jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sabine M. Neumayer | Stephen Jesse | Gabriel Velarde | Andréi L. Kholkin | Ivan I. Kravchenko | Lane W. Martin | Nina Balke | Petro Maksymovych
Source: Nanoscale Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

To switch or not to switch – a machine learning approach for ferroelectricity With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, <i>etc.</i> Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response <i>via</i> linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis. Ferroelectricity ; Hysteresis ; Computer science ; Polarization (electrochemistry) ; Decoupling (probability) ; Perceptron ; Artificial intelligence ; Cluster analysis ; Artificial neural network ; Algorithm ; Machine learning ; Materials science ; Physics ; Dielectric ; Engineering ; Control engineering ; Chemistry ; Condensed matter physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Piezoelectric Materials
Authors: Hongyan Zhu | Jun‐Li Xu
Source: Molecules (article, 2020-10-28)
Field: Chemistry — Analytical Chemistry

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm<sup>-1</sup> is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms. Algorithm ; Artificial intelligence ; Fourier transform ; Random forest ; Computer science ; Fourier transform infrared spectroscopy ; Wavelet transform ; Machine learning ; Statistical classification ; Identification (biology) ; Mathematics ; Pattern recognition (psychology) ; Wavelet ; Engineering ; Botany Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Fermentation and Sensory Analysis
Authors: Sensong An | Bowen Zheng | Mikhail Y. Shalaginov | Hong Tang | Hang Li | L. P. Zhou | Jun Ding | Anu Agarwal | Clara Rivero‐Baleine | Myungkoo Kang | Kathleen Richardson | Tian Gu | Juejun Hu | Clayton Fowler | Hualiang Zhang
Source: arXiv (Cornell University) (preprint, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Freeform Dielectric Metasurface Modeling Approach Based on Deep Neural Networks Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand designs and optimizations. Wavefront ; Computer science ; Convolutional neural network ; Characterization (materials science) ; Artificial neural network ; Deep learning ; Dielectric ; Atom (system on chip) ; Lattice (music) ; Process (computing) ; Artificial intelligence ; Electronic engineering ; Algorithm ; Optics ; Materials science ; Physics ; Optoelectronics ; Acoustics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Mengshan Li | Jiale Zhang | Yanying Zou | Fan Wang | Bingsheng Chen | Lixin Guan | Wu Yan
Source: Materials Today Communications (review, 2020-06-01)
Field: Materials Science — Polymers and Plastics

Models for the solubility calculation of a CO2/polymer system: A review Solubility ; Interpretability ; Materials science ; Multiscale modeling ; Polymer ; Rheology ; Scale (ratio) ; Stability (learning theory) ; Biological system ; Phase (matter) ; Statistical physics ; Nanotechnology ; Computer science ; Physics ; Artificial intelligence ; Chemistry ; Machine learning ; Computational chemistry ; Physical chemistry Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Injection Molding Process and Properties
Authors: Kelong Mao | Peilin Zhao | Tingyang Xu | Yu Rong | Xi Xiao | Junzhou Huang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-06)
Field: Materials Science — Materials Chemistry

Molecular Graph Enhanced Transformer for Retrosynthesis Prediction Abstract With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy. Retrosynthetic analysis ; Transformer ; Computer science ; Machine translation ; Artificial intelligence ; Graph ; Natural language processing ; Theoretical computer science ; Machine learning ; Data mining ; Engineering ; Chemistry ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Young Jae Shin | Wheemyung Shin | Takashi Taniguchi | Kenji Watanabe | Philip Kim | Sung‐Ho Bae
Source: 2D Materials (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

Fast and accurate robotic optical detection of exfoliated graphene and hexagonal boron nitride by deep neural networks Abstract Van der Waals (vdW) heterostructure constructed from atomically thin layered materials provides quantum material platforms with emergent physical phenomena and novel device applications. While stacking atomically thin vdW layers in combination with automate machine vision identification and semi-automated stacking have been demonstrated, a combination of machine learning based automatic detection/identification assembly capability is necessary to further advance vdW heterostructure fabrication. Here, we developed a new automatic optical detection technique with a deep neural network (DNN) incorporated into a motorized microscope that automatically scans entire silicon wafers to detect and identify two-dimensional (2D) materials. We demonstrated the automated combination on an optical microscope (OM) with a DNN algorithm that enables identification and classification of graphene with different sizes, shapes and thicknesses. For this purpose, we trained a representative DNN for object detection with approximately 1000 OM images, resulting in high accuracy in detection and classification. We further verified the effectiveness of graphene trained DNN (GT-DNN) in practice, by confirming the yield of flakes depending on exfoliation method. In addition, we also showed the transferability of our method to other 2D materials. Our pre-trained GT-DNN was used to train a detection model for hexagonal boron nitride with only a few samples, resulting in further improvement of the accuracy of DNN-based detection. Our experimental method can effectively be extended to automatic detection and assembly of a plethora of 2D materials. Materials science ; Stacking ; Artificial intelligence ; Graphene ; Exfoliation joint ; Wafer ; Hexagonal boron nitride ; Artificial neural network ; Computer science ; Heterojunction ; Identification (biology) ; Nanotechnology ; Optoelectronics ; Pattern recognition (psychology) Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Paul Sinz | Michael W. Swift | Xavier Brumwell | Jialin Liu | Kwang Jin Kim | Yue Qi | Matthew Hirn
Source: The Journal of Chemical Physics (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Wavelet scattering networks for atomistic systems with extrapolation of material properties The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond the interpolation of the training set to the prediction of properties that were not present in the original training data. In addition to advances in machine learning architectures and training techniques, achieving this ambitious goal requires a method to convert a 3D atomic system into a feature representation that preserves rotational and translational symmetries, smoothness under small perturbations, and invariance under re-ordering. The atomic orbital wavelet scattering transform preserves these symmetries by construction and has achieved great success as a featurization method for machine learning energy prediction. Both in small molecules and in the bulk amorphous Li<sub>α</sub>Si system, machine learning models using wavelet scattering coefficients as features have demonstrated a comparable accuracy to density functional theory at a small fraction of the computational cost. In this work, we test the generalizability of our Li<sub>α</sub>Si energy predictor to properties that were not included in the training set, such as elastic constants and migration barriers. We demonstrate that statistical feature selection methods can reduce over-fitting and lead to remarkable accuracy in these extrapolation tasks. Extrapolation ; Wavelet ; Feature (linguistics) ; Artificial neural network ; Test set ; Scattering ; Energy (signal processing) ; Representation (politics) ; Interpolation (computer graphics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Artemii S. Ivanov | Konstantin G. Nikolaev | Anna A. Stekolshchikova | Weini T. Tesfatsion | Stanislav O. Yurchenko | Kostya S. Novoselov | Daria V. Andreeva | M. Yu. Rubtsova | Mikhail F. Vorovitch | Aydar A. Ishmukhametov | А.М. Егоров | Ekaterina V. Skorb
Source: ACS Applied Bio Materials (article, 2020-11-02)
Field: Chemistry — Electrochemistry

Tick-Borne Encephalitis Electrochemical Detection by Multilayer Perceptron on Liquid–Metal Interface This work depicts an electrochemical hydrogel-eutectic gallium indium alloy interface for the detection of tick-borne encephalitis (TBE) virus. This interface allows recording of nonlinear current-voltage responses, depending on the composition of the hydrogel. The current-voltage data for the machine learning model are trained by a multilayer perceptron. This model accurately recognizes the TBE antibody, antigen, and an antibody-antigen complex in mixture with interfering bovine serum albumin with 93% accuracy. Thus, this interface can be used as a convenient method for expressed viruses and pathogens detection. Interface (matter) ; Tick-borne encephalitis ; Electrochemistry ; Encephalitis ; Materials science ; Virology ; Computer science ; Medicine ; Chemistry ; Electrode ; Composite material ; Virus ; Capillary action Electrochemical Analysis and Applications ; Neuroscience and Neural Engineering ; Mosquito-borne diseases and control
Authors: Nhan Nu Thanh Ton | Minh-Quyet Ha | Takuma Ikenaga | Ashutosh Thakur | Hieu‐Chi Dam | Toshiaki Taniike
Source: 2D Materials (article, 2020-10-13)
Field: Materials Science — Materials Chemistry

Solvent screening for efficient chemical exfoliation of graphite Abstract Chemical exfoliation of graphite is an effective method to produce graphene of relative high quality, where the choice of solvents plays an important role in the product yield and quality. Here, we performed screening of different solvents and their mixtures for the liquid-phase exfoliation of graphite under ultrasonication. A synergistic effect among aromatic, amine, and halogen groups was identified. The synergy was more effectively exploited when these functional groups were combined through solvent mixtures compared to when they were introduced in the molecular structure of single solvents. The screening results were utilized for a novel machine learning technique based on the Dempster–Shafer theory of evidence to systematically investigate synergistic effects and recommend new solvent combinations. The proposed combination of the experiment and the data-driven approach was demonstrated to be powerful for exploring synergistic solvent combinations. Exfoliation joint ; Solvent ; Graphite ; Sonication ; Yield (engineering) ; Amine gas treating ; Materials science ; Graphene ; Organic chemistry ; Chemistry ; Nanotechnology ; Chromatography ; Composite material Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Kh. Abd El‐Aziz | Д. Сабер | A. A. Megahed
Source: International Journal of Metalcasting (article, 2020-11-24)
Field: Materials Science — Materials Chemistry

Investigation and Prediction of Abrasive Wear Rate of Heat-Treated HCCIs with Different Cr/C Ratios Using Artificial Neural Networks Abrasive ; Materials science ; Austenite ; Carbide ; Cast iron ; Tempering ; Martensite ; Metallurgy ; Volume fraction ; Structural material ; Alloy ; Composite material ; Microstructure Metal Alloys Wear and Properties ; Advanced materials and composites ; High Entropy Alloys Studies
Authors: Sergio A. Hassan
Source: The Journal of Chemical Physics (article, 2020-08-03)
Field: Materials Science — Biomaterials

Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma proteins. The ability of a NP to satisfy one or more of these requirements depends on its physicochemical characteristics, such as size, shape, and surface chemistry. Multiscale and pattern recognition techniques are here integrated to guide the design of NPs with preferential nano-bio behaviors. Data systematically collected from simulations (or experiments, if available) are first used to train one or more artificial neural networks, each optimized for a specific kind of nano-bio interaction; the trained networks are then interconnected in suitable arrays to obtain the NP core morphology and layer composition that best satisfy all the nano-bio interactions underlying more complex behaviors. This reverse engineering approach is illustrated in the case of NP-membrane interactions, using binding modes and affinities and early stage membrane penetrations as training data. Adaptations for designing NPs with preferential nano-protein interactions and for optimizing solution conditions in the test tube are discussed. Nanoparticle ; Nano- ; Nanotechnology ; Biomedicine ; Artificial neural network ; Membrane ; Biological system ; Inverse ; Computer science ; Chemistry ; Materials science ; Artificial intelligence ; Bioinformatics ; Mathematics ; Biology Nanoparticle-Based Drug Delivery ; Nanopore and Nanochannel Transport Studies ; Microfluidic and Bio-sensing Technologies
Authors: Junwoong Yoon | Zachary W. Ulissi
Source: Physical Review Letters (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Differentiable Optimization for the Prediction of Ground State Structures (DOGSS) Ground state or relaxed inorganic structures are the starting point for most computational materials science or surface science analyses. Many of these structure relaxations represent systematic changes to the structure, but there are currently no general methods to improve the initial structure guess based on past calculations. Here we present a method to directly predict the ground state configuration using differentiable optimization and graph neural networks to learn the properties of a simple harmonic force field that approximates the ground state structure and properties. We demonstrate this flexible open source tool for improving the initial configurations for large datasets of inorganic multicomponent surface relaxations across 32 elements and the relaxation of adsorbates (H and CO) on these surfaces. Using these improved initial configurations reduces the expensive adsorbate-covered surface relaxations by approximately 50% and is complementary to other approaches to accelerate the relaxation process. Ground state ; Differentiable function ; Surface (topology) ; Computer science ; Relaxation (psychology) ; Statistical physics ; Simple (philosophy) ; Point (geometry) ; Field (mathematics) ; Physics ; Mathematics ; Geometry ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Yihang Wang | Pratyush Tiwary
Source: The Journal of Chemical Physics (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Understanding the role of predictive time delay and biased propagator in RAVE In this work, we revisit our recent iterative machine learning (ML)-molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling" [J. M. L. Ribeiro et al., J. Chem. Phys. 149, 072301 (2018) and Y. Wang, J. M. L. Ribeiro, and P. Tiwary, Nat. Commun. 10, 3573 (2019)] and analyze and formalize some of its approximations. These include (a) the choice of a predictive time-delay, or how far into the future should the ML try to predict the state of a given system output from MD, and (b) that for short time-delays, how much of an error is made in approximating the biased propagator for the dynamics as the unbiased propagator. We demonstrate through a master equation framework as to why the exact choice of time-delay is irrelevant as long as a small non-zero value is adopted. We also derive a correction to reweight the biased propagator, and somewhat to our dissatisfaction but also to our reassurance, we find that it barely makes a difference to the intuitive picture we had previously derived and used. Propagator ; Bayesian probability ; Dynamics (music) ; Bayes' theorem ; Value (mathematics) ; Term (time) ; Bayesian inference Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Mahdieh Parsaeian | Mojtaba Shahabi | Hamid Hassanpour
Source: Journal of the American Oil Chemists Society (article, 2020-04-17)
Field: Chemistry — Analytical Chemistry

Estimating Oil and Protein Content of Sesame Seeds Using Image Processing and Artificial Neural Network Abstract Image processing has many applications in different fields of agriculture. The present study aimed to use image processing techniques and artificial neural networks (ANN) to estimate oil and protein contents of sesame genotypes without the use of time‐consuming and costly laboratory methods. The proposed method accurately estimates the parameters in sesame seeds without destructing the genetically valuable material. In this study, a set of 138 morphological features were extracted from the digital image of 125 sesame seed genotypes. A multilayer perceptron (MLP) ANN was then employed to estimate oil and protein contents and determine the relationship between estimated values and laboratory‐measured values. The efficiency of this model was compared to radial bases function (RBF), extended RBF (ERBF), GRNN, M5‐Rule, M5‐Tree, support vector machine regression, and linear regression models. Results showed that MLP performed better in estimating qualitative parameters of seeds in the sesame germplasm. The model estimated oil content with an root mean square error (RMSE) of 2.13% (the accuracy of 97.87%) and an R 2 of 0.93. Protein content was estimated by an RMSE of 0.378% (the accuracy of 99.62%) and an R 2 of 0.96. Artificial neural network ; Mean squared error ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Multilayer perceptron ; Mathematics ; Image processing ; Computer science ; Statistics ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Sesame and Sesamin Research