Chemical Synthesis and Analysis
Authors: Firoozeh Danafar
Source: Cellulose Chemistry and Technology (article, 2020-03-04)
Field: Materials Science — Biomaterials

RECENT DEVELOPMENT AND CHALLENGES IN SYNTHESIS OF CELLULOSIC NANOSTRUCTURES AND THEIR APPLICATION IN DEVELOPING PAPER-BASED ENERGY DEVICES Cellulose can be extracted from different natural sources, including plants, marine animals, or microorganisms. Nanocellulose (NC) can be then produced from it, in the form of either crystal or fiber, depending on the source of cellulose and process conditions. In the last decades, the interest in the application of NC as a building block in functional composites has significantly increased. Such a trend will keep growing due to the exceptional properties and sustainability of NC. Paper energy devices have been developed for various applications, especially those related to harvesting and saving energy. This article first briefly reviews the fundamentals related to the NC and its synthesis, and then describes thoroughly the functionalities provided by paper-based energy devices. Although these functionalities bring a great opportunity to develop new value-added products, there are several challenges in the way of commercialization of paper-based electronic and electrical devices. These challenges are summarized to open up avenues of further research, in addition to providing novel ideas for developing such devices. Commercialization ; Cellulosic ethanol ; Nanotechnology ; Nanocellulose ; Cellulose ; Sustainability ; Biochemical engineering ; Process engineering ; Engineering ; Materials science ; Business ; Chemical engineering ; Ecology Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications
Authors: Kjell Jorner | Tore Brinck | Per‐Ola Norrby | David Buttar
Source: ChemRxiv (preprint, 2020-08-05)
Field: Materials Science — Materials Chemistry

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal/mol for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints. Computer science ; Chemoselectivity ; Machine learning ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Tanzila Sahar | Huma Munir | Zahida Zia | Nageen Nageen | Naila Rafiq | Nusrat Shafiq | Sobia Aleem | Sadia Aslam
Source: FRONTIERS IN CHEMICAL SCIENCES (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

Ecofriendly Green Synthesis of Iron Oxide Nanoparticles Using citrus sinensis Green protocols being eco-friendly and cost effective approach are most widely used for the production of iron oxide nanoparticles using plant-mediated extract of Citrus Sinensis, moreoverbiosynthesized iron oxide (FeO) nanoparticles shows better antibacterial activity.Green synthesis of nanoparticles has been broadly studied from the past few years because of their different features and potential applications in various fields. The successful biosynthesis of iron oxide nanoparticles was confirmed and characterized using UV-Visible spectroscopy, Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR) analysis and Zeta sizer. Antibacterial effect of biologically produced iron oxide nanoparticles was tested against Gram-negative bacteria (Escherichia coli) and Gram-positive bacteria (Macrococus). These results exhibited that iron oxide nanoparticles have high antibacterial potential as these nanoparticles showed significant zone of inhibition against bacteria strains. The proposed green synthesis of iron oxide nanoparticles (NPs) from Citrus Sinensis can be strongly recommended as a potential method for industrial application. Zeta potential ; Nanoparticle ; Iron oxide nanoparticles ; Iron oxide ; Fourier transform infrared spectroscopy ; Antibacterial activity ; Nuclear chemistry ; Bacteria ; Oxide ; Chemistry ; Scanning electron microscope ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Biology Nanoparticles: synthesis and applications
Authors: Yalin Wang | Jiandong Wu | Tao Han | Kiruba S. Haran | Yi Yin
Source: High Voltage (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network Abstract Form‐wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment, such as high temperature and low pressure. This study proposes a forewarning method for insulation condition monitoring of form‐wound windings based on partial discharge (PD) and deep learning neural network. Three PD features are extracted from the PD profile, which provides information about physics‐of‐failure and reflects the degree of insulation degradation. An algorithm fusion extracted from auto‐encoder and long short‐term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi‐time‐step prediction through historical data to provide forewarning. An electrical and thermal accelerated ageing test is performed on the form‐wound windings at 0.2 atm to simulate working environment of electric aircraft. The proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time‐series prediction methods, indicating the advantages of the proposed method. Moreover, an on‐line hardware setup using a deep learning processor is recommended to implement the forewarning method. The proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring. Electromagnetic coil ; Partial discharge ; Reliability (semiconductor) ; Artificial neural network ; Deep learning ; Reliability engineering ; Propulsion ; Computer science ; Automotive engineering ; Encoder ; Artificial intelligence ; Engineering ; Voltage ; Electrical engineering ; Aerospace engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Advanced Battery Technologies Research
Authors: Filipp Nikitin | Olexandr Isayev | Vadim Strijov
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

DRACON: disconnected graph neural network for atom mapping in chemical reactions Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP). We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs. Here we demonstrate that our approach can successfully predict centres of reaction and atoms of the main product. A set of experiments using the USPTO dataset demonstrates excellent performance and interpretability of the proposed model. Implicitly learned latent vector representation of chemical reactions strongly correlates with the class of the chemical reaction. Reactions with similar templates group together in the latent vector space. Graph ; Artificial neural network ; Atom (system on chip) ; Chemistry ; Computer science ; Combinatorics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Aditya R. Thawani | Ryan‐Rhys Griffiths | Arian R. Jamasb | Anthony Bourached | Penelope Jones | William McCorkindale | Alexander A. Aldrick | Alpha A. Lee
Source: ChemRxiv (preprint, 2020-07-06)
Field: Materials Science — Materials Chemistry

The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry The space of synthesizable molecules is greater than $10^{60}$, meaning only a vanishingly small fraction of these molecules have ever been realized in the lab. In order to prioritize which regions of this space to explore next, synthetic chemists need access to accurate molecular property predictions. While great advances in molecular machine learning have been made, there is a dearth of benchmarks featuring properties that are useful for the synthetic chemist. Focussing directly on the needs of the synthetic chemist, we introduce the Photoswitch Dataset, a new benchmark for molecular machine learning where improvements in model performance can be immediately observed in the throughput of promising molecules synthesized in the lab. Photoswitches are a versatile class of molecule for medical and renewable energy applications where a molecule's efficacy is governed by its electronic transition wavelengths. We demonstrate superior performance in predicting these wavelengths compared to both time-dependent density functional theory (TD-DFT), the incumbent first principles quantum mechanical approach, as well as a panel of human experts. Our baseline models are currently being deployed in the lab as part of the decision process for candidate synthesis. It is our hope that this benchmark can drive real discoveries in photoswitch chemistry and that future benchmarks can be introduced to pivot learning algorithm development to benefit more expansive areas of synthetic chemistry. Photoswitch ; Chemical space ; Computer science ; Benchmark (surveying) ; Expansive ; Artificial intelligence ; Machine learning ; Nanotechnology ; Chemistry ; Drug discovery ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Lagnajit Pattanaik | John Ingraham | Colin A. Grambow | William H. Green
Source: ChemRxiv (preprint, 2020-05-15)
Field: Materials Science — Materials Chemistry

Generating Transition States of Isomerization Reactions with Deep Learning Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions. Isomerization ; Transition state ; Stochastic matrix ; Artificial neural network ; Computer science ; Workflow ; Scope (computer science) ; Quantum ; Graph ; Product (mathematics) ; Algorithm ; Matrix (chemical analysis) ; Chemistry ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Catalysis ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jun Zhang | Yao-Kun Lei | Zhen Zhang | Junhan Chang | Maodong Li | Xu Han | Lijiang Yang | Yi Yang | Yi Qin Gao
Source: The Journal of Physical Chemistry B (article, 2020-07-14)
Field: Materials Science — Materials Chemistry

A Perspective on Deep Learning for Molecular Modeling and Simulations Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics, and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models, instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we outlook promising directions which may help address the existing issues in the current framework of deep molecular modeling. Deep learning ; Computer science ; Artificial intelligence ; Perspective (graphical) ; Focus (optics) ; Software deployment ; Cognitive science ; Data science ; Psychology ; Physics ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Oleh Yasniy | Iryna Didych | Sergiy Fedak | Yuri Lapusta
Source: Procedia Structural Integrity (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods There was studied a jump-like deformation of AMg6 aluminum alloy at static tensile test in the soft mode of loading. The experimental methods of jump-like deformation study of this alloy are often complicated, expensive and time-consuming. Therefore, it is more effective to model the stress-strain diagram numerically. One of the promising ways to predict the stress-strain diagram of AMg6 material is based on the application of machine learning methods, in particular by neural networks, boosted trees, support-vector machines, and k - nearest neighbors. It was discovered that the method of neural networks gives the least prediction error equal to 6.9%. The errors of the methods of boosted trees, support-vector machines and k - nearest neighbours are 9.1, 7.7, and 9.4% in test sets, respectively. Artificial neural network ; Deformation (meteorology) ; Jump ; Alloy ; Materials science ; Support vector machine ; Diagram ; Stress (linguistics) ; Tensile testing ; Structural engineering ; Computer science ; Ultimate tensile strength ; Artificial intelligence ; Algorithm ; Composite material ; Engineering ; Physics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Material Properties and Applications
Authors: Derek P. Metcalf | Andy Jiang | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: ChemRxiv (preprint, 2020-09-21)
Field: Materials Science — Materials Chemistry

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom-typing or relatively expensive quantum mechanical methods to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities, but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential.&lt;br&gt; Artificial neural network ; Charge (physics) ; Overfitting ; Molecular dynamics ; Quantum ; Atomic charge ; Statistical physics ; Heuristic ; Computer science ; Atom (system on chip) ; Electron ; Charge density ; Physics ; Algorithm ; Quantum mechanics ; Biological system ; Artificial intelligence ; Molecule ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gabriella Graziano
Source: Nature Reviews Chemistry (review, 2020-09-29)
Field: Materials Science — Materials Chemistry

Deep learning chemistry ab initio Wave function ; Quantum Monte Carlo ; Ab initio ; Quantum chemistry ; Monte Carlo method ; Electron ; Statistical physics ; Ab initio quantum chemistry methods ; Physics ; Computational chemistry ; Molecule ; Quantum mechanics ; Computer science ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kokin Nakajin | Takuya Minami | Masaaki Kawata | Toshio Fujita | Katsumi Murofushi | Hiroshi Uchida | Kazuhiro Omori | Yoshishige Okuno
Source: MRS Advances (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Prediction of physical properties of thermosetting resin by using machine learning and structural formulas of raw materials Thermosetting polymer ; Raw material ; Materials science ; Mixing (physics) ; Curing (chemistry) ; Biological system ; Random forest ; Computer science ; Composite material ; Machine learning ; Organic chemistry ; Chemistry ; Physics ; Biology Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Advancements in Photolithography Techniques
Authors: George S. Lamb | Brooks Paige
Source: arXiv (Cornell University) (preprint, 2020-11-25)
Field: Materials Science — Materials Chemistry

Bayesian Graph Neural Networks for Molecular Property Prediction Graph neural networks for molecular property prediction are frequently underspecified by data and fail to generalise to new scaffolds at test time. A potential solution is Bayesian learning, which can capture our uncertainty in the model parameters. This study benchmarks a set of Bayesian methods applied to a directed MPNN, using the QM9 regression dataset. We find that capturing uncertainty in both readout and message passing parameters yields enhanced predictive accuracy, calibration, and performance on a downstream molecular search task. Computer science ; Bayesian probability ; Property (philosophy) ; Bayesian network ; Artificial neural network ; Machine learning ; Graph ; Regression ; Artificial intelligence ; Set (abstract data type) ; Calibration ; Test set ; Data mining ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rafael M. Mamede | Bruno Simões de-Almeida | Mengyao Chen | Qingyou Zhang | João Aires‐de‐Sousa
Source: Journal of Chemical Information and Modeling (article, 2020-12-22)
Field: Chemistry — Spectroscopy

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher. Optical rotation ; Chloroform ; Dichloromethane ; Rotation (mathematics) ; Methanol ; Molecule ; Specific rotation ; Sign (mathematics) ; Center (category theory) ; Chemistry ; Materials science ; Biological system ; Computer science ; Artificial intelligence ; Mathematics ; Stereochemistry ; Organic chemistry ; Crystallography ; Mathematical analysis ; Solvent Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Various Chemistry Research Topics