jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Louis Allen | A. J. Gill | A. J. Smith | Dominic Hill | Peyman Z. Moghadam | Joan Cordiner
Source: Digital Chemical Engineering (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Development of a machine learning framework to determine optimal alloy composition based on steel hardenability prediction Mechanical property prediction plays a crucial role in the steel industry, enabling better materials selection and enhancing production efficiency. In this study, we propose a novel framework that facilitates optimal materials selection for steel’s alloying elements, based on accurate predictions of the Jominy hardness curve. Leveraging Gaussian Process (GP) regression, we provide probabilistic predictions of steel hardenability characteristics from alloy element composition. Taking it a step further, our framework incorporates these accurate predictions into a constrained optimization process, yielding optimal compositions that reduce overall spending while meeting performance specifications. Through data obtained from 1080 steel samples, our GP regression model exhibits high accuracy, achieving an RMSE of 1.37 and showcasing significant improvements in the field. Moreover, our constrained optimization utilizing the GP model and historical market data reveals an average cost reduction of 18% on alloying element expenses, highlighting the tangible cost-saving potential of this approach. By leveraging Gaussian Process (GP) regression, we not only achieve accurate predictions of the Jominy hardness curve based on alloy element composition, but we also introduce a crucial element of uncertainty quantification. This empowers us to place trust in the results of our optimization process, ensuring robust and reliable materials selection. The integration of GP regression and optimization provides a powerful tool for achieving cost-effective materials selection and marks a significant advancement compared to existing studies. This research underscores the promise of machine learning in the steel industry, demonstrating its ability to yield substantial cost savings and enhance decision-making in materials selection. Hardenability ; Kriging ; Process (computing) ; Gaussian process ; Computer science ; Selection (genetic algorithm) ; Regression analysis ; Regression ; Machine learning ; Mathematical optimization ; Gaussian ; Alloy ; Materials science ; Statistics ; Mathematics ; Metallurgy Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Gladys C. Vista | Dario Landa-Silva
Source:  (article, 2023-08-04)
Field: Materials Science — Polymers and Plastics

Performance Evaluation of Fiber Waste-Reinforced Polymer Using Backpropagation Neural Network Model and Finite Element Analysis Sustainable and renewable polymer composites are a remarkable feat, hinged on being eco-conscious by nature. This study aims to enhance global support in pursuing the potential of utilizing bio-composites in strengthening concrete structures. Beam-column joint, the most critical element in a building, is considered for studying concrete performance by incorporating fiber wastes for strengthening applications using fiber-reinforced polymer. This paper focuses on utilizing natural fiber wastes from abaca and coconut to manufacture polymer composites with epoxy resin, using a hand lay-up technique, designed to strengthen unreinforced beam-column joints. The fabricated polymers have undergone experimental investigation to obtain their strength properties for concrete retrofitting. Researchers conducted finite element analysis for 25 exterior joint specimens, applying different polymer configurations theoretically. Artificial neural network was utilized to create predictive strength models using the dataset obtained from numerical simulation, which included minimum, maximum, and average values. According to the analysis, the wrapping arrangement and thickness greatly influenced the concrete behavior when subjected to cyclic loading. Experimental data and numerical investigation with neural network analysis validate the hypothetical assumption to make fiber wastes an alternative to conventional material in developing fiber-reinforced polymers for structural applications. Backpropagation ; Finite element method ; Artificial neural network ; Fiber ; Materials science ; Computer science ; Structural engineering ; Composite material ; Engineering ; Artificial intelligence Natural Fiber Reinforced Composites ; Structural Behavior of Reinforced Concrete ; Textile materials and evaluations
Authors: Jan‐Michael Y. Carrillo | P Vijith | Tarak K. Patra | Zhan Chen | Thomas P. Russell | Subramanian K. R. S. Sankaranarayanan | Bobby G. Sumpter | Rohit Batra
Source: arXiv (Cornell University) (preprint, 2023-08-16)
Field: Materials Science — Materials Chemistry

Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of different chemical blocks (e.g., solvophilic and solvophobic blocks) that are covalently joined at one junction point. Various parameters of these macromolecules can be tuned to obtain desired surface properties, including the number of arms, composition of the arms, and the degree-of-polymerization of the blocks (or the length of the arm). This makes identification of the optimal s-BCP design highly non-trivial as the total number of plausible s-BCPs architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with reinforcement learning based Monte Carlo tree search (MCTS) to identify s-BCPs designs that minimize the interfacial tension between polar and non-polar solvents. We first validate the MCTS approach for design of small- and medium-sized s-BCPs, and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified using the configurations obtained from MD simulations. Chemical insights on the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provide important groundwork for future experimental investigation of s-BCPs sequences for various applications. Copolymer ; Solvophobic ; Computer science ; Surface tension ; Molecular dynamics ; Monte Carlo method ; Block (permutation group theory) ; Materials science ; Polymerization ; Macromolecule ; Fusion ; Biological system ; Algorithm ; Chemistry ; Polymer ; Molecule ; Physics ; Computational chemistry ; Mathematics ; Thermodynamics Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Xiangguo Li | Shuozhi Xu | Qian Zhang | Shenghua Liu | Jing Shuai
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Materials Science — Materials Chemistry

Complex strengthening mechanisms in nanocrystalline Ni-Mo alloys revealed by a machine-learning interatomic potential A nanocrystalline metal's strength increases significantly as its grain size decreases, a phenomenon known as the Hall-Petch relation. Such relation, however, breaks down when the grains become too small. Experimental studies have circumvented this problem in a set of Ni-Mo alloys by stabilizing the grain boundaries (GB). Here, using atomistic simulations with a machine learning-based interatomic potential (ML-IAP), we demonstrate that the inverse Hall-Petch relation can be correctly reproduced due to a change in the dominant deformation mechanism as the grain becomes small in the Ni-Mo polycrystals. It is found that the atomic von Mises strain can be significantly reduced by either solute doping and/or annealing for small-grain-size polycrystals, leading to the increased strength of the polycrystals. On the other hand, for large-grain-size polycrystals, annealing weakens the material due to the large atomic movements in GB regions. Over a broad range of grain size, the superposition of the solute and annealing effects on polycrystals enhances the strength of those with small grain size more than those with large ones, giving rise to the continuous strengthening at extremely small grain sizes. Overall, this study not only demonstrates the reliability of the ML-IAP, but also provides a comprehensive atomistic view of complex strengthening mechanisms in nanocrystals, opening up a new avenue to tailor their mechanical properties. Nanocrystalline material ; Materials science ; Grain size ; Grain boundary ; Grain boundary strengthening ; Annealing (glass) ; Strengthening mechanisms of materials ; Nanocrystal ; Metallurgy ; Condensed matter physics ; Nanotechnology ; Microstructure ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Ion-surface interactions and analysis
Authors: Alexander Wang | Samuel Chen | Matthew Chang | Evan Xie | Anthony Zhu | Adam Hansen | John Jerome | Miriam Rafailovich
Source: ChemRxiv (preprint, 2023-09-11)
Field: Materials Science — Materials Chemistry

Utilizing Machine Learning to Model Interdependency of Bulk Molecular Weight, Solution Concentration, and Thickness of Spin Coated Polystyrene Thin Films Spin coating is a wide-spread, quick, and inexpensive method to create nanometer-thick thin films of various polymers, such as polystyrene, on top of solid substrates. Since the film thickness determines the mechanical, optical, and degradation properties of the coated film, it is essential to develop a simple method to predict thickness based on other manipulatable factors. In this study, a three-dimensional manifold relating initial solution concentration, thin film coverage thickness, and monodisperse bulk molecular weight is developed utilizing curve-fit machine learning. The model is able to receive polystyrene bulk molecular weight and desired thin film thickness as input and output an accurate prediction of initial solution concentration required to generate a coating of a desired thickness. Polystyrene ; Dispersity ; Spin coating ; Materials science ; Thin film ; Nanometre ; Polymer ; Coating ; Composite material ; Nanotechnology ; Polymer chemistry High voltage insulation and dielectric phenomena
Authors: H Suhendar | A Windiyanti | A Asriani
Source: Journal of Physics Conference Series (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Prediction of organic molecular optical absorption energy based on deep learning using mordred descriptor features Abstract Organic molecules have several characteristics based on optical absorption. A molecule absorbs a specific light wavelength that represents electronic energy in the material. The absorption wavelength is also related to atomic binding of molecules that interact with material during light exposure producing electronic instability in the material. Molecular absorption can be measured using several optical spectrometer configurations that contain a light source, optical path, and light detector. The measurements of molecular optical absorption have been reported in several research and have been collected into a material database. In this paper, we developed a model for predicting organic molecular optical absorption using deep learning. The model can predict molecular absorption energy based on extracted features of molecular structure using Mordred descriptor features extraction. Our model used 1625 molecules of absorption in several solvent datasets that split into 80% training and 20% testing dataset. The result show that our model has a good agreement with experimental data with correlation coefficient 0.96 and mean average error 0.172 eV. Absorption (acoustics) ; Wavelength ; Attenuation coefficient ; Materials science ; Molecule ; Absorption spectroscopy ; Spectrometer ; Correlation coefficient ; Chemistry ; Optics ; Optoelectronics ; Computer science ; Physics ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: A Obayuwana | J.U. Okoye
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Towards the Development of Machine Learning Models for Deep Cycle Battery Performance Analysis: Research Gap Identification via Bibliometric Analysis and Global Evidence Mapping <em>The use of machine learning has recently increased in the field of energy storage. This is due to the level of accuracy and convenience in estimating battery states like the state of health (SOH), state of charge (SOC), and remaining useful life (RUL) which is used to estimate the performance of the battery. A lot of researchers have used machine models to estimate these battery states. These models each differ in approach, outcome, and effectiveness. This study was undertaken to identify research gaps in topics related to machine learning models for battery performance analysis. These related works were identified using bibliometric analysis coupled with a systematic literature review of the study search index through the Scopus database-indexed publications. The results from this study show 286 articles selected from 3,086 documents identified via a visualization of the network map rendered using VOSviewer. Sensitivity analysis, cycle life, and lead acid batteries were revealed as the particular research gaps. The study also presented results of mapping different methods used to identify, prioritize, and visualize research gaps. The study output would prove a useful resource for quick insight into the techniques and methodologies in this field</em><em>.</em> Identification (biology) ; Battery (electricity) ; Computer science ; Data science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science
Authors: Paul J. Dauenhauer
Source: ChemRxiv (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Up Up Down Down Left Right Left Right B A Start for the Catalytic Hackers of Programmable Materials The valuable information of catalysis for the past century has been the composition and structure of high-performing catalytic materials. But a new class of programmable catalysts that change the electronic characteristics of their active sites on the time scale of the surface reaction are changing the catalyst design process by requiring additional information describing the input program that directs the temporal changes in the catalyst surface. Catalyst programs vary in complexity associated with the number of combined waveforms required to optimize surface chemistry rates and selectivity to products. The path forward for writing and optimizing catalyst programs will combine together the methods of parameter screening, rational design based on molecular models, and machine learning. This new approach to catalysis will change the nature of catalysis science, with researchers pursuing dynamic catalytic programs with improved catalytic performance over static catalyst compositions. Catalysis ; Hacker ; Process (computing) ; Class (philosophy) ; Computer science ; Chemical engineering ; Chemistry ; Nanotechnology ; Materials science ; Engineering ; Organic chemistry ; Artificial intelligence ; Computer security Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Desmarita Leni
Source: Journal of Energy Material and Instrumentation Technology (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm This study aims to evaluate the influence of heatmap correlation-based feature selection on predictive modeling of low alloy steel mechanical properties using an artificial neural network (ANN) algorithm. Heatmap correlation was used to determine the chemical elements most correlated to the low alloy steel mechanical properties, such as Yield strength (YS) and Tensile strength (TS). There were 15 input variables of chemical elements in this study, and after feature selection, 11 input variables were obtained for YS, and 13 input variables were obtained for TS. The ANN model was validated using K-fold 10 cross-validation and evaluated using loss metric, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results showed that modeling with feature selection was able to improve the YS prediction, with a decrease in value of 6.83% in MAE and 4.97% in RMSE, while the TS prediction decreased by 16.46% in MAE and 18.34% in RMSE after feature selection. These results indicate that the use of feature selection provides better performance compared to the model without feature selection, and heatmap correlation can be used as an alternative to improve model performance in predictive modeling of low alloy steel mechanical properties using the ANN algorithm. Mean squared error ; Feature selection ; Artificial neural network ; Metric (unit) ; Feature (linguistics) ; Selection (genetic algorithm) ; Root mean square ; Correlation ; Predictive modelling ; Ultimate tensile strength ; Alloy ; Pattern recognition (psychology) ; Mathematics ; Computer science ; Artificial intelligence ; Materials science ; Statistics ; Engineering ; Metallurgy Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Advanced machining processes and optimization
Authors: Fereshteh Ramezani | Sheikh Parvez | J. Pierce Fix | Arthur Battaglin | Seamus Whyte | Nick Borys | Bradley M. Whitaker
Source:  (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

Automatic 2D material detection in optical images using deep-learning-based computer vision Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (&lt; 50 atomic layers thick). Computer science ; Artificial intelligence ; Object detection ; Deep learning ; Computer vision ; Pipeline (software) ; Task (project management) ; Materials science ; Pattern recognition (psychology) ; Engineering 2D Materials and Applications ; Ga2O3 and related materials ; Advanced biosensing and bioanalysis techniques
Authors: Xuejia Liu | Haoyu Zhang | Shuai Zhang | Wen Peng | Ge Zhou | Chuan Wang | Lijia Chen
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Hot Deformation Behavior of Near-Β Titanium Alloy Ti-3mo-6cr-3al-3sn Based on Phenomenological Constitutive Model and a Machine Learning Algorithm Materials science ; Constitutive equation ; Titanium alloy ; Phenomenological model ; Deformation (meteorology) ; Alloy ; Composite material ; Physics ; Thermodynamics ; Condensed matter physics ; Finite element method Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Zhiwei Zheng | Siyi Qiu | Yue Xuezheng | Jincheng Wang | Juan Hou
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Detecting Irradiation Defects in Materials: A Machine Learning Approach to Analyze Helium Bubble Images Bubble ; Irradiation ; Helium ; Materials science ; Nuclear engineering ; Nuclear physics ; Computer science ; Engineering ; Physics ; Mechanics ; Atomic physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Basu Dev Oli | Qiang Zou | Xin Li | Lian Li
Source: AIP Advances (article, 2023-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic-scale electronic inhomogeneity in single-layer iron chalcogenide alloys revealed by machine learning of STM/S data Chemical pressure from the isovalent substitution of Se by a larger Te atom in the epitaxial film of iron chalcogenide FeSe can effectively tune its superconducting, topological, and magnetic properties. However, such substitution during epitaxial growth inherently leads to defects and structural inhomogeneity, making the determination of alloy composition and atomic sites for the substitutional Te atoms challenging. Here, we utilize machine learning to distinguish between Se and Te atoms in scanning tunneling microscopy images of single-layer FeSe1−xTex on SrTiO3(001) substrates. Defect locations are first identified by analyzing spatial-dependent dI/dV tunneling spectra using the K-means clustering method. After excluding the defect regions, the remaining dI/dV spectra are further analyzed using the singular value decomposition method to determine the Se/Te ratio. Our findings demonstrate an effective and reliable approach for determining alloy composition and atomic-scale electronic inhomogeneity in superconducting single-layer iron chalcogenide films. Chalcogenide ; Scanning tunneling microscope ; Materials science ; Epitaxy ; Superconductivity ; Atomic units ; Alloy ; Atom (system on chip) ; Condensed matter physics ; Spectral line ; Crystallography ; Layer (electronics) ; Nanotechnology ; Chemistry ; Optoelectronics ; Metallurgy ; Physics ; Computer science Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Magnetic and transport properties of perovskites and related materials