jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timothy Anderson | Yunan Li | Anthony R. Kovscek
Source: SPE Western Regional Meeting (article, 2021-04-07)
Field: Chemistry — Analytical Chemistry

Predicting Heavy Oil Combustion Kinetics with Machine Learning Abstract Heavy oil resources are becoming increasingly important for the global oil supply, and consequently there has been renewed interest in techniques for extracting heavy oil. Among these, in-situ combustion (ISC) has tremendous potential for late-stage heavy oil fields, as well as high viscosity, very deep, or other unconventional reservoirs. A critical step in evaluating the use of ISC in a potential project is developing an accurate chemical reaction model to employ for larger-scale simulations. Such models can be difficult to calibrate, however, that in turn can lead to large errors in upscaled simulations. Data-driven models of ISC kinetics overcome these issues by foregoing the calibration step and predicting kinetics directly from laboratory data. In this work, we introduce the Non-Arrhenius Machine Learning Approach (NAMLA). NAMLA is a machine learning-based method for predicting O2 consumption in heavy oil combustion directly from ramped temperature oxidation (RTO) experimental data. Our model treats the O2 consumption as a function of only temperature and total O2 conversion and uses a locally-weighted linear regression model to predict the conversion rate at a query point. We apply this method to simulated and experimental data from heavy oil samples and compare its ability to predict O2 consumption curves with a previously proposed interpolation-based method. Results show that the presented method has better performance than previously proposed interpolation models when the available experimental data is very sparse or the query point lies outside the range of RTO experiments in the dataset. When available data is sufficiently dense or the query point is within the range of RTO curves in the training set, then linear interpolation has comparable or better accuracy than the proposed method. The biggest advantage of the proposed method is that it is able to compute confidence intervals for experimentally measured or estimated O2 consumption curves. We believe that future methods will be able to use the efficiency and accuracy of interpolation-based methods with the statistical properties of the proposed machine learning approach to better characterize and predict heavy oil combustion. Combustion ; Interpolation (computer graphics) ; Calibration ; Computer science ; Range (aeronautics) ; Work (physics) ; Artificial intelligence ; Mathematics ; Chemistry ; Thermodynamics ; Engineering ; Statistics Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Market Dynamics and Volatility
Authors: Bin Ding | Dong Li | Yuli Chen
Source: Nanomaterials (article, 2021-05-25)
Field: Materials Science — Biomaterials

A Novel Long Short-Term Memory Based Optimal Strategy for Bio-Inspired Material Design Biological materials have attracted a lot of attention due to their simultaneous superior stiffness and toughness, which are conventionally attributed to their staggered structure (also known as brick and mortar) at the most elementary nanoscale level and self-similar hierarchy at the overall level. Numerous theoretical, numerical, and experimental studies have been conducted to determine the mechanism behind the load-bearing capacity of the staggered structure, while few studies focus on whether the staggered structure is globally optimal in the entire design space at the nanoscale level. Here, from the view of structural optimization, we develop a novel long short-term memory (LSTM) based iterative strategy for optimal design to demonstrate the simultaneous best stiffness and toughness of the staggered structure. Our strategy is capable of both rapid discovery and high accuracy based on less than 10% of the entire design space. Besides, our strategy could obtain and maintain all of the best sample configurations during iterations, which can hardly be done by the convolutional neural network (CNN)-based optimal strategy. Moreover, we discuss the possible future material design based on the failure point of the staggered structure. The LSTM-based optimal design strategy is general and universal, and it may be employed in many other mechanical and material design fields with the premise of conservation of mass and multiple optimal sample configurations. Optimal design ; Computer science ; Design strategy ; Stiffness ; Mathematical optimization ; Sample (material) ; Iterative design ; Focus (optics) ; Distortion (music) ; Material Design ; Structural engineering ; Mechanical engineering ; Mathematics ; Machine learning ; Engineering ; Scheduling (production processes) Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Machine Learning in Materials Science
Authors: Abolfazl Zare | Pedram Payvandy
Source: Pigment & Resin Technology (article, 2021-12-08)
Field: Materials Science — Biomaterials

The prediction of optimal conditions for the surface grafting of<i>β</i>-cyclodextrin onto silk fabrics by an artificial neural network (ANN) Purpose The purpose of this study is the chemical grafting of β -Cyclodextrin ( β -CD) onto silk fabrics by the use of butane tetracarboxylic acid (BTCA) as a crosslinking agent and nano-TiO 2 (NTO) as a catalyst. The effects of different parameters involved in this particular process, e.g. β -CD, BTCA and NTO concentrations, are examined using the artificial neural network (ANN). The method is evaluated for its ability to predict certain properties of treated fabrics, including grafting yield, dry crease recovery angle (DCRA) and wet crease recovery angle (WCRA), tensile strength, elongation at break and methylene blue dye absorption. Design/methodology/approach This study was conducted to describe the cross-linking of silk with 1,2,3,4-BTCA as a crosslinking agent in a wet state at low temperatures using NTO catalyst to improve the dry and wet wrinkle recovery (DCRA and WCRA) of silk fabrics. An ANN was also used to model and analyze the effects of BTCA, β -CD and NTO concentrations on the grafting percentage and some properties of the treated samples. Findings According to the results, the wet and dry wrinkle recovery of the silk fabrics was improved for about 38% and 11%, respectively, as compared to the non-cross-linked fabrics, without significantly affecting the tensile strength retention of the fabrics. Originality/value This research model and analyze the effects of BTCA, β -CD and NTO concentrations on the grafting percentage and some properties of the treated samples for the first time. Ultimate tensile strength ; Materials science ; SILK ; Grafting ; Composite material ; Yield (engineering) ; Chemical engineering ; Polymer Silk-based biomaterials and applications ; Dyeing and Modifying Textile Fibers ; Textile materials and evaluations
Authors: Aini Palizhati | Muratahan Aykol | Santosh K. Suram | Jens S. Hummelshøj | Joseph H. Montoya
Source: ChemRxiv (preprint, 2021-03-29)
Field: Materials Science — Materials Chemistry

Multi-fidelity Sequential Learning for Accelerated Materials Discovery We introduce a new agent-based framework for materials discovery that combines multi-fidelity modeling and sequential learning to lower the number of expensive data acquisitions while maximizing discovery. We demonstrate the framework's capability by simulating a materials discovery campaign using experimental and DFT band gap data. Using these simulations, we determine how different machine learning models and acquisition strategies influence the overall rate of discovery of materials per experiment. The framework demonstrates that including lower fidelity (DFT) data, whether as a-priori knowledge or using in-tandem acquisition, increases the discovery rate of materials suitable for solar photoabsorption. We also show that the performance of a given agent depends on data size, model selection, and acquisition strategy. As such, our framework provides a tool that enables materials scientists to test various acquisition and model hyperparameters to maximize the discovery rate of their own multi-fidelity sequential learning campaigns for materials discovery. Computer science ; Fidelity ; Knowledge extraction ; Data discovery ; Hyperparameter ; Machine learning ; High fidelity ; A priori and a posteriori ; Data acquisition ; Drug discovery ; Discovery learning ; Data mining ; Artificial intelligence ; Data science ; World Wide Web Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Expert finding and Q&A systems
Authors: Nicholas Gao | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2021-10-11)
Field: Materials Science — Materials Chemistry

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural networks succeeded at modeling wave functions of many-electron systems. Together with the variational Monte-Carlo (VMC) framework, this led to solutions on par with the best known classical methods. Still, these neural methods require tremendous amounts of computational resources as one has to train a separate model for each molecular geometry. In this work, we combine a Graph Neural Network (GNN) with a neural wave function to simultaneously solve the Schrödinger equation for multiple geometries via VMC. This enables us to model continuous subsets of the potential energy surface with a single training pass. Compared to existing state-of-the-art networks, our Potential Energy Surface Network PESNet speeds up training for multiple geometries by up to 40 times while matching or surpassing their accuracy. This may open the path to accurate and orders of magnitude cheaper quantum mechanical calculations. Wave function ; Artificial neural network ; Potential energy surface ; Quantum ; Computer science ; Statistical physics ; Surface (topology) ; Ab initio ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Hao Zhou | Yajuan Feng | Chao Wang | Teng Huang | Yi-Rong Liu | Shuai Jiang | Chunyu Wang | Wei Huang
Source: Nanoscale (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A high-accuracy machine-learning water model for exploring water nanocluster structures Water, the most important molecule on the Earth, possesses many essential and unique physical properties that are far from completely understood, partly due to serious difficulties in identifying the precise microscopic structures of water. Hence, identifying the structures of water nanoclusters is a fundamental and challenging issue for studies on the relationship between the macroscopic physical properties of water and its microscopic structures. For large-scale simulations (at the level of nm and ns) of water nanoclusters, a calculation method with simultaneous accuracy at the level of quantum chemistry and efficiency at the level of an empirical potential method is in great demand. Herein, a machine-learning (ML) water model was utilized to explore the microscopic structural features at different length scales for water nanoclusters with a size up to several nm. The ML water model can be employed to efficiently predict the structures of water nanoclusters with a similar accuracy to that of density functional theory and with substantially lower computational resource demands. To validate the low-lying structure search results with experimental spectral results, an ML water model combined with velocity autocorrelation function analysis was used to simulate the vibrational spectra of water nanoclusters with up to thousands of water molecules. By comparing the simulated and experimentally recorded vibrational spectra, the atomic structures determined by a simulation based on the ML water model are all verified. To demonstrate its ability to represent water's structural evolution at large length and time scales, the ML water model was employed to model the structural evolution during the crystal-liquid transition, and the phase transition temperatures of water clusters with different sizes were precisely predicted. The ML water model provides an efficient theoretical calculation tool for exploring the structures and physical properties of water and their relationships, especially for clusters with relatively large sizes and processes with relatively long durations. Nanoclusters ; Computer science ; Water model ; Nanotechnology ; Artificial intelligence ; Materials science ; Machine learning ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; nanoparticles nucleation surface interactions
Authors: Inho Kim | Yoonsung Bae | Seoungho Gwak | Euiseok Kum | Taeyong Jo
Source:  (article, 2021-06-16)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning aided profile measurement in high-aspect-ratio nanostructures The trend to produce semiconductor devices having more complex nanostructures results in the increasing importance of exquisite systems measuring multiple Critical Dimensions (CDs) of nanostructures. However, from a practical point of view, it is difficult to apply conventional methodologies to mass production because of cost and complexity issues. In this study, we propose an application of machine learning techniques utilizing optical information to measure nanoscale profiles of channel holes in High-Aspect-Ratio (HAR) structure of vertical NAND flash, which is applicable to mass production. By combining the conventional methodologies, the proposed method yields data pairs for supervised learning which include optical spectra obtained with Rotating Polarizer Ellipsometer (RPE) and images obtained with Scanning Electron Microscopy (SEM). Several preprocessing steps and machine learning techniques are introduced to train a model with sufficient performance to be applicable to mass production. In experiments, we obtained a model with coefficient of determination (R2) of 0.8 and Root Mean Square Error (RMSE) of 1.3 nm when predicting hundreds of nanoscale profiles of the channel holes which are measured with SEM. Furthermore, we confirmed that only 500 samples of data are sufficient to achieve the model performance with R2 greater than 0.7 and RMSE less than 1.5 nm. The proposed method is capable of replacing the conventional methods of profile measurement in the mass production stage by reducing the cost of destructive methods and accurately measuring the profiles of complex nanostructures without theoretical modeling. Mean squared error ; Computer science ; Preprocessor ; Materials science ; Nanostructure ; Artificial intelligence ; Aspect ratio (aeronautics) ; Metrology ; Machine learning ; Algorithm ; Nanotechnology ; Mathematics ; Optoelectronics ; Statistics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Optical Coatings and Gratings
Authors: Chen Wang | Qixiang Luo | Elizabeth A. Holm
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Hierarchically Structured Classification of Carbon Nanostructures from TEM Images by Machine Learning and Computer Vision Carbon nanotubes (CNT) and carbon nanofibers (CNF) are two widely used carbon nanomaterials for various industrial applications. The airborne particles released from of these materials during the handling and manufacturing of CNT/CNF products in workplaces has potential health impact on humans when exposed through inhalation In order to evaluate the potential exposure hazards of these materials, the airborne nanoparticulate samples were collected and analyzed by transmission electron microscopy (TEM) to determine their types, sizes, and specific morphological properties. After samples were obtained and imaged, individual particles were identified and classified based on aspect ratios and degree of agglomeration, among other descriptors. However, manual identification and classification of nanoscale structures require significant technical expertise and can be highly time-intensive for complex nanostructures Therefore, we introduced transfer learning-based machine learning algorithms and incorporated computer vision approaches to classify a dataset that consisted of 5,323 greyscale TEM images of airborne carbon/non-carbon nanomaterials (see representative images in Fig. Computer science ; Action (physics) ; Artificial intelligence ; Carbon fibers ; Nanostructure ; Computer vision ; Materials science ; Nanotechnology ; Physics ; Algorithm Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Carbon Nanotubes in Composites
Authors: Ethan T. Holleman | E.M. Duguid | Lisa J. Keefe | Sarah Bowman
Source: Journal of Applied Crystallography (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

<i>Polo</i>: an open-source graphical user interface for crystallization screening <i>Polo</i> is a Python-based graphical user interface designed to streamline viewing and analysis of images to monitor crystal growth, with a specific target to enable users of the High-Throughput Crystallization Screening Center at Hauptman-Woodward Medical Research Institute (HWI) to efficiently inspect their crystallization experiments. <i>Polo</i> aims to increase efficiency, reducing time spent manually reviewing crystallization images, and to improve the potential of identifying positive crystallization conditions. <i>Polo</i> provides a streamlined one-click graphical interface for the Machine Recognition of Crystallization Outcomes (MARCO) convolutional neural network for automated image classification, as well as powerful tools to view and score crystallization images, to compare crystallization conditions, and to facilitate collaborative review of crystallization screening results. Crystallization images need not have been captured at HWI to utilize <i>Polo</i>'s basic functionality. <i>Polo</i> is free to use and modify for both academic and commercial use under the terms of the copyleft GNU General Public License v3.0. Crystallization ; Open source ; Computer science ; Interface (matter) ; Graphical user interface ; Materials science ; Physics ; Operating system ; Thermodynamics ; Software Crystallization and Solubility Studies
Authors: Feng Ye | Joseph Ervin | Yang Lu | Anthony Woo | Martyn Coogans | Ivan Chakarov | David Fried
Source:  (article, 2021-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Scatterometry-based calibration of a 3D virtual fabrication model for gate-all-around devices Process optimization is a required step during semiconductor technology pathfinding and device evaluation. Virtual process modeling and 3D fabrication tools can be used to study diagnostic, predictive, and prescriptive modeling of process windows and to accelerate process integration. These virtual techniques will become especially valuable as novel gate-all-around devices (GAA) are introduced to replace state-of-the-art FinFET technologies. Model calibration is needed to ensure the accuracy of any virtual fabrication model and requires wafer-based metrology data. Optical scatterometry has established its value in the FinFET era as an effective inline metrology technique due to its accuracy, throughput, and non-destructive nature. In this article, we demonstrate how spectra collected from scatterometry targets can be utilized to resolve sub-nanometer feature changes within a virtual fabrication platform. First, FEOL GAA simulations up to the SiGe epitaxial growth step were performed to establish spectral sensitivity to upstream process changes. A virtual fabrication model was subsequently calibrated using spectra with variations from earlier process steps as model parameters. These variations were accurately pinpointed for unknown spectra via least-square optimization. Additionally, machine learning methods were leveraged to provide instantaneous feedback during the inference phase. Sub-nanometer accuracy was achieved, enabling wide applications in semiconductor technology development. This newly demonstrated capability will be indispensable in GAA commercialization, where 3D metrology and process integration are ongoing challenges. Metrology ; Semiconductor device fabrication ; Fabrication ; Calibration ; Process (computing) ; Wafer ; Computer science ; Semiconductor device modeling ; Optical proximity correction ; Throughput ; Electronic engineering ; Materials science ; Nanotechnology ; Engineering ; Optics ; Physics ; CMOS Optical Coatings and Gratings ; Advanced Surface Polishing Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Ganesh Sivaraman | Jicheng Guo | Logan Ward | Nathaniel C. Hoyt | Mark Williamson | Ian Foster | Chris J. Benmore | Nicholas Jackson
Source: ChemRxiv (preprint, 2021-03-18)
Field: Materials Science — Materials Chemistry

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl &lt;p&gt;The &lt;i&gt;in silico&lt;/i&gt; modeling of molten salts is of crucial importance to emerging "carbon free" energy applications, but is inhibited by the computational cost of quantum mechanically treating the high polarizabilities characteristic of molten salts. Here, we integrate configurational sampling using classical force-fields with active learning to automate the generation of near-DFT accurate machine learning Gaussian Approximation Potentials (GAP) for molten LiCl using fewer than 600 atomic configurations. Relative to conventional&lt;i&gt; ab initio&lt;/i&gt; molecular dynamics, the molten LiCl GAP model exhibits a &lt;b&gt;19,000x &lt;/b&gt;speedup and improved experimental agreement as gauged by calculated R-factors. The accuracy of the GAP parametrization workflow is validated by its ability to reproduce experimental structure factors, densities, self-diffusion coefficients, and ionic conductivities for molten LiCl. This hybrid simulation strategy significantly accelerates the generation of machine learning potentials for molten salts by reducing the expensive &lt;i&gt;ab initio&lt;/i&gt; calculations required for parameterization to &lt;b&gt;O(100)&lt;/b&gt; evaluations, enabling the facile generation of first-principles quality predictions of structural and dynamical properties of molten salts.&lt;/p&gt; Molten salt ; Ab initio ; Chemistry ; Parametrization (atmospheric modeling) ; Molecular dynamics ; Ionic liquid ; Ionic bonding ; Speedup ; Computational chemistry ; Computer science ; Physics ; Ion ; Catalysis ; Inorganic chemistry ; Quantum mechanics ; Parallel computing Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; X-ray Diffraction in Crystallography