jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhounuo Tong | Leyun Wang | Gaoming Zhu | Xiaoqin Zeng
Source: Metallurgical and Materials Transactions A (article, 2019-09-30)
Field: Materials Science — Biomaterials

Predicting Twin Nucleation in a Polycrystalline Mg Alloy Using Machine Learning Methods Nucleation ; Crystallite ; Electron backscatter diffraction ; Materials science ; Crystal twinning ; Artificial intelligence ; Ultimate tensile strength ; Machine learning ; Crystallography ; Metallurgy ; Microstructure ; Computer science ; Physics ; Chemistry ; Thermodynamics Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; MXene and MAX Phase Materials
Authors: Arindam Paul | Al’ona Furmanchuk | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Molecular Informatics (article, 2019-09-10)
Field: Materials Science — Materials Chemistry

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed.<sup>1</sup> The proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes. Photovoltaic system ; Property (philosophy) ; Computer science ; Organic molecules ; Cheminformatics ; Organic solar cell ; Materials science ; Data mining ; Artificial intelligence ; Biological system ; Molecule ; Chemistry ; Computational chemistry ; Engineering ; Organic chemistry ; Biology ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Jian-E Dong | Ye Wang | Zhi‐Tian Zuo | Yuanzhong Wang
Source: Chemometrics and Intelligent Laboratory Systems (article, 2019-12-23)
Field: Chemistry — Analytical Chemistry

Deep learning for geographical discrimination of Panax notoginseng with directly near-infrared spectra image Panax notoginseng ; Artificial intelligence ; Convolutional neural network ; Residual ; Pattern recognition (psychology) ; Set (abstract data type) ; Computer science ; Deep learning ; Mathematics ; Medicine Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Traditional Chinese Medicine Studies
Authors: Shigeaki Morita
Source: Analytical Sciences (article, 2019-11-14)
Field: Chemistry — Analytical Chemistry

Chemometrics and Related Fields in Python Chemometrics ; Python (programming language) ; Principal component analysis ; Multivariate statistics ; Computer science ; Chemistry ; Artificial intelligence ; Data mining ; Machine learning ; Programming language Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Markus Schöberl | Nicholas Zabaras | Phaedon‐Stelios Koutsourelakis
Source: The Journal of Chemical Physics (article, 2019-01-14)
Field: Materials Science — Materials Chemistry

Predictive collective variable discovery with deep Bayesian models Extending spatio-temporal scale limitations of models for complex atomistic systems considered in biochemistry and materials science necessitates the development of enhanced sampling methods. The potential acceleration in exploring the configurational space by enhanced sampling methods depends on the choice of collective variables (CVs). In this work, we formulate the discovery of CVs as a Bayesian inference problem and consider the CVs as hidden generators of the full-atomistic trajectory. The ability to generate samples of the fine-scale atomistic configurations using limited training data allows us to compute estimates of observables as well as our probabilistic confidence on them. The methodology is based on emerging methodological advances in machine learning and variational inference. The discovered CVs are related to physicochemical properties which are essential for understanding mechanisms especially in unexplored complex systems. We provide a quantitative assessment of the CVs in terms of their predictive ability for alanine dipeptide (ALA-2) and ALA-15 peptide. Inference ; Computer science ; Machine learning ; Artificial intelligence ; Bayesian probability ; Sampling (signal processing) ; Variable (mathematics) ; Complex system ; Bayesian inference ; Trajectory ; Bayesian network ; Probabilistic logic ; Mathematics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Yasong Chen | Fuying Wang | Lipan Dong | Zheng Li | Li Chen | Xinhai He | Jixian Gong | Jianfei Zhang | Qiujin Li
Source: Polymers (article, 2019-06-02)
Field: Materials Science — Polymers and Plastics

Design and Optimization of Flexible Polypyrrole/Bacterial Cellulose Conductive Nanocomposites Using Response Surface Methodology Flexible conductive materials have greatly promoted the rapid development of intelligent and wearable textiles. This article reports the design of flexible polypyrrole/bacterial cellulose (PPy/BC) conductive nanocomposites by in situ chemical polymerization. Box-Behnken response surface methodology has been applied to optimize the process. The effects of the pyrrole amount, the molar ratio of HCl to pyrrole and polymerization time on conductivity were investigated. A flexible PPy/BC nanocomposite was obtained with an outstanding electrical conductivity as high as 7.34 S cm<sup>-1</sup>. Morphological, thermal stability and electrochemical properties of the nanocomposite were also studied. The flexible PPy/BC composite with a core-sheath structure exhibited higher thermal stability than pure cellulose, possessed a high areal capacitance of 1001.26 mF cm<sup>-2</sup> at the discharge current density of 1 mA cm<sup>-2</sup>, but its cycling stability could be further improved. The findings of this research demonstrate that the response surface methodology is one of the most effective approaches for optimizing the conditions of synthesis. It also indicates that the PPy/BC composite is a promising material for applications in intelligent and wearable textiles. Polypyrrole ; Materials science ; Nanocomposite ; Thermal stability ; Bacterial cellulose ; Composite number ; Polymerization ; Composite material ; Cellulose ; Response surface methodology ; Conductive polymer ; Electrical conductor ; In situ polymerization ; Chemical engineering ; Conductivity ; Polymer ; Chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Hyungjun Kim | Ji Young Park | Sunghwan Choi
Source: Scientific Data (article, 2019-07-03)
Field: Materials Science — Materials Chemistry

Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method A wide variety of data-driven approaches have been introduced in the field of quantum chemistry. To extend the applicable range and improve the prediction power of those approaches, highly accurate quantum chemical benchmarks that cover extremely large chemical spaces are required. Here, we report ~134 k quantum chemical calculations performed with G4MP2, the fourth generation of the G-n series in which second-order perturbation theory is employed. A single composite method calculation executes several low-level calculations to reproduce the results of high-level ab initio calculations with the aim of saving computational costs. Therefore, our database reports the results of the various methods (e.g., density functional theory, Hartree-Fock, Møller-Plesset perturbation theory, and coupled-cluster theory). Additionally, we examined the structure information of both the QM9 and the revised databases via chemical graph analysis. Our database can be applied to refine and improve the quality of data-driven quantum chemical prediction. Furthermore, we reported the raw outputs of all calculations performed in this work for other potential applications. Ab initio ; Perturbation theory (quantum mechanics) ; Quantum chemical ; Coupled cluster ; Density functional theory ; Quantum chemistry ; Computer science ; Graph theory ; Computational chemistry ; Chemistry ; Statistical physics ; Physics ; Quantum mechanics ; Molecule ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Yinhang Zhang | Mira Park | Soo‐Jin Park
Source: Scientific Reports (article, 2019-02-27)
Field: Materials Science — Materials Chemistry

Implication of thermally conductive nanodiamond-interspersed graphite nanoplatelet hybrids in thermoset composites with superior thermal management capability Novel hybrid nanofillers composed of nanodiamond-attached graphite nanoplatelets (ND@GNPs) were designed and employed to toughen the epoxy (EP) matrix for fabricating superior thermal conductive and physically robust thermoset nanocomposites for electronics and auto industries. The hybrid nanofiller was covalently bonded by 4,4'-diphenylmethane diisocyanate and it provided distinct enhancement in thermal conductivity and dynamic storage modulus of the EP/ND@GNPs nanocomposites attributing to the unique nanostructure of ND@GNPs that can form strong interfacial interaction with EP matrix, thus restrict the EP molecular motions. The EP/ND@GNPs20 presented a thermal conductivity of 2.48 W · m<sup>-1</sup> · K<sup>-1</sup> and dynamic storage modulus of 5.6 GPa. The presence of ND particles not only can enhance heat transfer efficiency but also improve the interfacial interaction between ND and EP matrix, which can directly affect physical properties of the EP composites. Thermosetting polymer ; Nanodiamond ; Materials science ; Composite material ; Thermal management of electronic devices and systems ; Graphite ; Electrical conductor ; Thermal ; Diamond Thermal properties of materials ; Fiber-reinforced polymer composites ; Graphene research and applications
Authors: Yanxin Wang | Jing Yan | Zhou Yang | Tingliang Liu | Yiming Zhao | Junyi Li
Source: Energies (article, 2019-12-09)
Field: Materials Science — Materials Chemistry

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data. Switchgear ; Convolutional neural network ; Partial discharge ; Pattern recognition (psychology) ; Computer science ; Visualization ; Artificial intelligence ; Artificial neural network ; Deep learning ; Feature engineering ; Feature (linguistics) ; Data mining ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Elevator Systems and Control
Authors: He Ming Yao | Min Li | Lijun Jiang
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies In this paper, we utilize the deep learning approach for the subwavelength imaging in far-field, which is realized by the near-field resonant metalens at microwave frequencies. The resonating metalens consisting of split-ring resonators (SRRs) are equipped with the strong magnetic coupling ability and can convert evanescent waves into propagating waves using the localized resonant modes. The propagating waves in the far-field are utilized as the input of a trained deep convolutional neural network (CNN) to realize the imaging. The training data for establishing the deep CNN are obtained by the EM simulation tool. Besides, the white Gaussian noise is added into the training data to simulate the interference in the real application scenario. The proposed CNN composes of three pairs of convolutional and activation layers with one additional fully connected layer to realize the recognition, i.e., the imaging process. The feasibility of utilizing the trained deep CNN for imaging is validated by numerical benchmarks. Distinguished from the subwavelength imaging methods, the spatial response and Green's function need not be measured and evaluated in the proposed method. Convolutional neural network ; Microwave imaging ; Deep learning ; Interference (communication) ; Near and far field ; Computer science ; Microwave ; Physics ; Optics ; Field (mathematics) ; Noise (video) ; Resonator ; Artificial intelligence ; Telecommunications ; Image (mathematics) Metamaterials and Metasurfaces Applications ; Antenna Design and Optimization ; Microwave Engineering and Waveguides
Authors: Rachel C. Irlam | Mark C. Parkin | Dermot Brabazon | Matthew S. Beardah | Michael J. O’Donnell | Leon Barron
Source: Talanta (article, 2019-05-11)
Field: Chemistry — Spectroscopy

Improved determination of femtogram-level organic explosives in multiple matrices using dual-sorbent solid phase extraction and liquid chromatography-high resolution accurate mass spectrometry Identification and trace quantification of multiple explosives residues, their precursors and transformation products in complex samples remains very challenging. For solid phase extraction (SPE) and liquid chromatography-high resolution accurate mass spectrometry-based methods (LC-HRMS), interferences from co-extracted matrix components can significantly affect recovery during extraction and/or detector signal. The aim of this work was to develop a new, improved and more generalisable extraction approach to trace explosives analysis in a range of matrices using dual-sorbent SPE with LC-HRMS. Recoveries of 44 organic explosives from model solutions were optimised and compared for seven different sorbents (Oasis HLB, HyperSep Retain PEP and Isolute ENV+, HyperSep SAX, HyperSep NH2, Strata Alumina-N and Bond Elut CN). On average, Oasis HLB and Isolute ENV+ yielded the best recoveries (>80%). For three sorbents, mean recoveries remained ≤1%, which made them potentially suitable for matrix removal when used in series with more analyte-selective sorbents. To evaluate matrix effects, a range of aqueous (river- and wastewater), solid (soil), dirty (road sign swabs), oily (oven hood swabs) and biological (dried blood) samples were selected based on complexity and forensic relevance. With the exception of river water, matrix effects were lowest using dual-sorbent SPE, with little/no compromise in recovery. Quantitative method performance assessment is presented for 14 selected explosives, representative of different classes, molecular weights and volatilities, and across three different matrices (i.e. untreated wastewater, cooking oil residues and dried blood). Limits of detection improved by ∼10-fold over a single sorbent approach, enabling low fg sensitivity in many cases. Finally, application of the method to untreated wastewater enabled detection of new explosives traces for the first time, which could be used to help identify clandestine manufacture or sources of environmental toxicity. This approach offered a versatile solution to sample preparation for robust and highly sensitive detection/quantification of large numbers of explosives residues in a range of complex sample types. Sorbent ; Chemistry ; Chromatography ; Solid phase extraction ; Extraction (chemistry) ; Matrix (chemical analysis) ; Analyte ; Detection limit ; Mass spectrometry ; Adsorption ; Organic chemistry Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Jia Jin | Quan Wang
Source: Remote Sensing (article, 2019-01-20)
Field: Chemistry — Analytical Chemistry

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters. Partial least squares regression ; Hyperspectral imaging ; Computer science ; Regression ; Reflectivity ; Pattern recognition (psychology) ; Feature selection ; Linear regression ; Artificial intelligence ; Variable elimination ; Remote sensing ; Mathematics ; Biological system ; Statistics ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Ian Clark | Jacob Smith | Rachel L. Gomes | Edward Lester
Source: Journal of environmental chemical engineering (article, 2019-05-24)
Field: Materials Science — Materials Chemistry

Continuous Synthesis of Zn2Al-CO3 Layered Double Hydroxides for the Adsorption of Reactive Dyes from Water Adsorption ; Layered double hydroxides ; Calcination ; Intercalation (chemistry) ; Chemistry ; Langmuir adsorption model ; Particle size ; Molecule ; Diffusion ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Physical chemistry ; Catalysis ; Thermodynamics Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Thuan Van Tran | Duyen Thi Cam Nguyen | Hanh T.N. Le | Long Giang Bạch | Dai‐Viet N. Vo | Kwon Taek Lim | Linh Xuan Nong | Trinh Duy Nguyen
Source: Molecules (article, 2019-05-16)
Field: Chemistry — Inorganic Chemistry

Combined Minimum-Run Resolution IV and Central Composite Design for Optimized Removal of the Tetracycline Drug Over Metal–Organic Framework-Templated Porous Carbon In this study, a minimum-run resolution IV and central composite design have been developed to optimize tetracycline removal efficiency over mesoporous carbon derived from the metal-organic framework MIL-53 (Fe) as a self-sacrificial template. Firstly, minimum-run resolution IV, powered by the Design-Expert program, was used as an efficient and reliable screening study for investigating a set of seven factors, these were: tetracycline concentration (A: 5-15 mg/g), dose of mesoporous carbons (MPC) (B: 0.05-0.15 g/L), initial pH level (C: 2-10), contact time (D: 1-3 h), temperature (E: 20-40 °C), shaking speed (F: 150-250 rpm), and Na<sup>+</sup> ionic strength (G: 10-90 mM) at both low (-1) and high (+1) levels, for investigation of the data ranges. The 20-trial model was analyzed and assessed by Analysis of Variance (ANOVA) data, and diagnostic plots (e.g., the Pareto chart, and half-normal and normal probability plots). Based on minimum-run resolution IV, three factors, including tetracycline concentration (A), dose of MPC (B), and initial pH (C), were selected to carry out the optimization study using a central composite design. The proposed quadratic model was found to be statistically significant at the 95% confidence level due to a low <i>P</i>-value (<0.05), high R<sup>2</sup> (0.9078), and the AP ratio (11.4), along with an abundance of diagnostic plots (3D response surfaces, Cook's distance, Box-Cox, DFFITS, Leverage versus run, residuals versus runs, and actual versus predicted). Under response surface methodology-optimized conditions (e.g., tetracycline concentration of 1.9 mg/g, MPC dose of 0.15 g/L, and pH level of 3.9), the highest tetracycline removal efficiency via confirmation tests reached up to 98.0%-99.7%. Also, kinetic intraparticle diffusion and isotherm models were systematically studied to interpret how tetracycline molecules were absorbed on an MPC structure. In particular, the adsorption mechanisms including "electrostatic attraction" and "π-π interaction" were proposed. Central composite design ; Tetracycline ; Ionic strength ; Composite number ; Materials science ; Mathematics ; Analytical Chemistry (journal) ; Chemistry ; Statistics ; Response surface methodology ; Composite material ; Chromatography ; Aqueous solution Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal