jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryoji Katsube | Kei Terayama | Ryo Tamura | Yoshitaro Nose
Source: ACS Materials Letters (article, 2020-04-24)
Field: Materials Science — Materials Chemistry

Experimental Establishment of Phase Diagrams Guided by Uncertainty Sampling: An Application to the Deposition of Zn–Sn–P Films by Molecular Beam Epitaxy The construction of phase diagrams is an essential step in the development of materials and devices, but this process requires considerable time and the efforts of well-established researchers. Recently, a machine learning method based on uncertainty sampling, phase diagram construction (PDC), was developed in order to minimize the number of required experiments. In the present study, we, for the first time, demonstrate PDC-assisted experimental determination of an uninvestigated phase diagram for the deposition of Zn–Sn–P films by molecular beam epitaxy (MBE). Interestingly, without any background knowledge of materials science, an undetected phase in the initial dataset was detected after only 4 cycles of PDC-assisted experiments, and the phase diagram was sufficiently refined after only 11 cycles. Based on the results and thermodynamic discussions, we confirm the effectiveness of PDC on the construction of phase diagrams for MBE process. The demonstration in this study shows the machine-learning method, PDC, will accelerate discovery and development of materials; furthermore, we discuss how to use PDC more effectively, considering the objectives involved in the construction of phase diagrams. Phase diagram ; Molecular beam epitaxy ; Deposition (geology) ; Phase (matter) ; Process (computing) ; Diagram ; Sampling (signal processing) ; Materials science ; Computer science ; Beam (structure) ; Epitaxy ; Nanotechnology ; Process engineering ; Optics ; Chemistry ; Physics ; Engineering ; Geology ; Telecommunications Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Jenna A. Bilbrey | Joseph P. Heindel | Malachi Schram | Pradipta Bandyopadhyay | Sotiris S. Xantheas | Sutanay Choudhury
Source: The Journal of Chemical Physics (article, 2020-07-13)
Field: Materials Science — Materials Chemistry

A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters We describe a method for the post-hoc interpretation of a neural network (NN) trained on the global and local minima of neutral water clusters. We use the structures recently reported in a newly published database containing over 5 × 10<sup>6</sup> unique water cluster networks (H<sub>2</sub>O)<sub>N</sub> of size N = 3-30. The structural properties were first characterized using chemical descriptors derived from graph theory, identifying important trends in topology, connectivity, and polygon structure of the networks associated with the various minima. The code to generate the molecular graphs and compute the descriptors is available at https://github.com/exalearn/molecular-graph-descriptors, and the graphs are available alongside the original database at https://sites.uw.edu/wdbase/. A Continuous-Filter Convolutional Neural Network (CF-CNN) was trained on a subset of 500 000 networks to predict the potential energy, yielding a mean absolute error of 0.002 ± 0.002 kcal/mol per water molecule. Clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN protocol accurately predicts energies of networks for both smaller and larger sizes than those used during training. The graph-theoretical descriptors were further employed to interpret the predictive power of the CF-CNN. Topological measures, such as the Wiener index, the average shortest path length, and the similarity index, suggested that all networks from the test set were within the range of values as the ones from the training set. The graph analysis suggests that larger errors appear when the mean degree and the number of polygons in the cluster lie further from the mean of the training set. This indicates that the structural space, and not just the chemical space, is an important factor to consider when designing training sets, as predictive errors can result when the structural composition is sufficiently different from the bulk of those in the training set. To this end, the developed descriptors are quite effective in explaining the results of the CF-CNN (a.k.a. the "black box") model. Maxima and minima ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Artificial neural network ; Graph ; Test set ; Shortest path problem ; Artificial intelligence ; Algorithm ; Mathematics ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yu Mao | Ningning Dong | Lei Wang | Xin Chen | Hongqiang Wang | Zixin Wang | I. M. Kislyakov | Jun Wang
Source: Nanomaterials (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

Machine Learning Analysis of Raman Spectra of MoS2 Defects introduced during the growth process greatly affect the device performance of two-dimensional (2D) materials. Here we demonstrate the applicability of employing machine-learning-based analysis to distinguish the monolayer continuous film and defect areas of molybdenum disulfide (MoS<sub>2</sub>) using position-dependent information extracted from its Raman spectra. The random forest method can analyze multiple Raman features to identify samples, making up for the problem of not being able to effectively identify by using just one certain variable with high recognition accuracy. Even some dispersed nucleation site defects can be predicted, which would commonly be ignored under an optical microscope because of the lower optical contrast. The successful application for classification and analysis highlights the potential for implementing machine learning to tap the depth of classical methods in 2D materials research. Raman spectroscopy ; Molybdenum disulfide ; Nucleation ; Materials science ; Monolayer ; Contrast (vision) ; Random forest ; Computer science ; Position (finance) ; Process (computing) ; Artificial intelligence ; Nanotechnology ; Biological system ; Pattern recognition (psychology) ; Optics ; Chemistry ; Physics ; Composite material 2D Materials and Applications ; Machine Learning and ELM ; Graphene research and applications
Authors: Indrajit Srivastava | Muhammad S. Khan | Ketan Dighe | Maha Alafeef | Zhen Wang | Tuseeta Banerjee | Tanmay Ghonge | Lauren M. Grove | Rashid Bashir | Dipanjan Pan
Source: Small Methods (article, 2020-04-20)
Field: Materials Science — Biomaterials

On‐Chip Electrical Monitoring of Real‐Time “Soft” and “Hard” Protein Corona Formation on Carbon Nanoparticles Abstract When nanoparticles encounter a biological fluid, proteins, lipids, and other biomolecules adsorb on the nanoscale surface consequently leading to the evolution of a protein shell or “corona.” The corona formed is dynamic in nature and depends on the “synthetic identity” of the NPs, ultimately affecting their biological response. In this paper, an integrated microfluidic platform coupled with an electrical resistance measurement setup is developed to monitor and investigate the real‐time formation of a biomolecular corona of carbon nanoparticles. “Soft” and “hard” corona formation stages are effectively discriminated based on their nanoscale surface chemistries when combined with a time‐frequency tool known as wavelet transform and machine‐learning techniques. Additionally, the corona and its composition are studied using different techniques such as dynamic light scattering, nanoparticle tracking analysis, zeta potential, excitation–emission profiles, 1D sodium dodecyl polyacrylamide gel electrophoresis and subsequently, liquid chromatography‐mass spectrometry analysis. The dynamic setup can eventually be used as a valuable tool for screening of any nanoparticles formulations with distinct surface chemistries for the purpose of reduced protein adsorption/weaker protein corona formation and consequently enhance the success of targeted drug delivery. Corona (planetary geology) ; Nanoparticle ; Biomolecule ; Corona discharge ; Nanotechnology ; Adsorption ; Microfluidics ; Chemistry ; Dynamic light scattering ; Nanoscopic scale ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Electrode ; Organic chemistry Nanoparticle-Based Drug Delivery ; Nanopore and Nanochannel Transport Studies ; Electrostatics and Colloid Interactions
Authors: Changwoo Do | Wei‐Ren Chen | Sangkeun Lee
Source: MRS Advances (article, 2020-02-24)
Field: Materials Science — Materials Chemistry

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods Scattering ; Computer science ; Matrix (chemical analysis) ; Machine learning ; Artificial intelligence ; Materials science ; Optics ; Physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Shuhui Guan | Kexiang Zhang | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Chemical Physics (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

Stability and anion diffusion kinetics of Yttria-stabilized zirconia resolved from machine learning global potential energy surface exploration Yttria-stabilized zirconia (YSZ) is an important material with wide industrial applications particularly for its good conductivity in oxygen anion transportation. The conductivity is known to be sensitive to Y concentration: 8 mol. % YSZ (8YSZ) achieves the best performance, which, however, degrades remarkably under ∼1000 °C working conditions. Here, using the recently developed SSW-NN method, stochastic surface walking global optimization based on global neural network potential (G-NN), we establish the first ternary Y-Zr-O G-NN potential by fitting 28 803 first principles dataset screened from more than 10<sup>7</sup> global potential energy surface (PES) data and explore exhaustively the global PES of YSZ at different Y concentrations. Rich information on the thermodynamics and the anion diffusion kinetics of YSZ is, thus, gleaned, which helps resolve the long-standing puzzles on the stability and conductivity of the 8YSZ. We demonstrate that (i) 8YSZ is the cubic phase YSZ with the lowest possible Y concentrations. It is thermodynamically unstable, tending to segregate into the monoclinic phase of 6.7YSZ and the cubic phase of 20YSZ. (ii) The O anion diffusion in YSZ is mediated by O vacancy sites and moves along the ⟨100⟩ direction. In 8YSZ and 10YSZ, despite different Y concentrations, their anion diffusion barriers are similar, ∼ 1 eV, but in 8YSZ, the O diffusion distance is much longer due to the lack of O vacancy aggregation along the ⟨112⟩ direction. Our results illustrate the power of G-NN potential in solving challenging problems in material science, especially those requiring a deep knowledge on the complex PES. Yttria-stabilized zirconia ; Diffusion ; Vacancy defect ; Conductivity ; Materials science ; Phase (matter) ; Ion ; Cubic zirconia ; Ternary operation ; Monoclinic crystal system ; Thermodynamics ; Chemical physics ; Chemistry ; Physical chemistry ; Crystallography ; Computer science ; Ceramic ; Composite material ; Physics ; Crystal structure Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Xinhui Yu | Weidong Guo | Nan Wu | Liang Zou | Meng Lei
Source: Infrared Physics & Technology (article, 2020-01-14)
Field: Chemistry — Analytical Chemistry

Rapid discrimination of coal geographical origin via near-infrared spectroscopy combined with machine learning algorithms Linear discriminant analysis ; Principal component analysis ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Multivariate statistics ; Coal ; Identification (biology) ; Algorithm ; Machine learning ; Data mining ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Asrin Bahmani | Hamid Tanzadehpanah | Neda Hosseinpour Moghadam | Massoud Saidijam
Source: Molecular Diversity (article, 2020-04-16)
Field: Chemistry — Organic Chemistry

Introducing a pyrazolopyrimidine as a multi-tyrosine kinase inhibitor, using multi-QSAR and docking methods Quantitative structure–activity relationship ; Docking (animal) ; Regorafenib ; Computational biology ; Chemistry ; Drug discovery ; Receptor tyrosine kinase ; Binding site ; Stereochemistry ; Biology ; Kinase ; Biochemistry ; Cancer ; Colorectal cancer ; Genetics ; Medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; HER2/EGFR in Cancer Research
Authors: Guoxiang Hu | Victor Fung | Xiahan Sang | Raymond R. Unocic | Panchapakesan Ganesh
Source: npj Computational Materials (article, 2020-05-05)
Field: Materials Science — Materials Chemistry

Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides Abstract Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions. However, the whole family of synthesizable, reconstructed edges remains poorly studied. Here, we develop a computational approach integrating ensemble-generation, force-relaxation, and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. Using MoS 2 as a model system, we screened hundreds of edge-reconstruction to discover over 160 reconstructed edges to be more stable than the conventional ones. More excitingly, we discovered nine new synthesizable reconstructred edges with record thermodynamic stability, in addition to successfully reproducing three recently synthesized edges. We also find our predicted reconstructed edges to have multi-functional properties—they show near optimal hydrogen evolution activity over the conventional edges, ideal for catalyzing hydrogen-evolution reaction (HER) and also exhibit half-metallicity with a broad variation in magnetic moments, making them uniquely suitable for nanospintronic applications. Our work reveals the existence of a wide family of synthesizable, reconstructed edges in 2D TMDCs and opens a new materials-by-design paradigm of ‘intrinsic’ edge engineering multifunctionality in 2D materials. Zigzag ; Enhanced Data Rates for GSM Evolution ; Materials science ; Transition metal ; Density functional theory ; Stability (learning theory) ; Nanotechnology ; Computer science ; Computational chemistry ; Chemistry ; Mathematics ; Geometry ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Robin Singh | Anu Agarwal | Brian Anthony
Source: Scientific Reports (article, 2020-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of optical meta-structures with applications to beam engineering using deep learning Nanophotonics is a rapidly emerging field in which complex on-chip components are required to manipulate light waves. The design space of on-chip nanophotonic components, such as an optical meta surface which uses sub-wavelength meta-atoms, is often a high dimensional one. As such conventional optimization methods fail to capture the global optimum within the feasible search space. In this manuscript, we explore a Machine Learning (ML)-based method for the inverse design of the meta-optical structure. We present a data-driven approach for modeling a grating meta-structure which performs photonic beam engineering. On-chip planar photonic waveguide-based beam engineering offers the potential to efficiently manipulate photons to create excitation beams (Gaussian, focused and collimated) for lab-on-chip applications of Infrared, Raman and fluorescence spectroscopic analysis. Inverse modeling predicts meta surface design parameters based on a desired electromagnetic field outcome. Starting with the desired diffraction beam profile, we apply an inverse model to evaluate the optimal design parameters of the meta surface. Parameters such as the repetition period (in 2D axis), height and size of scatterers are calculated using a feedforward deep neural network (DNN) and convolutional neural network (CNN) architecture. A qualitative analysis of the trained neural network, working in tandem with the forward model, predicts the diffraction profile with a correlation coefficient as high as 0.996. The developed model allows us to rapidly estimate the desired design parameters, in contrast to conventional (gradient descent based or genetic optimization) time-intensive optimization approaches. Computer science ; Deep learning ; Artificial intelligence ; Data science Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Sneha Nayak | Shyama Prasad Sajankila | Louella Concepta Goveas | Vaman C. Rao | Srinivas Mutalik | Banerjee Shreya
Source: SN Applied Sciences (article, 2020-03-13)
Field: Materials Science — Materials Chemistry

Two fold increase in synthesis of gold nanoparticles assisted by proteins and phenolic compounds in Pongamia seed cake extract: response surface methodology approach Response surface methodology ; Nanoparticle ; Factorial experiment ; Central composite design ; Pongamia ; Colloidal gold ; Chemistry ; Aqueous solution ; Nuclear chemistry ; Materials science ; Chemical engineering ; Chromatography ; Nanotechnology ; Organic chemistry ; Mathematics ; Catalysis Nanoparticles: synthesis and applications
Authors: A. Anvari
Source: International Journal of Aerospace Engineering (article, 2020-05-23)
Field: Materials Science — Materials Chemistry

The Influence of CNT Structural Parameters on the Properties of CNT and CNT-Reinforced Epoxy The main objective of this research is to review and investigate the influence of carbon nanotube structure on the properties of carbon nanotube and carbon nanotube-reinforced epoxy. Carbon nanotube and carbon nanotube-reinforced epoxy are currently being frequently used in many applications such as aerospace, automotive, and electronics industries due to their excellent properties such as high tensile strength, high Young’s modulus, and electrical and thermal conductivity. In this study, the obstacles to apply carbon nanotubes as fibers within the matrix have been introduced and discussed. Additionally, the epoxy properties and application have been cited, and failure mechanisms of carbon nanotube-reinforced epoxy and geometries of carbon nanotubes have been reviewed. Furthermore, with using experimental data and applying an analytical method, the effect of carbon nanotube diameter on interlaminar shear stress within the carbon nanotube-reinforced epoxy interface has been evaluated. Additionally, the effect of temperature variation on the value of interlaminar shear stress within the single-walled carbon nanotube-reinforced epoxy interface has been discussed. Finally, the influence of the number of hexagons in the unit cell on the Young’s modulus of zigzag and armchair single-walled carbon nanotubes has been evaluated. Carbon nanotube ; Materials science ; Epoxy ; Composite material ; Carbon nanotube metal matrix composites ; Carbon nanotube actuators ; Mechanical properties of carbon nanotubes ; Zigzag ; Ultimate tensile strength ; Nanotube ; Modulus Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Smart Materials for Construction
Authors: Doruk Aksoy | Rémi Dingreville | Douglas E. Spearot
Source: Acta Materialia (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Spectrum of embrittling potencies and relation to properties of symmetric-tilt grain boundaries Grain boundary ; Materials science ; Embrittlement ; Grain boundary strengthening ; Metastability ; Tilt (camera) ; Crystallography ; Condensed matter physics ; Thermodynamics ; Metallurgy ; Geometry ; Microstructure ; Physics ; Mathematics Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Chun‐hsien Chen | Chun‐Chi Wang | Po‐Yun Ko | Yen‐Ling Chen
Source: Journal of Food and Drug Analysis (review, 2020-12-02)
Field: Materials Science — Materials Chemistry

Nanomaterial-based adsorbents and optical sensors for illicit drug analysis The abuse of illicit drugs has been prevalent in recent years and is associated with crime and public health issues. To strengthen public security and fortify public health services with respect to the increasing severity of drug abuse, academic and government institutes have been devoted to constructing relative analytical methods for illicit drugs. To date, the development of sensors has been greatly emphasized due to their features of high sensitivity, prompt detection and flexible manipulation; thus, sensors can serve as alternatives to conventional sophisticated instruments. Recently, the use of nanomaterials has inspired the development of a series of innovative sample pretreatment and detection strategies in the field of analytical chemistry. Herein, this review elaborated the application of nanomaterials in analytical methods, including sample pretreatments, colorimetric sensors and fluorescent sensors. The utilization of nanomaterials in the analytical field provides novel perspectives for the development of detection platforms and facilitates the monitoring of illicit drugs in diverse complex matrices. Illicit drug ; Drugs of abuse ; Government (linguistics) ; Public security ; Nanotechnology ; Sample (material) ; Computer science ; Risk analysis (engineering) ; Drug ; Business ; Chemistry ; Materials science ; Medicine ; Political science ; Pharmacology ; Chromatography ; Public relations Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques