jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: AVS MadhuLatha | N Sojana | N Mounika | G Priyanka | A Venkatesh | JN Suresh Kumar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-08-30)
Field: Materials Science — Polymers and Plastics

Design and optimization of clotrimazole emulgel by using various polymers Emulgel is one of the recent technologies in Novel Drug Delivery system (NDDS) used for dual controlled release of emulsion and gel for topical use. The stability of emulsion is increased, when it is incorporated into gel. Clotrimazole is a broad spectrum anti - fungal agent, also a hydrophobic drug. It is difficult to formulate as topical delivery. So emulgel is one of the best approaches to incorporate hydrophobic drugs. The purpose of the present study was to develop and optimized the Clotrimazole emulgel using polymers like Carbopol 934 and HPMC K4M at different concentrations. The Preformulation studies were conducted by using Fourier Transform Infrared (FTIR) studies, the prepared emulgels were evaluated in terms of physical appearance, measurement of pH, viscosity, spreadability, drug content and in vitro diffusion studies<em>. In vitro</em> drug release studies were performed up to 8hrs. Among all formulations F6 shows better releasing property. So F6 was proved as the best formulation among all. F6 containing HPMC K4M polymer in low concentration showed better releasing property when compared to formulations containing Carbopol 934. So it was suggested that the Clotrimazole emulgel have potential advantages when compared to other topical drug delivery systems for incorporating hydrophobic drugs and posses better activity and stability. Clotrimazole ; Computer science ; Microbiology ; Biology Synthesis and properties of polymers
Authors: Elena Smirnova
Source: E3S Web of Conferences (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Problems of ecology and ensuring environmental safety in relation to toxic “Krasny Bor” dump site In the article the author raises the problem of ecology and ensuring environmental safety in relation to toxic “Krasny Bor” landfill. Comprehensive analysis of the existing system shortcomings to storage toxic industrial waste has been given. The principal threats of a dangerous object to the environment being in a critical state have been discussed. Reliable and high-quality environmental monitoring in the landfill is still absent. At all stages of the life cycle of the “Krasny Bor”, bureaucratic confusion impedes the development of environmentally oriented investment activities for the reclamation of toxic pits. The main elements of the project structure to remediate the “Krasny Bor” have been considered. It has been shown that the project must include all acceptable technological solutions related to landfill disposal and that it is necessary to focus on environmental protection and environmental safety of the area. In the framework of current situation at the disposal site, it does not seem impossible to solve the issues of ensuring environmental safety in the direction of moving towards sustainable and balanced development of the North-Westregion. Environmental planning ; Confusion ; Environmental quality ; Land reclamation ; Principal (computer security) ; Relation (database) ; Environmental safety ; Industrial ecology ; Business ; Engineering ; Sustainability ; Environmental resource management ; Risk analysis (engineering) ; Environmental science ; Ecology ; Computer science ; Computer security Radioactive element chemistry and processing ; Engineering and Environmental Studies ; Mining and Gasification Technologies
Authors: Yanzan Han | Jimeng Zhang
Source: Symmetry (article, 2020-01-07)
Field: Materials Science — Materials Chemistry

Recognition Algorithm of Transient Overvoltage Characteristic Based on Symmetrical Components Estimation The recognition of transient overvoltage characteristics is the premise of disturbance compensation of the transient overvoltage. Based on that, the recognition algorithm of transient overvoltage characteristics based on symmetrical components estimation was proposed. The generation mechanism of the transient overvoltage in gas insulated switchgear (GIS) was analyzed. Then, the transient overvoltage was measured via the capacitive sensor method. The three-phase voltage of ultra-high voltage grid was asymmetrical when the transient overvoltage appeared. At present, the asymmetrical three-phase voltage was decomposed into the superposition of a symmetrical positive-sequence component, a negative-sequence component, and a zero-sequence component via the symmetrical components estimation to build the superposition model. The model was decomposed via the trigonometric identity and the modified neural network of the least mean square learning rule was used to estimate the parameter vector of the characteristic quantity of the transient overvoltage in real time. The feasibility of the proposed algorithm was verified via comparing the simulation of the proposed algorithm and the algorithm based on dp transformation. The experimental results show that the proposed algorithm has the advantages of a small operand, high detection precision, and fast action. Overvoltage ; Transient (computer programming) ; Superposition principle ; Algorithm ; Switchgear ; Computer science ; Sequence (biology) ; Voltage ; Topology (electrical circuits) ; Control theory (sociology) ; Mathematics ; Engineering ; Artificial intelligence ; Electrical engineering ; Mathematical analysis High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power System Reliability and Maintenance
Authors: Yuxin Li | Wenhui Yang | Rongzhi Dong | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2020-10-30)
Field: Materials Science — Materials Chemistry

MLatticeABC: Generic Lattice Constant Prediction of Crystal Materials using Machine Learning Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average $R^2$ of 0.82. Other models tailored for special materials family of a fixed form such as ABX3 perovskites can achieve much higher performance due to the homogeneity of the structures. However, these models trained with small datasets are usually not applicable to generic lattice parameter prediction of materials with diverse compositions. Herein, we report MLatticeABC, a random forest machine learning model with a new descriptor set for lattice unit cell edge length ($a,b,c$) prediction which achieves an R2 score of 0.979 for lattice parameter $a$ of cubic crystals and significant performance improvement for other crystal systems as well. Source code and trained models can be freely accessed at https://github.com/usccolumbia/MLatticeABC Lattice constant ; Lattice (music) ; Crystal structure ; Homogeneity (statistics) ; Artificial neural network ; Artificial intelligence ; Computer science ; Crystal (programming language) ; Lattice plane ; Machine learning ; Crystal structure prediction ; Support vector machine ; Source code ; Algorithm ; Materials science ; Statistical physics ; Crystallography ; Optics ; Physics ; Reciprocal lattice ; Diffraction ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Mojtaba Haghighatlari | Jie Li | Farnaz Heidar‐Zadeh | Yuchen Liu | Xingyi Guan | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2020-02-29)
Field: Materials Science — Materials Chemistry

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with the chemically motivated descriptors and the size and type of data sets needed for molecular property prediction. Using Nuclear Magnetic Resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data is abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning algorithms drawn from deep learning, random forests, or kernel methods. Machine learning ; Artificial intelligence ; Computer science ; Property (philosophy) ; Feature (linguistics) ; Chemical space ; Perspective (graphical) ; Representation (politics) ; Kernel (algebra) ; Focus (optics) ; Algorithm ; Feature learning ; Kernel method ; Random forest ; Support vector machine ; Mathematics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Katsuhiko Ishiguro | Kenta Oono | Kohei Hayashi
Source: arXiv (Cornell University) (preprint, 2020-06-12)
Field: Materials Science — Materials Chemistry

Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules. Compared with other deep networks, however, the current performance of a GNN is limited owing to the "curse of depth." Inspired by long-established feature engineering in the field of chemistry, we expanded an atom representation using Weisfeiler-Lehman (WL) embedding, which is designed to capture local atomic patterns dominating the chemical properties of a molecule. In terms of representability, we show WL embedding can replace the first two layers of ReLU GNN -- a normal embedding and a hidden GNN layer -- with a smaller weight norm. We then demonstrate that WL embedding consistently improves the empirical performance over multiple GNN architectures and several molecular graph datasets. Embedding ; Graph ; Computer science ; Artificial neural network ; Graph embedding ; Representation (politics) ; Deep learning ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: M. Ross Kunz | Adam Yonge | Zongtang Fang | Andrew J. Medford | Denis Constales | Gregory S. Yablonsky | Rebecca Fushimi
Source: arXiv (Cornell University) (preprint, 2020-11-17)
Field: Materials Science — Materials Chemistry

Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With physical and mechanistic complexity of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood mechanism driving the reaction. As oxygen accumulated on the catalyst, a transition in the mechanism was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data. Transient (computer programming) ; Mechanism (biology) ; Set (abstract data type) ; Work (physics) ; Computer science ; Kinetic energy ; Biological system ; Chemical kinetics ; Elementary reaction ; Reaction mechanism ; Measure (data warehouse) ; Kinetics ; Catalysis ; Chemistry ; Thermodynamics ; Data mining ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Vladimir Ankudinov
Source: Mathematical Methods in the Applied Sciences (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Approximation of correlation functions in phase‐field crystal model by machine learning approach Two different phase‐field crystal (PFC) free excess energy expansions have been analyzed with respect to the order of a truncation term and to the accuracy of a pair correlation functions fitting. The coefficients of the correlation function in PFC model was found for the aluminum crystal based on the molecular dynamics data. A machine learning (ML) approach has been utilized to construct a neural network (NN) for the PFC approximation of correlation functions. The effective iteratomic potentials of the embedded atom model were used as an input data for NN training. Obtained NN predicts the coefficients of the correlation functions with the potentials data as input. Predictions were studied and verified with respect to the properties of NN and parameters of ML. Mathematics ; Truncation (statistics) ; Correlation ; Field (mathematics) ; Artificial neural network ; Correlation function (quantum field theory) ; Correlation coefficient ; Phase (matter) ; Crystal (programming language) ; Function (biology) ; Statistical physics ; Applied mathematics ; Machine learning ; Statistics ; Physics ; Computer science ; Quantum mechanics ; Pure mathematics ; Geometry Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Elizabeth J. Kautz | Wufei Ma | Saumyadeep Jana | Arun Devaraj | Vineet V. Joshi | Bülent Yener | D. Lewis
Source: Materials Characterization (preprint, 2020-05-16)
Field: Materials Science — Materials Chemistry

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction Artificial intelligence ; Convolutional neural network ; Computer science ; Thresholding ; Pattern recognition (psychology) ; Artificial neural network ; Segmentation ; Cluster analysis ; Image (mathematics) ; Machine learning Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (article, 2020-12-14)
Field: Materials Science — Materials Chemistry

Quantum machine learning in chemical compound space Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The virtual exploration of this space for the design and discovery of novel molecules and materials exhibiting desirable properties is therefore generally prohibitive for all but the smallest sub-sets and simplest properties, and typically relies heavily on access to substantial allocations on modern high-performance computing hardware. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data generated using quantum mechanics based methods and (ii) model architectures inspired by quantum mechanics. Such Quantum based Machine Learning (QML) approaches combine the advantages of a first principles view on matter, i.e.~reflecting properly the underlying physics which guarantees universality and transferability of models across all of CCS, with the numerical efficiency of statistical surrogate models. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the rigour and reliability of a physics based understanding of trends and relationships throughout CCS. Rigour ; Chemical space ; Computer science ; Universality (dynamical systems) ; Quantum ; Transferability ; Theoretical computer science ; Computational science ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Quantum mechanics ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Miaolei Zhou | Chen Zhang | Yewei Yu | Shouchun Wang
Source: Acta Physica Polonica A (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator Based on Dynamic Fuzzy Neural Network Magnetic shape memory alloy is a new class of smart metallic materials, which has excellent characteristics of huge strain and fast response. These characteristics make magnetic shape memory alloy-based actuator a potential alternative to replace traditional actuators in the high-precision positioning applications. However, the magnetic shape memory alloy-based actuator has not found its way into micro positioning field due to the obvious hysteresis behavior. In this paper, we present the prototype of the magnetic shape memory alloy-based actuator, and analyze the complex hysteresis nonlinearity between the input signal and output displacement. Then, dynamic fuzzy neural network is first utilized to construct hysteresis model for the magnetic shape memory alloy-based actuator. Dynamic fuzzy neural network is a fuzzy-logic based neural network system, which has the capability of approximating nonlinear mapping and self-adjustment. Experimental results confirm the effectiveness of the proposed hysteresis model. Shape-memory alloy ; Hysteresis ; Artificial neural network ; Actuator ; Fuzzy logic ; Materials science ; Computer science ; Alloy ; Control theory (sociology) ; Artificial intelligence ; Condensed matter physics ; Physics ; Control (management) ; Composite material Shape Memory Alloy Transformations ; Laser and Thermal Forming Techniques ; Advanced Sensor and Control Systems
Authors: Wei Pan | Rui Xu | Yewei Yu | Chen Zhang | Miaolei Zhou
Source: Acta Physica Polonica A (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator via Pi-Sigma Neural Network with Backlash-Like Operator Magnetic shape memory alloy materials have a bright prospect in micro-nano intelligent actuators, however, the hysteresis in these materials is prevalent and damages the positioning precision of these actuators. In this paper, a Pi-Sigma neural network model with a modified backlash-like operator is developed to capture the dynamic hysteresis of the magnetic shape memory alloy material actuator. The modified backlash-like operator is designed for the multi-value-mapping and asymmetrical hysteresis of the magnetic shape memory alloy material actuator. The rate-dependent hysteresis of the magnetic shape memory alloy material actuator is described via the Pi-Sigma neural network model. Experimental results show that the effectiveness of the proposed model outperforms the Krasnosel'skii-Pokrovskii model. Backlash ; Hysteresis ; Sigma ; Shape-memory alloy ; Actuator ; Operator (biology) ; Artificial neural network ; Control theory (sociology) ; Pi ; Materials science ; Computer science ; Physics ; Condensed matter physics ; Artificial intelligence ; Mathematics ; Control (management) ; Geometry ; Quantum mechanics ; Chemistry Shape Memory Alloy Transformations ; Neural Networks and Applications ; Piezoelectric Actuators and Control
Authors: Mingjian Wen | Samuel M. Blau | Evan Walter Clark Spotte‐Smith | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2020-09-22)
Field: Materials Science — Materials Chemistry

BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future. Homolysis ; Heterolysis ; Chemistry ; Molecule ; Bond-dissociation energy ; Computer science ; Dissociation (chemistry) ; Computational chemistry ; Artificial intelligence ; Biological system ; Radical ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering