jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianqi Xiao | Dong Ni
Source: Processes (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

Multiscale Modeling and Recurrent Neural Network Based Optimization of a Plasma Etch Process In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving optimization method is applied to overcome the error accumulation problem when using RNN models. The optimization goal is to achieve the desired average etching depth and average bottom roughness within the least amount of time. The simulation results show that our prediction model is accurate enough and the optimization objectives can be completed well. Recurrent neural network ; Computer science ; Artificial neural network ; Monte Carlo method ; Optimization problem ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics Block Copolymer Self-Assembly ; Semiconductor materials and devices ; Neural Networks and Applications
Authors: Sungwoo Kang | Seungwu Han | Youngho Kang
Source: ACS Materials Au (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

First-Principles Calculations of Luminescence Spectra of Real-Scale Quantum Dots The luminescence line shape is an important feature of semiconductor quantum dots (QDs) and affects performance in various optical applications. Here, we report a first-principles method to predict the luminescence spectrum of thousands of atom QDs. In our approach, neural network potential calculations are combined with density functional theory calculations to describe exciton-phonon coupling (EPC). Using the calculated EPC, the luminescence spectrum is evaluated within the Franck-Condon approximation. Our approach results in the luminescence line shape for an InP/ZnSe core/shell QD (3406 atoms) that exhibits excellent agreement with the experiments. From a detailed analysis of EPC, we reveal that the coupling of both acoustic and optical phonons to an exciton are important in determining the spectral line shapes of core/shell QDs, which is in contrast with previous studies. On the basis of the present simulation results, we provide guidelines for designing high-performance core/shell QDs with ultrasharp emission spectra. Quantum dot ; Luminescence ; Scale (ratio) ; Spectral line ; Materials science ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Advanced Semiconductor Detectors and Materials
Authors: Aryan Deshwal | Cory M. Simon | Janardhan Rao Doppa
Source:  (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

Bayesian optimization of nanoporous materials Nanoporous materials (NPMs) could be used to store, capture, and sense many different gases. Given an adsorption task, we often wish to search a library of NPMs for the one with the optimal adsorption property. The high cost of NPM synthesis and gas adsorption measurements, whether these experiments are in the lab or in a simulation, often precludes exhaustive search. We explain, demonstrate, and advocate Bayesian optimization (BO) to actively search for the optimal NPM in a library of NPMs-- and find it using the fewest experiments. The two ingredients of BO are a surrogate model and an acquisition function. The surrogate model is a probabilistic model reflecting our beliefs about the NPM-structure--property relationship based on observations from past experiments. The acquisition function uses the surrogate model to score each NPM according to the utility of picking it for the next experiment. It balances two competing goals: (a) exploitation of our current approximation of the structure-property relationship to pick the highest-performing NPM, and (b) exploration of blind spots in the NPM space to pick an NPM we are uncertain about, to improve our approximation of the structure-property relationship. We demonstrate BO by searching an open database of ~70,000 hypothetical covalent organic frameworks (COFs) for the COF with the highest simulated methane deliverable capacity. BO finds the optimal COF and acquires 30% of the top 100 highest-ranked COFs after evaluating only ~120 COFs. More, BO searches more efficiently than evolutionary and one-shot supervised machine learning approaches. Nanoporous ; Property (philosophy) ; Bayesian optimization ; Computer science ; Probabilistic logic ; Bayesian probability ; Function (biology) ; Space (punctuation) ; Tabu search ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics ; Materials science ; Nanotechnology Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: N. S. Mondal | Subhadip Nath | Suman Chowdhury | Debnarayan Jana
Source: Applied Surface Science (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Electric field-induced electronic-thermoelectric-optical properties of typical isoelectronic HNC6 monolayers: A theoretical study Thermoelectric effect ; Electric field ; Band gap ; Graphene ; Materials science ; Electronic structure ; Condensed matter physics ; Thermoelectric materials ; Optoelectronics ; Nanotechnology ; Physics ; Thermodynamics MXene and MAX Phase Materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Ehsan Rahmatizad Khajehpasha | Stefan Goedecker | S. Alireza Ghasemi
Source: Journal of Computational Chemistry (article, 2021-02-08)
Field: Materials Science — Materials Chemistry

New strontium titanate polymorphs under high pressure We report six new dynamically stable structures of SrTiO<sub>3</sub> at various pressures ranging from 0 to 200 GPa. These structures were found by exploring the enthalpy surface with the Minima Hopping structure prediction method. The potential energy surface was generated by a machine learned potential, the charge equilibration via neural network technique (CENT), based on an extensive training data set of highly diverse SrTiO<sub>3</sub> periodic and cluster structures. All our CENT structures were validated at the level of density functional theory. For our new structures, we performed phonon calculations and NVT molecular dynamics calculations to investigate their dynamical stability. Finally, X-ray diffraction patterns were simulated to help to identify our predicted structures in experiments. Strontium titanate ; Maxima and minima ; Molecular dynamics ; Cluster (spacecraft) ; Enthalpy ; Materials science ; Stability (learning theory) ; Density functional theory ; Diffraction ; Potential energy surface ; Phonon ; Chemical physics ; Statistical physics ; Molecule ; Thermodynamics ; Computer science ; Physics ; Nanotechnology ; Computational chemistry ; Condensed matter physics ; Chemistry ; Mathematics ; Thin film ; Optics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Sherif Haggag | Loai Nasrat | Hanafy M. Ismail
Source: Journal of Advanced Dielectrics (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

ANN approaches to determine the dielectric strength improvement of MgO based low density polyethylene nanocomposite This paper presents the modification occurred to the dielectric strength feature of low density polyethylene compounded with nano magnesia (LDPE/MgO). MgO nanoparticles were prepared using sol–gel technique, MgO filler surface was functionalized to improve the interfacial bonding. Specimen’s groups of composites with different filler concentrations were fabricated by mix blend method. Samples exposed to various salinity media by immersion, dielectric strength test was applied on each set according to relevant ASTM standard with identical testing technique. The results were statistically processed then compared to the pristine material. Tests results utilized to learn Artificial Neural Network in order to acquire the value of dielectric strength of compounds having similar composition but containing different doping amounts or influenced with various salinity level media. The dielectric strength is enhanced by the addition of MgO nanofiller. From the investigation of the obtained results, it is concluded that additives of 1.4% filler concentration by weight is the optimum MgO content for LDPE/MgO nanofiller material. We think that this paper may promote a good researching methodology that gather both empirical work and numerical tools in this field. Materials science ; Low-density polyethylene ; Nanocomposite ; Dielectric ; Composite material ; Filler (materials) ; Polyethylene ; Magnesium ; Surface modification ; Chemical engineering ; Metallurgy High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Vidya Bommanapally | Md Ashaduzzman | Milind Malshe | Parvathi Chundi | Mahadevan Subramaniam
Source: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

Self-supervised Learning Approach to Detect Corrosion Products in Biofilm images Detection of microbially influenced corrosion (MIC) products in biofilm images is an important problem in material science and engineering. Neural network models that can accurately detect biofilm images having corrosion products are likely to have significant and broad implications in the study and development of materials. However, generating and annotating biofilm image datasets in sufficient volumes is a major impediment in developing such networks. A self-supervised learning approach is presented for automatically detecting MIC products on metal surfaces based on analyses of Scanning Electron Microscope (SEM) biofilm images. The proposed approach uses a Simple Siamese (SimSiam) architecture to learn visual image representations from an unlabeled set of biofilm images, which is then fine-tuned using a scarce set of labeled images to build a model to detect biofilms containing corrosion products. The architecture generates two different augmented versions of the input images and learns the representations using an encoder that uses ResNet backbone. The architecture aims to minimize the negative cosine similarity of the outputs from the encoders and hence learns the representations of the images as both augmented versions belong to the same image. In order to improve the dataset quality and volume, input images are contrast enhanced, scaled, and overlapping image patches are generated and used to learn representations and fine-tuning. The performance of the models are analyzed using precision and recall metrics for patches of varying sizes. An overall accuracy of 62% was obtained for the classification of corrosion in the images after finetuning the model with scarce labeled dataset. Our results show that the models built using the proposed self-supervised learning approach can successfully detect corrosion products in biofilm images and that the performance of the models successively improves with increase in the patch size. Artificial intelligence ; Computer science ; Biofilm ; Set (abstract data type) ; Deep learning ; Pattern recognition (psychology) ; Precision and recall ; Encoder ; Image (mathematics) ; Computer vision ; Cosine similarity ; Similarity (geometry) ; Discrete cosine transform Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Qian Du | Quan Zhang | Guohua Liu
Source: Nanotechnology (article, 2021-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning: an efficient method for plasmonic design of geometric nanoparticles Plasmons of noble metal nanoparticles have played an important role in energy transfer research, biomedical sensing, drug preparation and photocatalysis in recent years. The scattering spectra of nanoparticles are dramatically affected by numerous complex parameters, such as morphology, material, and volumes, making the parameters design a necessary step before the experiment. However, the plasmonic design is limited by several difficulties, such as the high degree of freedom, expensive trial and error costs. Herein, a plasmonic design method based on dual strategy deep learning is proposed, which can provide the design proposals according to the required spectrum. To make the model closer to the real experimental situation, the boundary element method was used to build a scattering spectra dataset of geometric nanoparticles (>1200 000 samples). Driven by the above data, the artificial intelligence (AI) model learns the relationship between spectral features and design parameters. Then, the performance statistics of the model were implemented from multiple dimensions, and a high design precision of over 90% was achieved in testing cases (testing samples >120 000). Moreover, to verify the realizability of the proposed model, the scattering spectra of nanoparticles designed by AI were constructed using a dark-field microscope system. The experimental results showed that the deviation between the target and the actual spectra was very small and within the acceptable range. This proves the realizability of the AI model proposed in this paper, and sheds light on the application of AI in plasmonic design. Plasmon ; Realizability ; Materials science ; Computer science ; Nanoparticle ; Plasmonic nanoparticles ; Artificial intelligence ; Deep learning ; Scattering ; Nanotechnology ; Algorithm ; Optics ; Optoelectronics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Nanoplatforms for cancer theranostics
Authors: Yueting Wang | Minzan Li | Ronghua Ji | Minjuan Wang | Yao Zhang | Lihua Zheng
Source: Soil and Tillage Research (article, 2021-06-16)
Field: Chemistry — Analytical Chemistry

Construction of complex features for predicting soil total nitrogen content based on convolution operations Deconvolution ; Computer science ; Algorithm ; Biological system ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Soil Geostatistics and Mapping
Authors: Zbigniew Łodziana
Source: Frontiers in Energy Research (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Surface Properties of LaNi5 and TiFe—Future Opportunities of Theoretical Research in Hydrides Hydrogen in the solid state compounds is still considered as a safe method of energy storage. The ultimate metal hydrides or other materials that can be used for this purpose remain unknown. Such metal hydrides shall have favorable thermodynamics and kinetics of hydrogen ad/desorption, and it shall be resistant to contamination of H 2 and should not constitute any environmental hazards. Theoretical investigations, based on quantum mechanics approach, have a well-established position in modern materials research; however, their application for design of new alloys with tailored properties for reversible hydrogen storage is rarely present in the literature. The mainstream research deals with accurate prediction of thermodynamic and structural properties of hydrides as a function of composition or external parameters. On the other hand, the kinetic effects related to hydrogen transport or interaction between solid and pure or contaminated H 2 are more demanding. They cannot be easily automated. We present calculations of the equilibrium crystal shapes for LaNi 5 and TiFe—two important materials that show reversible hydrogen cycling near ambient conditions. Understanding of the surface properties is crucial for development of materials with better cyclability or resistance to hydrogen impurities. Indeed, the calculated adsorption energy of carbon oxides or water is stronger than hydrogen. These molecules block the active sites for H 2 dissociation, leading to formation of surface oxides. Particularly strong adsorption of CO/CO 2 on TiFe explains large degradation of hydrogen storage capacity of this compound by carbon oxides. Over-representation of La on exposed facets of LaNi 5 is related to formation of La 2 O 3 and La(OH) 3 . Such examples show that the present development of computational methods allows reliable studies of intermetallic properties related to their surface or novel catalytic applications. Hydrogen storage ; Hydrogen ; Materials science ; Adsorption ; Dissociation (chemistry) ; Desorption ; Metal ; Thermodynamics ; Chemical physics ; Physical chemistry ; Chemistry ; Metallurgy ; Organic chemistry Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Catalysis and Hydrodesulfurization Studies
Authors: Karol Frydrych | Kamran Karimi | Michal Pecelerowicz | Rene Alvarez | Francesco Javier Dominguez-Gutiérrez | Fabrizio Rovaris | Stefanos Papanikolaou
Source: Preprints.org (review, 2021-09-09)
Field: Materials Science — Materials Chemistry

Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials discovery or develop new understanding of materials&amp;rsquo; behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects. Materials informatics ; Deformation (meteorology) ; Focus (optics) ; Intersection (aeronautics) ; Informatics ; Data science ; Computer science ; Field (mathematics) ; Nanotechnology ; Materials science ; Health informatics ; Engineering ; Engineering informatics ; Physics ; Aerospace engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yoyo Hinuma | Shinya Mine | Takashi Toyao | Zen Maeno | Ken‐ichi Shimizu
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Surface activation by electron scavenger metal nanorod adsorption on TiH<sub>2</sub>, TiC, TiN, and Ti<sub>2</sub>O<sub>3</sub> Metal/oxide support perimeter sites are known to provide unique properties because the nearby metal changes the local environment on the support surface. In particular, the electron scavenger effect reduces the energy necessary for surface anion desorption, and thereby contributes to activation of the (reverse) Mars-van Krevelen mechanism. This study investigated the possibility of such activation in hydrides, carbides, nitrides, and sulfides. The work functions (WFs) of known hydrides, carbides, nitrides, oxides, and sulfides with group 3, 4, or 5 cations (Sc, Y, La, Ti, Zr, Hf, V, Nb, and Ta) were calculated. The WFs of most hydrides, carbides, and nitrides are smaller than the WF of Ag, implying that the electron scavenger effect may occur when late transition metal nanoparticles are adsorbed on the surface. The WF of oxides and sulfides decreases when reduced. The surface anion vacancy formation energy correlates well with the bulk formation energy in carbides and nitrides, while almost no correlation is found in hydrides because of the small range of surface hydrogen vacancy formation energy values. The electron scavenger effect is explicitly observed in nanorods adsorbed on TiH2 and Ti2O3; the surface vacancy formation energy decreases at anion sites near the nanorod, and charge transfer to the nanorod happens when an anion is removed at such sites. Activation of hydrides, carbides, and nitrides by nanorod adsorption and screening support materials through WF calculation are expected to open up a new category of supported catalysts. Vacancy defect ; Materials science ; Nanorod ; Carbide ; Nitride ; Adsorption ; Inorganic chemistry ; MXenes ; Metal ; Oxide ; Chemistry ; Physical chemistry ; Nanotechnology ; Crystallography ; Metallurgy ; Layer (electronics) Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Maryam Darvishi | Shahab Shariati | Fariba Safa | Akbar Islamnezhad
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Electrochemistry

Surface blocking of azolla modified copper electrode for trace determination of phthalic acid esters as the molecular barricades by differential pulse voltammetry: response surface modelling optimized biosensor In this work, a sensitive and efficient voltammetric biosensor was introduced for differential pulse voltammetric (DPV) determination of some phthalic acid esters (PAEs) including dibutyl phthalate (DBP), dimethyl phthalate (DMP), di(2-ethylhexyl)phthalate (DEHP) and dicyclohexyl phthalate (DCHP) in aqueous solutions. Briefly, the surface of a copper electrode was modified by azolla paste prepared using azolla powder and electroencephalography gel (EEG). The modified surface was characterized by electrochemical impedance spectroscopy (EIS), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), Brunauer-Emmett-Teller (BET) analysis and energy dispersive X-ray (EDX) methods. Determination of PAEs was conducted based on their blocking effect on the electrode surface for ferrous ion oxidation. The central composite design (CCD) was conducted to optimize the effects of four experimental parameters including the concentration of Fe<sup>2+</sup> ions (<i>C</i> <sub>Fe<sup>2+</sup></sub> ) and supporting electrolyte (<i>C</i> <sub>sup. elec</sub>), solution pH and modifier/gel mass ratio on the decrease in the anodic peak current of ferrous ions as the response. Predicted optimal conditions (<i>C</i> <sub>Fe<sup>2+</sup></sub> = 319 μM, <i>C</i> <sub>sup. elec</sub>= 0.125 M, pH = 7.52 and modifier/gel mass ratio = 0.19) were validated by experimental checking which resulted in an error of 1.453%. At the optimum conditions, linear relationships were found between the DPV responses and PAEs concentrations and the limit of detection (LOD) and limit of quantification (LOQ) values were in the ranges of 0.2-0.4 μg L<sup>-1</sup> and 0.5-1.0 μg L<sup>-1</sup>, respectively. Good recovery percentages ranging from 97.3 to 100.3% with RSD < 3.2% suggested the proposed method for efficient, accurate and quick determination of PAEs in real water samples. Azolla ; Phthalic acid ; Copper ; Differential pulse voltammetry ; Chemistry ; Electrode ; Voltammetry ; Biosensor ; Pulse (music) ; Cyclic voltammetry ; Inorganic chemistry ; Organic chemistry ; Voltage ; Electrochemistry ; Physical chemistry ; Botany Electrochemical Analysis and Applications ; Analytical chemistry methods development ; Effects and risks of endocrine disrupting chemicals