jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: L. Li | Mayank Agrawal | Sy-Jer Yeh | Kanber Lam | Jun Wu | Blanka Magyari‐Köpe
Source: 2022 International Electron Devices Meeting (IEDM) (article, 2022-12-03)
Field: Materials Science — Materials Chemistry

Towards accurate and efficient process simulations based on atomistic and neural network approaches Leveraging a powerful new methodology based on machine learning techniques coupled with atomistic simulation approaches, a previously undiscovered reaction sequence is predicted for Si epitaxial growth using $\mathrm{S}\mathrm{i}_{2}\mathrm{H}_{6}$ precursor. The complex surface reaction mechanism revealed by metadynamics simulations involves process controllable intermediate $\mathrm{S}\mathrm{i}\mathrm{H}_{2}$ formation that explains a long standing unresolved experimentally reported growth plateau appearance during $\mathrm{S}\mathrm{i}_{2}\mathrm{H}_{6}$ adsorption in the presence of H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> carrier gas. Due to its accuracy and transferable flexible design, the proposed general workflow model can be applied to any deposition and etching process condition optimization to enhance thin film quality and to provide yield improvement pathways for next generation electronic device materials. Yield (engineering) ; Metadynamics ; Workflow ; Process (computing) ; Artificial neural network ; Materials science ; Etching (microfabrication) ; Adsorption ; Computer science ; Nanotechnology ; Molecular dynamics ; Physics ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physical chemistry ; Thermodynamics Machine Learning in Materials Science ; Semiconductor materials and devices ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuuichi Orimoto | Keisuke Hisama | Yuriko Aoki
Source: The Journal of Chemical Physics (article, 2022-05-04)
Field: Materials Science — Materials Chemistry

Local electronic structure analysis by <i>ab initio</i> elongation method: A benchmark using DNA block polymers The ab initio elongation (ELG) method based on a polymerization concept is a feasible way to perform linear-scaling electronic structure calculations for huge aperiodic molecules while maintaining computational accuracy. In the method, the electronic structures are sequentially elongated by repeating (1) the conversion of canonical molecular orbitals (CMOs) to region-localized MOs (RLMOs), that is, active RLMOs localized onto a region close to an attacking monomer or frozen RLMOs localized onto the remaining region, and the subsequent (2) partial self-consistent-field calculations for an interaction space composed of the active RLMOs and the attacking monomer. For each ELG process, one can obtain local CMOs for the interaction space and the corresponding local orbital energies. Local site information, such as the local highest-occupied/lowest-unoccupied MOs, can be acquired with linear-scaling efficiency by correctly including electronic effects from the frozen region. In this study, we performed a local electronic structure analysis using the ELG method for various DNA block polymers with different sequential patterns. This benchmark aimed to confirm the effectiveness of the method toward the efficient detection of a singular local electronic structure in unknown systems as a future practical application. We discussed the high-throughput efficiency of our method and proposed a strategy to detect singular electronic structures by combining with a machine learning technique. Electronic structure ; Ab initio ; Atomic orbital ; Materials science ; Benchmark (surveying) ; Molecular physics ; Chemical physics ; Physics ; Computer science ; Statistical physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; DNA and Nucleic Acid Chemistry
Authors: Sanjay Kumar | Sahil Kansal | Monagi H. Alkinani | Ahmed Elaraby | Saksham Garg | Natarajan Shanthi | Vishnu Sharma
Source: Electronics (article, 2022-08-20)
Field: Chemistry — Analytical Chemistry

Segmentation of Spectral Plant Images Using Generative Adversary Network Techniques The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Image processing ; Computer vision ; Pixel ; Segmentation ; Image segmentation ; Image (mathematics) ; Feature detection (computer vision) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Currency Recognition and Detection
Authors: Joelle Nader
Source:  (article, 2022-02-24)
Field: Chemistry — Analytical Chemistry

Advanced Analytics Tools for Process Improvement: A Case Study in a Brewery The fourth industrial revolution (Industry 4.0) and the digital transformation are rising exponentially nowadays. This digital revolution is mainly manifested by incorporation of different technologies that rely on Artificial Intelligence (AI) and Big Data to foster automatic learning systems. In this context, big data enables the integration of previously isolated systems allowing companies to obtain a complete visualization of operations and processes. Therefore, advanced analytics tools are needed to cope with complex data and provide deeper understandings and accurate predictions. This research paper presents insights on the most prominent analytics techniques including machine learning, neural network, genetic algorithm, dynamic programming, lean six sigma and response surface methodology. Additionally, the benefits of such techniques were portrayed to highlight their important role as efficient optimization methods in any organization. In this paper, one of the above-mentioned methods was valorized through a case study tackling the wort boiling in a brewery. Design of experiments, which is an advanced lean six sigma approach, was adopted to improve and optimize the brewing process. By implementing response surface methodology and by varying three operating parameters (pressure, boiling duration and extract before boiling) within their ranges of application, results suggest a positive impact on energy and steam consumption; the evaporation rate was stabilized, and total condensate was minimized. Although this case study sheds light on the necessary tools to reduce time, material and energy waste in a brewery, same strategy can be extrapolated to other comparable manufacturing companies. Big data ; Computer science ; Analytics ; Context (archaeology) ; Process (computing) ; Industrial engineering ; Artificial intelligence ; Artificial neural network ; Data science ; Manufacturing engineering ; Process engineering ; Machine learning ; Data mining ; Engineering Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Advanced Chemical Sensor Technologies
Authors: In-Hyuk Jang | Supreet Kaur | Arun Yethiraj
Source: The Journal of Chemical Physics (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Importance of feature construction in machine learning for phase transitions Machine learning is an important tool in the study of the phase behavior from molecular simulations. In this work, we use un-supervised machine learning methods to study the phase behavior of two off-lattice models, a binary Lennard-Jones (LJ) mixture and the Widom-Rowlinson (WR) non-additive hard-sphere mixture. The majority of previous work has focused on lattice models, such as the 2D Ising model, where the values of the spins are used as the feature vector that is input into the machine learning algorithm, with considerable success. For these two off-lattice models, we find that the choice of the feature vector is crucial to the ability of the algorithm to predict a phase transition, and this depends on the particular model system being studied. We consider two feature vectors, one where the elements are distances of the particles of a given species from a probe (distance-based feature) and one where the elements are +1 if there is an excess of particles of the same species within a cut-off distance and -1 otherwise (affinity-based feature). We use principal component analysis and t-distributed stochastic neighbor embedding to investigate the phase behavior at a critical composition. We find that the choice of the feature vector is the key to the success of the unsupervised machine learning algorithm in predicting the phase behavior, and the sophistication of the machine learning algorithm is of secondary importance. In the case of the LJ mixture, both feature vectors are adequate to accurately predict the critical point, but in the case of the WR mixture, the affinity-based feature vector provides accurate estimates of the critical point, but the distance-based feature vector does not provide a clear signature of the phase transition. The study suggests that physical insight into the choice of input features is an important aspect for implementing machine learning methods. Artificial intelligence ; Feature (linguistics) ; Ising model ; Feature vector ; Machine learning ; Embedding ; Computer science ; k-nearest neighbors algorithm ; Phase transition ; Algorithm ; Statistical physics ; Mathematics ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Fabian Jirasek | Robert Bamler | Sophie Fellenz | Michael Bortz | Marius Kloft | Stephan Mandt | Hans Hasse
Source:  (preprint, 2022-04-11)
Field: Materials Science — Materials Chemistry

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies, as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machinelearning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model. UNIQUAC ; UNIFAC ; Binary number ; Computer science ; Gibbs free energy ; Physical system ; Matrix (chemical analysis) ; Artificial intelligence ; Machine learning ; Thermodynamics ; Activity coefficient ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; Process Optimization and Integration ; Crystallization and Solubility Studies
Authors: Yu Fu | Chen Liu | Yunkun Song | Hai Hao | Yongdong Xu | Zhiwen Shao | Jun Wang | Xiurong Zhu
Source: Crystals (article, 2022-09-01)
Field: Materials Science — Biomaterials

Modeling of Microstructure and Mechanical Properties of Heat Treated ZE41-Ca-Sr Alloys for Integrated Computing Platform The main objective of this study is to present a methodology to model the microstructure and mechanical properties of ZE41-xCa-ySr alloys for integrated optimization calculation of the heat treatment process of gearbox casting. Firstly, the models of microstructure and mechanical properties of ZE41-xCa-ySr alloys (0 ≤ x ≤ 2, 0 ≤ y ≤ 0.2) are developed using an artificial neural network (ANN) and multivariate regression. The dataset for ANN and regression models is generated by investigating the microstructures and mechanical properties of the ZE41-xCa-ySr alloys. The inputs for ANN and regression models are Ca and Sr contents, aging temperature and aging time. The outputs are grain size, ultimate tensile strength, elongation and microhardness. The optimal ANN model is obtained by testing the performance of different network architectures. In addition, multivariate regression models have been built based on the Least Squares method. Secondly, based on SiPESC software, an Integrated Computing Platform is constructed by combining the scripting language with the command line operation of simulation software, realizing the “process—microstructure—property” optimization calculation. Finally, based on the developed regression model, an Integrated Computing Platform batch called MATLAB achieves the heat treatment process optimization based on mechanical property prediction. The optimum aging temperature of the ZE41-0.17Ca-0.2Sr alloy is 322 °C, and the corresponding aging time is 11 h. Furthermore, the optimized results are validated by the ANN model, suggesting that ANN predicted results are in good agreement with optimized results. As a consequence, this work provides a new strategy for the research and development of Mg alloys, contributing to acceleration in the development of magnesium alloys. Microstructure ; Artificial neural network ; Multivariate statistics ; Computer science ; Ultimate tensile strength ; Indentation hardness ; MATLAB ; Scripting language ; Materials science ; Computational science ; Artificial intelligence ; Machine learning ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Linlin Huang | Yuan Fang | Zenan Lin | Shengyu Shi | Heng Wu | Xianrong Liang | Mengmeng Wang | Gang Jin
Source: Applied Spectroscopy (article, 2022-01-27)
Field: Chemistry — Analytical Chemistry

A Component Content Measurement Method Modified Using Indirect Hard Modeling for Polymer Blends Based on Raman Spectroscopy Raman spectroscopy has been proven to be useful for the component content measurement of polymer blends. However, the soft modeling methods commonly used in quantitative analysis of Raman spectroscopy require a large number of training samples, resulting in a waste of materials and time. This work adopted a modified indirect hard modeling (IHM) method to measure the component content of polymer blends based on Raman spectroscopy. The Raman spectra of polypropylene (PP)/polystyrene (PS) blends with different component content were collected and resolved into the sum of multiple Voigt peak functions. For a large number of peak parameters, the two-dimensional correlation spectroscopy was used to screen out the characteristic Voigt peaks highly correlated with component content to reduce the parameter dimensions and build the parameterized spectral models. The spectral model of the blend was expressed as the weighted sum of the pure component spectral models, during which the parameters of the pure component models were adjusted within a range. According to the relationship between the weight and content of the pure component, a linear regression model for component content prediction was established. The coefficient of determination (R<sup>2</sup>)/root mean squared error of the IHM component content prediction model was 0.9931/0.4367 wt%. Besides, two popular soft modeling methods, partial least squares and artificial neural network, were compared with the IHM method, which showed that the IHM model had higher prediction accuracy with fewer training samples. Raman spectroscopy ; Component (thermodynamics) ; Partial least squares regression ; Content (measure theory) ; Biological system ; Analytical Chemistry (journal) ; Spectroscopy ; Materials science ; Chemometrics ; Polypropylene ; Parameterized complexity ; Principal component analysis ; Component analysis ; Chemistry ; Mathematics ; Optics ; Thermodynamics ; Chromatography ; Statistics ; Composite material ; Algorithm ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Otávio Bianchi | Patrícia Bereta Pereira | Carlos Ferreira
Source: Preprints.org (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

Mechanochemical Treatment in High-Shear Thermokinetic Mixer an Alternative for Tire Recycling This publication highlights the effect of mechanochemical treatment in a high-speed thermoki-netic mixer as an alternative for recycling ground tire rubber (GTR). The GTR initially has an 80% gelled fraction and presented up to 50% gel fraction in the most intensive condition (5145 rpm, s2). Processing at the lowest speed (2564 rpm, s1) resulted in greater selectivity concerning the mechanochemical treatment (K~1). In the most intense processing condition (10 min. at s2), more significant degradation was observed via random scission with a reduction of the glass transition temperature, Tg (11&amp;ordm;C), an increase of the soluble polymer fraction, and a more signif-icant reduction in crosslink density. The artificial neural network could describe and correlate the thermal degradation profile and processing conditions with the physicochemical characteris-tics of the GTR. The approach presented here represents an alternative for mechanochemical treatment since it can reduce the crosslink density with selectivity and in short times (1-3 min.). Fraction (chemistry) ; Degradation (telecommunications) ; Natural rubber ; Materials science ; Polymer ; Chemical engineering ; Shear (geology) ; Thermal treatment ; Composite material ; Chemistry ; Chromatography ; Engineering ; Electrical engineering Thermal and Kinetic Analysis ; thermodynamics and calorimetric analyses ; Calcium Carbonate Crystallization and Inhibition
Authors: Bouchaib Zazoum
Source: Materials science forum (article, 2022-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Enabled Prediction of Electromagnetic Interference Shielding Effectiveness of Poly(Vinylidene Fluoride)/Mxene Nanocomposites Using machine learning (ML) approaches for the design and manufacturing of materials becomes an emerging technology that may possibly allow us to systematically discover novel materials with promising electromagnetic interference (EMI) shielding properties. Herein, we explored the correlation between input variables such as MXene loading, thickness of nanocomposites films, frequency, and predicted EMI shielding effectiveness (ES) of poly (vinylidene fluoride)/MXene (PVDF/MXene) nanocomposites materials via ML. Two different models of ML including Gaussian process regression (GPR) and support vector machine (SVM) were considered and compared. The results showed that the predicted data by the two models are in good agreement with the experimental values, indicating that the developed ML models are appropriate for predicting properties of nanocomposites materials for EMI shielding applications. EMI ; Materials science ; Electromagnetic shielding ; Electromagnetic interference ; Nanocomposite ; Fluoride ; Kriging ; Composite material ; Support vector machine ; Interference (communication) ; Machine learning ; Electronic engineering ; Computer science ; Engineering Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Amirreza Hashemi | Ruiqiang Guo | Keivan Esfarjani | Sangyeop Lee
Source: Physical Review Materials (article, 2022-04-14)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> phonon transport across grain boundaries in graphene using machine learning based on small dataset Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next-generation functional materials but has been severely hampered due to its extremely large configurational space. Atomistic simulations with low computational cost and high predictive power are strongly desirable, but the conventional simulations using empirical interatomic potentials and density functional theory suffer from the lack of predictive power and high computational cost, respectively. A machine learning interatomic potential (MLIP) recently emerged but often requires extensive size of the training dataset, making it a less feasible approach. Here, we demonstrate that an MLIP trained with a rationally designed small training dataset can predict thermal transport across GBs in graphene with ab initio accuracy at an affordable computational cost. We employed a rational approach based on the structural unit model to find a small set of GBs that can represent the entire configurational space and thus can serve as a cost-effective training dataset for the MLIP. Only 5 GBs were found to be enough to represent the entire configurational space of graphene GBs. Using the atomistic Green's function approach and the MLIP, we revealed that the structure-thermal resistance relation in graphene does not follow the common understanding that large dislocation density causes larger thermal resistance. In fact, thermal resistance is nearly independent of dislocation density at room temperature and is higher when the dislocation density is small at sub-room temperature. We explain this intriguing behavior with the buckling near a GB causing a strong scattering of flexural phonon modes. In this paper, we show that a machine learning technique combined with conventional wisdom (e.g., structural unit model) can extend the recent success of ab initio thermal transport simulation, which has been mostly limited to single crystals, to complex yet practically important polycrystals with GBs. Graphene ; Phonon ; Materials science ; Ab initio ; Dislocation ; Grain boundary ; Density functional theory ; Thermal conductivity ; Interatomic potential ; Condensed matter physics ; Machine learning ; Statistical physics ; Computer science ; Molecular dynamics ; Nanotechnology ; Microstructure ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications