jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel R. Cassar | Saulo Martiello Mastelini | Tiago Botari | Edesio Alcobaça | André C. P. L. F. de Carvalho | Edgar Dutra Zanotto
Source: arXiv (Cornell University) (preprint, 2020-09-01)
Field: Materials Science — Ceramics and Composites

Predicting thermal, mechanical, and optical properties of oxide glasses by machine learning using large datasets With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. Due to their (hidden) smooth composition-property relationships, this strategy is especially relevant for the development of new glasses. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller sets for each property investigated. Using the decision tree, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algorithms, we induced predictive models for glass transition temperature, liquidus temperature, elastic modulus, thermal expansion coefficient, refractive index, and the Abbe number. Moreover, each model was induced with the default and tuned hyperparameter values. We demonstrate that, apart from the elastic modulus (which had the smallest training dataset), the induced predictive models for the other five properties yield a comparable uncertainty to the usual data spread. However, for glasses that have extremely low or high values of these properties, the prediction uncertainty is significantly higher. Finally, as expected, glasses containing chemical elements that are poorly represented in the training set yielded higher prediction errors. The method developed here calls attention for the success and also some possible pitfalls of ML methods, and is beneficial for selecting or developing new glasses with a suitable combination of properties. The ultimate goal is to use these induced models, which were a necessary step for the inverse task of recommending the chemical composition that might yield any desired combination of these six properties. Liquidus ; Machine learning ; Thermal expansion ; Hyperparameter ; Artificial intelligence ; Computer science ; Algorithm ; Elastic modulus ; Property (philosophy) ; Decision tree ; Refractive index ; Set (abstract data type) ; Materials science Glass properties and applications ; Phase-change materials and chalcogenides
Authors: Faisal Abbas
Source: Pure and Applied Biology (article, 2020-12-30)
Field: Chemistry — Analytical Chemistry

Artificial neural network (ANN) approach for modeling of methyl orange adsorption by Syzygium cumini seed coat In current study remediation of methyl orange was studied from aqueous media using Syzygium cumini seed coat as an active material. Batch adsorption method was utilized for the purpose and parameters optimization, including pH (3, 7 & 12), initial concentration (40, 80 & 120 ppm) and contact time (40, 80 & 120 minutes). After parameters optimization these variables were taken as input layer of Artificial Neural Network (ANN) to calculate predictive model for the removal of dye. For this purpose STATISTICA 10 software was used and architecture used for model generation was 3-8-1, which includes 3 input layers, 8 hidden and 1 output layers. Experimental results shows that Syzygium cumini seed coat is an excellent adsorber for methyl orange due to different functional groups ( -OH, C=O and C-O) present on its surface as analyzed by FTIR and rough surface shown in SEM image. Predictive modeling was performed by ANN to determine adsorption capacity of the studied adsorbent for dyes. Validation of generated model was done by measuring R-square value (0.96). Results indicates Syzygium cumini can be successfully utilized for remediation of methyl orange from aqueous media and predictive modeling by ANN can predict future adsorption on this adsorbent. Coat ; Syzygium ; Orange (colour) ; Artificial neural network ; Chemistry ; Computer science ; Horticulture ; Botany ; Biology ; Artificial intelligence ; Materials science ; Composite material Spectroscopy and Chemometric Analyses
Authors: Abhishek Mall | Abhijeet Patil | Amit Sethi | Anshuman Kumar
Source: Scientific Reports (preprint, 2020-11-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response. Nanophotonics ; Computer science ; Inverse ; Genetic algorithm ; Design process ; Inverse problem ; Topology optimization ; Electronic engineering ; Optics ; Mathematics ; Physics ; Finite element method ; Engineering ; Machine learning ; Geometry ; Work in process Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Acoustic Wave Phenomena Research
Authors: Uwe Möginger | Niels Marcussen | Ole N. Jensen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-02-20)
Field: Chemistry — Spectroscopy

Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections Abstract Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub)types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n=11), clear cell renal cell carcinoma (ccRCC, n=12) and chromophobe renal cell carcinoma (ChRCC, n=5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases. Proteome ; Renal cell carcinoma ; Chromophobe cell ; Profiling (computer programming) ; Mass spectrometry imaging ; Clear cell ; Kidney cancer ; Proteomics ; Clear cell renal cell carcinoma Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Renal cell carcinoma treatment
Authors: Onur Çaylak | O. Anatole von Lilienfeld | Björn Baumeier
Source: arXiv (Cornell University) (preprint, 2020-01-29)
Field: Materials Science — Materials Chemistry

Wasserstein metric for improved QML with adjacency matrix representations We study the Wasserstein metric to measure distances between molecules represented by the atom index dependent adjacency "Coulomb" matrix, used in kernel ridge regression based supervised learning. Resulting quantum machine learning models exhibit improved training efficiency and result in smoother predictions of molecular distortions. We first demonstrate smoothness for the continuous extraction of an atom from some organic molecule. Learning curves, quantifying the decay of the atomization energy's prediction error as a function of training set size, have been obtained for tens of thousands of organic molecules drawn from the QM9 data set. In comparison to conventionally used metrics ($L_1$ and $L_2$ norm), our numerical results indicate systematic improvement in terms of learning curve off-set for random as well as sorted (by norms of row) atom indexing in Coulomb matrices. Our findings suggest that this metric corresponds to a favorable similarity measure which introduces index-invariance in any kernel based model relying on adjacency matrix representations. Adjacency matrix ; Measure (data warehouse) ; Metric (unit) ; Mathematics ; Clef ; Matrix norm ; Distance matrix ; Smoothness ; Matrix (chemical analysis) ; Adjacency list ; Kernel (algebra) ; Algorithm ; Applied mathematics ; Computer science ; Discrete mathematics ; Mathematical analysis ; Eigenvalues and eigenvectors ; Graph ; Physics ; Quantum mechanics ; Data mining ; Chemistry Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Lan Fu | Hongkai Yu | M.C. Shah | Jeff Simmons | Song Wang
Source: Electronic Imaging (article, 2020-01-15)
Field: Materials Science — Materials Chemistry

Crystallographic Symmetry for Data Augmentation in Detecting Dendrite Cores Accurately and rapidly detecting the locations of the cores of large-scale dendrites from 2D sectioned microscopic images helps quantify the microstructure of material components. This provides a critical link between the processing and properties of the material. Such a tool could be a critical part of a quality control procedure for manufacturing these components. In this paper, we propose to use Faster R-CNN, a convolutional neural network (CNN) model that considers both the detection accuracy and computational efficiency, to detect the dendrite cores with complex shapes. However, training CNN models usually requires a large number of images annotated with ground-truth locations of dendrite cores, which are usually obtained by highly laborintensive manual annotations. In this paper, we leverage the crystallographic symmetry of dendrite cores for data augmentation – the cross sections of dendrite cores show, not perfect, but near four-fold rotation symmetry and we can rotate the image around the center of dendrite cores by specified angles to construct new training data without additional manual annotations. We conduct a series of experiments and the results show the effectiveness of the Faster R-CNN method with the proposed data augmentation strategy. Particularly, we find that we can reduce the number of the manually annotated training images by 75% while still maintaining the same detection accuracy of dendrite cores. Symmetry (geometry) ; Crystallographic point group ; Dendrite (mathematics) ; Materials science ; Crystallography ; Chemistry ; Geometry ; Mathematics ; Crystal structure Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Cultural Heritage Materials Analysis
Authors: Lubna Sherin | Ayesha Sohail | Shahida Shujaat | Muhammad Mathar Bashir | Sabiqa Inam | Zareen Arshad | Anwar Ul‐Hamid
Source: Journal of Cluster Science (article, 2020-08-29)
Field: Materials Science — Materials Chemistry

Investigation of Catalytic Potential and Radical Scavenging Efficacy of Terminalia bellerica Roxb Bark Mediated Ecofriendly Silver Nanoparticles DPPH ; Silver nanoparticle ; Chemistry ; Catalysis ; Nuclear chemistry ; Scavenging ; Methyl orange ; ABTS ; Nanochemistry ; Phenol ; Antioxidant ; Nanoparticle ; Organic chemistry ; Photocatalysis ; Materials science ; Nanotechnology Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions
Authors: Rama K. Vasudevan | Maxim Ziatdinov | Lukáš Vlček | Anna N. Morozovska | Eugene А. Eliseev | Shize Yang | Yongji Gong | Pulickel M. Ajayan | Wu Zhou | Matthew F. Chisholm | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-06-17)
Field: Materials Science — Materials Chemistry

Investigating phase transitions from local crystallographic analysis based on machine learning of atomic environments Traditionally, phase transitions are explored using a combination of macroscopic functional characterization and scattering techniques, providing insight into average properties and symmetries of the lattice but local atomic level mechanisms during phase transitions generally remain unknown. Here we explore the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalogenides in the MoS2 ReS2 system from the observations of local degrees of freedom, namely atomic positions by Scanning Transmission Electron Microscopy (STEM). We employ local crystallographic analysis based on machine learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. In particular, we argue that the dependence of the average symmetry breaking distortion amplitude on global and local concentration can be used to separate local chemical and global electronic effects on transition. This approach allows exploring atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of realized and observed local stoichiometries and atomic configurations. Local symmetry ; Phase transition ; Scanning transmission electron microscopy ; Local structure ; Lattice (music) ; Symmetry breaking ; Scattering ; Chemical physics ; Condensed matter physics ; Characterization (materials science) ; Phase (matter) ; Atomic units ; Physics ; Materials science ; Transmission electron microscopy ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Volodymyr Yuzevych | Fedir Horbonos | Roman Rogalskyi | Iryna Yemchenko | Mykhailo Yasinskyi
Source: International Journal of Recent Technology and Engineering (IJRTE) (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

Determination of the Place Depressurization of Underground Pipelines in the Monitoring of Oil and Gas Enterprises Analytical correlations of mathematical model that characterizes the processes of development of corrosive cavity on the surface of underground metallic pipeline are analysed. A pipeline in the environment of moist soil with solution of electrolyte is placed.In the top of cavity a crack appears and translates the system “underground pipeline (UP) – pumping station (PS)” in the state of depressurization.A new approach is proposed for diagnosing places of underground pipeline depressurization on the basis of two types of devices: pressure sensors and non-destructive testing devices, with the help of which we measure potentials and corrosion currents in surface defects.The presence of only pressure sensors makes it possible to set the coordinates of depressurization with a large error – 20…25%. Diagnosis of the pipeline only by devices BVS-K, VPP-M (at the first stage) allows to reveal surface defects. But this information is not enough for a qualitative experiment, as shown by the qualimetric quality criterion. Taking into account the pressure data in the second stage allows to determine which of the defects is the leakage point.A test example for the distance L1 = 6 km from the pumping station of the pipeline is considered. The results of the experiment are used for the example and the growth time of the corrosion crack t = 0.62 year is established.On the basis of computational experiment the errors of estimating of crack growth time t and coordinates of the leakage points was established. They present 5…7 %. Based on the method of neural networks, the main informative parameters for determining the places of depressurization on the surface of the underground pipe are estimated. A method for estimating the gas pressure change in the vicinity of the crack after depressurization of the pipeline was proposed. The principles of determining the limit values of the parameters of the system “pipeline – pumping station” taking into account the criteria of quality and strength of the metal are formulated. Cabin pressurization ; Pipeline transport ; Leakage (economics) ; Petroleum engineering ; Corrosion ; Pipeline (software) ; Internal pressure ; Environmental science ; Mechanical engineering ; Materials science ; Engineering ; Metallurgy ; Composite material Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Brian M. Barnes | Mark-Alexander Henn
Source:  (article, 2020-03-24)
Field: Materials Science — Surfaces, Coatings and Films

Contrasting conventional and machine learning approaches to optical critical dimension measurements Accurate, optics-based measurement of feature sizes at deep sub-wavelength dimensions has been conventionally challenged by improved manufacturing, including smaller linewidths, denser layouts, and greater materials complexity at near-atomic scales. Electromagnetic modeling is relied upon heavily for forward maps used to solve the inverse problem of optical measurements for parametric estimation. Machine learning (ML) approaches are continually under consideration, either as a means to bypass direct comparison to simulation or as a method to augment nonlinear regression. In this work, ML approaches are investigated using a well-characterized experimental data set and its simulation library that assumes a 2-D geometry. The benefits and limitations of ML for optical critical dimension (OCD) metrology are illustrated by comparing a straightforward library lookup method and two ML approaches, a data-driven surrogate model for nonlinear regression using radial basis functions (RBF) and multiple-output Gaussian process regression (GPR) that indirectly applies the simulated intensity data. Both RBF and GPR generally improve accuracy over the conventional method with as few as 32 training points. However, as measurement noise is decreased the uncertainties from RBF and GPR differ greatly as the GPR posterior estimate of the variance appears to overestimate parametric uncertainties. Both accuracy and uncertainty must be addressed in OCD while balancing simulation versus ML computational requirements. Kriging ; Sliced inverse regression ; Parametric statistics ; Computer science ; Gaussian process ; Metrology ; Algorithm ; Dimension (graph theory) ; Ground-penetrating radar ; Nonlinear system ; Noise (video) ; Inverse problem ; Machine learning ; Artificial intelligence ; Gaussian ; Regression analysis ; Mathematics ; Statistics Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Optical Polarization and Ellipsometry
Authors: Giulio Imbalzano | Yongbin Zhuang | Venkat Kapil | Kevin Rossi | Edgar A. Engel | Federico Grasselli | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2020-11-10)
Field: Materials Science — Materials Chemistry

Uncertainty estimation by committee models for molecular dynamics and thermodynamic averages. Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, and obtain an estimate of the energy and properties of an atomistic system - enabling simulations of greater size, time scale and complexity, without sacrificing the accuracy of first-principles calculations. Given the interpolative nature of these models, the accuracy of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the model training. Committee models that combine information from multiple training exercises, performed for instance using different reference data, use the spread of their predictions to obtain reliable estimates of the uncertainty of a single-point calculation, such as a lattice energy. Such uncertainty quantification is particularly useful in the context of molecular dynamics simulations. Here we discuss how it can be used, together with a baseline energy model, or a more robust although less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in the thermodynamic averages extracted from long trajectories, incorporating both the error in the single-point predictions of the properties and the error due to the distortion of the sampling probability. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium. Computer science ; Statistical physics ; Context (archaeology) ; Position (finance) ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods