jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikita V. Muravyev | Алла Н. Пивкина | Nobuyoshi Koga
Source: Molecules (article, 2019-06-21)
Field: Materials Science — Materials Chemistry

Critical Appraisal of Kinetic Calculation Methods Applied to Overlapping Multistep Reactions Thermal decomposition of solids often includes simultaneous occurrence of the overlapping processes with unequal contributions in a specific physical quantity variation during the overall reaction (e.g., the opposite effects of decomposition and evaporation on the caloric signal). Kinetic analysis for such reactions is not a straightforward, while the applicability of common kinetic calculation methods to the particular complex processes has to be justified. This study focused on the critical analysis of the available kinetic calculation methods applied to the mathematically simulated thermogravimetry (TG) and differential scanning calorimetry (DSC) data. Comparing the calculated kinetic parameters with true kinetic parameters (used to simulate the thermoanalytical curves), some caveats in the application of the Kissinger, isoconversional Friedman, Vyazovkin and Flynn-Wall-Ozawa methods, mathematical and kinetic deconvolution approaches and formal kinetic description were highlighted. The model-fitting approach using simultaneously TG and DSC data was found to be the most useful for the complex processes assumed in the study. Kinetic energy ; Critical appraisal ; Chemistry ; Physics ; Medicine ; Classical mechanics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Ulrika Johansson | Mona Widhe | Nancy Dekki Shalaly | Irene Linares Arregui | Linnea Nilebäck | Christos Panagiotis Tasiopoulos | Carolina Åstrand | Per‐Olof Berggren | Christian Gasser | My Hedhammar
Source: Scientific Reports (article, 2019-04-18)
Field: Materials Science — Biomaterials

Assembly of functionalized silk together with cells to obtain proliferative 3D cultures integrated in a network of ECM-like microfibers Tissues are built of cells integrated in an extracellular matrix (ECM) which provides a three-dimensional (3D) microfiber network with specific sites for cell anchorage. By genetic engineering, motifs from the ECM can be functionally fused to recombinant silk proteins. Such a silk protein, FN-silk, which harbours a motif from fibronectin, has the ability to self-assemble into networks of microfibers under physiological-like conditions. Herein we describe a method by which mammalian cells are added to the silk solution before assembly, and thereby get uniformly integrated between the formed microfibers. In the resulting 3D scaffold, the cells are highly proliferative and spread out more efficiently than when encapsulated in a hydrogel. Elongated cells containing filamentous actin and defined focal adhesion points confirm proper cell attachment to the FN-silk. The cells remain viable in culture for at least 90 days. The method is also scalable to macro-sized 3D cultures. Silk microfibers formed in a bundle with integrated cells are both strong and extendable, with mechanical properties similar to that of artery walls. The described method enables differentiation of stem cells in 3D as well as facile co-culture of several different cell types. We show that inclusion of endothelial cells leads to the formation of vessel-like structures throughout the tissue constructs. Hence, silk-assembly in presence of cells constitutes a viable option for 3D culture of cells integrated in a ECM-like network, with potential as base for engineering of functional tissue. Microfiber ; SILK ; Tissue engineering ; Extracellular matrix ; Fibronectin ; Cell biology ; Scaffold ; Cell adhesion ; Cell culture ; 3D cell culture ; Materials science ; Chemistry ; Nanotechnology ; Cell ; Biology ; Biomedical engineering ; Biochemistry Silk-based biomaterials and applications ; 3D Printing in Biomedical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Brandon Anderson | Truong-Son Hy | Risi Kondor
Source: Neural Information Processing Systems (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Cormorant: Covariant Molecular Neural Networks We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground state properties of molecules calculated by Density Functional Theory. Some of the key features of our network are that (a) each neuron explicitly corresponds to a subset of atoms; (b) the activation of each neuron is covariant to rotations, ensuring that overall the network is fully rotationally invariant. Furthermore, the non-linearity in our network is based upon tensor products and the Clebsch-Gordan decomposition, allowing the network to operate entirely in Fourier space. Cormorant significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces from conformational geometries in the MD-17 dataset, and is competitive with other methods at learning geometric, energetic, electronic, and thermodynamic properties of molecules on the GDB-9 dataset. Covariant transformation ; Invariant (physics) ; Artificial neural network ; Computer science ; Statistical physics ; Density functional theory ; Artificial intelligence ; Topology (electrical circuits) ; Biological system ; Physics ; Theoretical computer science ; Computational chemistry ; Chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: R. V. Krems
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Bayesian machine learning for quantum molecular dynamics This article discusses applications of Bayesian machine learning for quantum molecular dynamics. One particular formulation of quantum dynamics advocated here is in the form of a machine learning simulator of the Schrödinger equation. If combined with the Bayesian statistics, such a simulator allows one to obtain not only the quantum predictions but also the error bars of the dynamical results associated with uncertainties of inputs (such as the potential energy surface or non-adiabatic couplings) into the nuclear Schrödinger equation. Instead of viewing atoms as undergoing dynamics on a given potential energy surface, Bayesian machine learning allows one to formulate the problem as the Schrödinger equation with a non-parametric distribution of potential energy surfaces that becomes conditioned by the desired dynamical properties (such as the experimental measurements). Machine learning models of the Schrödinger equation solutions can identify the sensitivity of the dynamical properties to different parts of the potential surface, the collision energy, angular momentum, external field parameters and basis sets used for the calculations. This can be used to inform the design of efficient quantum dynamics calculations. Machine learning models can also be used to correlate rigorous results with approximate calculations, providing accurate interpolation of exact results. Finally, there is evidence that it is possible to build Bayesian machine learning models capable of physically extrapolating the solutions of the Schrödinger equation. This is particularly valuable as such models could complement common discovery tools to explore physical properties at Hamiltonian parameters not accessible by rigorous quantum calculations or experiments, and potentially be used to accelerate the numerical integration of the nuclear Schrödinger equation. Bayesian probability ; Quantum ; Hamiltonian (control theory) ; Quantum dynamics ; Dynamical systems theory ; Bayesian inference ; Quantum machine learning ; Molecular dynamics ; Complement (music) Machine Learning in Materials Science ; Quantum many-body systems ; Gaussian Processes and Bayesian Inference
Authors: Sirawit Pruksawan | G. Lambard | Sadaki Samitsu | Keitaro Sodeyama | Masanobu Naito
Source: Science and Technology of Advanced Materials (article, 2019-10-21)
Field: Materials Science — Materials Chemistry

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples) limits the development of chemical intuition from experimentalists, as it constrains the use of machine-learning algorithms for extracting relevant information. We tackle this issue to predict and optimize adhesive materials by combining laboratory experimental design, an active learning pipeline and Bayesian optimization. We start from an initial dataset of 32 adhesive samples that were prepared from various molecular-weight bisphenol A-based epoxy resins and polyetheramine curing agents, mixing ratios and curing temperatures, and our data-driven method allows us to propose an optimal preparation of an adhesive material with a very high adhesive joint strength measured at 35.8 ± 1.1 MPa after three active learning cycles (five proposed preparations per cycle). A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient of determination, root mean square error and mean absolute error of 0.85, 4.0 MPa and 3.0 MPa, respectively. This study demonstrates the important impact of active learning to accelerate the design and development of tailored highly functional materials from very small datasets. Adhesive ; Materials science ; Machine learning ; Computer science ; Curing (chemistry) ; Boosting (machine learning) ; Artificial intelligence ; Active learning (machine learning) ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Epoxy Resin Curing Processes
Authors: Haiping Su | Sen Lin | Shengwei Deng | Cheng Lian | Yazhuo Shang | Honglai Liu
Source: Nanoscale Advances (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the capacitance of carbon-based electric double layer capacitors by machine learning Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials. Capacitance ; Supercapacitor ; Capacitor ; Electrode ; Carbon fibers ; Materials science ; Layer (electronics) ; Computer science ; Optoelectronics ; Electrical engineering ; Composite material ; Engineering ; Chemistry ; Voltage Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Emine Baştürk | Alper Alver
Source: Journal of Environmental Management (article, 2019-07-24)
Field: Materials Science — Materials Chemistry

Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches Response surface methodology ; Artificial neural network ; Mean squared error ; Box–Behnken design ; Biological system ; Central composite design ; Pollutant ; Genetic algorithm ; Computer science ; Mathematics ; Machine learning ; Chemistry ; Statistics Ultrasound and Cavitation Phenomena ; Advanced oxidation water treatment ; Water Quality Monitoring and Analysis
Authors: Wei Yang | Ce Yang | Ziyuan Hao | Chuanqi Xie | Minzan Li
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Diagnosis of Plant Cold Damage Based on Hyperspectral Imaging and Convolutional Neural Network Cold damage is one of the disasters that cause significant loss and irreversible damage in crop production. To avoid yield loss, high-throughput phenotyping can be used to select the crop varieties with cold stress resistance. Nowadays, non-destructive spectral image analysis has become an effective way and is widely used in high-throughput phenotyping, which reflects the structural, physiological, biochemical characteristics and traits of plant structure and composition, plant growth and development processes and outcomes. This study used convolutional neural network (CNN) model to extract spectral features in the visible-near-infrared range to estimate cold damage of corn seedlings. The hyperspectral images of cold treated corn seedlings from five varieties were used as research objects in this study. The spectral range of the images was 450-885nm. Gaussian low-pass filter and the Savitzky-Golay smoothing method combined with the first-order derivative was used to do pre-processing for spectral data. For each corn variety, 3600 pixel samples obtained from the selected region of interests in each variety of corn seedlings were used for the CNN modeling. After the CNN modeling, 400 pixel samples extracted from the hyperspectral images were used as the testing set for each variety. Finally, a 10-layer knot CNN model was determined by analyzing the classification accuracy and computational efficiency. CNN detected the cold damage level of different types of corn seedlings as W22 (41.8 %), BxM (35%), B73 (25.6%), PH207 (20%), Mo17 (14%), which had high correlation with the ranking given by chemical method. The coefficient of correlation between cold damage detection results of CNN and results from chemical method is 0.8219. Therefore, it proves that spectral analysis based on CNN modeling can provide reference for detecting cold damage in corn seedlings. Hyperspectral imaging ; Convolutional neural network ; Smoothing ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Pixel ; Environmental science ; Mathematics ; Computer vision ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Reda Rabie | Taoufiq Saffaj | Ilham Bouzida | Ouadi Saidi | Issam Meftah Kadmiri | Brahim Lakssir | El Mestafa El Hadrami
Source: Chemometrics and Intelligent Laboratory Systems (article, 2019-10-24)
Field: Chemistry — Analytical Chemistry

A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy Partial least squares regression ; Mean squared error ; Soil carbon ; Algorithm ; Regression analysis ; Mathematics ; Regression ; Absolute deviation ; Artificial intelligence ; Computer science ; Machine learning ; Soil science ; Statistics ; Environmental science ; Soil water Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Water Quality Monitoring and Analysis
Authors: Yukari Katsura | Masaya Kumagai | Takushi Kodani | Mitsunori Kaneshige | Yuki Ando | Sakiko Gunji | Y. Imai | Hideyasu Ouchi | Kazuki Tobita | Kaoru Kimura | Koji Tsuda
Source: Science and Technology of Advanced Materials (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials Data mining from published papers can generate large experimental datasets that have been overlooked in computational materials informatics. We developed an open web system Starrydata2 to accelerate a comprehensive digitization of data of materials from as-reported plot images in published papers, without sample selection based on performance. By plotting results obtained from our dataset on experimental thermoelectric properties of 434 samples of rock-salt-type (PbTe-type) thermoelectric materials, we revealed differences in electronic structure of parent compounds PbTe, PbSe, PbS, and SnTe from just experimental data. We observed that the calculated Seebeck coefficients were fairly consistent with experimental data for n-type PbTe but not for p-type PbTe, indicating possible modifications in its valence-band electronic structure. We evaluated the electron relaxation time τel from 207 reported samples of n-type PbTe by combining calculations and experimental data. We found that τel is not a constant but varies by at least two orders of magnitude. Achieving long τel was suggested to be critical in increasing the thermoelectric figure of merit ZT. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Electronic structure ; Electron ; Condensed matter physics ; Seebeck coefficient ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Jinxing Chen | Ji Feng | Fan Yang | Rashed Aleisa | Qiao Zhang | Yadong Yin
Source: Angewandte Chemie International Edition (review, 2019-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Space‐Confined Seeded Growth of Cu Nanorods with Strong Surface Plasmon Resonance for Photothermal Actuation Herein, we show that copper nanostructures, if made anisotropic, can exhibit strong surface plasmon resonance comparable to that of gold and silver counterparts in the near-infrared spectrum. Further, we demonstrate that a robust confined seeded growth strategy allows the production of high-quality samples with excellent control over their size, morphology, and plasmon resonance frequency. As an example, copper nanorods (CuNRs) are successfully grown in a limited space of preformed rod-shaped polymer nanocapsules, thereby avoiding the complex nucleation kinetics involved in the conventional synthesis. The method is unique in that it enables the flexible control and fine-tuning of the aspect ratio and the plasmonic resonance. We also show the high efficiency and stability of the as-synthesized CuNRs in photothermal conversion and demonstrate their incorporation into nanocomposite polymer films that can be used as active components for constructing light-responsive actuators and microrobots. Nanorod ; Photothermal therapy ; Surface plasmon resonance ; Seeding ; Materials science ; Surface plasmon ; Resonance (particle physics) ; Nanotechnology ; Localized surface plasmon ; Photothermal effect ; Plasmon ; Optoelectronics ; Nanoparticle ; Physics ; Atomic physics Gold and Silver Nanoparticles Synthesis and Applications ; Solar-Powered Water Purification Methods ; Copper-based nanomaterials and applications
Authors: Daniil M. Polyukhov | Artem S. Poryvaev | S. А. Gromilov | Matvey V. Fedin
Source: Nano Letters (article, 2019-08-26)
Field: Chemistry — Inorganic Chemistry

Precise Measurement and Controlled Tuning of Effective Window Sizes in ZIF-8 Framework for Efficient Separation of Xylenes Metal-organic frameworks (MOFs) are the promising nanomaterials for separation of molecules with close dimensions and structures, such as various types of isomers. The efficiency of separation can be greatly enhanced if the apertures of the nanosized windows, controlling the diffusion of a particular molecule inside the cavities, are fine-tuned by external stimuli. We report the new approach for precise measurement of window sizes in ZIF-8 MOF and employ it in efficient separation of xylenes, which is of high practical importance. For this sake, we synthesized ZIF-8 with embedded stable nitroxides in the pores and applied electron paramagnetic resonance spectroscopy for in situ kinetic measurement of the diffusion of various guest molecules through the material. Slight variation of temperature within 298-333 K allowed tuning of the windows and reaching optimum conditions for separation of <i>p</i>-, <i>m</i>-, and <i>o</i>-xylenes with the efficiency up to 92-95%. The developed methodology provides deeper understanding of steric and kinetic aspects of molecular diffusion in ZIF-8 and paves the way to rational optimization of other MOF-based separation applications. Window (computing) ; Separation (statistics) ; Materials science ; Nanotechnology ; Computer science ; Process engineering ; Engineering ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Lanthanide and Transition Metal Complexes
Authors: James Dean | Michael G. Taylor | Giannis Mpourmpakis
Source: Science Advances (article, 2019-09-06)
Field: Materials Science — Materials Chemistry

Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis Metal nanoparticles have received substantial attention in the past decades for their applications in numerous areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of adsorption on nanoparticle surfaces as a function of nanoparticle size, shape, metal composition, and type of adsorbate are yet to be found. Herein, we introduce the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition, and different adsorbates by combining first principles calculations with machine learning. Our model fits a large number of data and accurately predicts adsorption trends on nanoparticles (both monometallic and alloy) that have not been previously seen. In addition to its application power, the model is simple and uses tabulated and rapidly calculated data for metals and adsorbates. We connect adsorption with stability behavior of nanoparticles, thus advancing the design of optimal nanoparticles for applications of interest, such as catalysis. Adsorption ; Nanoparticle ; Catalysis ; Metal ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Gold and Silver Nanoparticles Synthesis and Applications