jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kazuya Hiraide | Yutaka Oya | Kenta Hirayama | Katsuhiro Endo | Mayu Muramatsu
Source: Advanced Composite Materials (article, 2024-02-24)
Field: Materials Science — Materials Chemistry

Development of a deep-learning model for phase-separation structure of diblock copolymer based on self-consistent field analysis Self-consistent field (SCF) analysis is an indispensable tool for predicting the microphase separation structures of polymer alloys.However, the computation of the phase-separated structures in the equilibrium state is computationally intensive, leading to high costs.To address this challenge, we propose a novel deep learning approach that leverages a generative adversarial network (GAN), a powerful deep generative model, to accelerate SCF analysis.Specifically, we trained the GAN using comprehensive data obtained from SCF analysis, enabling us to generate various images of feasible structures that are similar to the SCF analysis results.Our results demonstrate that the latent variables in the GAN are linked to the physical parameters and features of the phase-separation structures. Materials science ; Copolymer ; Phase (matter) ; Field (mathematics) ; Polymer science ; Composite material ; Chemical physics ; Polymer ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Electron and X-Ray Spectroscopy Techniques
Authors: Huaizhong Zhang | Kristen A. Fichthorn
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Quantifying temperature- and composition-dependent structures of AgCu nanocrystals using machine learning Cluster analysis ; Mixture model ; Silhouette ; Nanoparticle ; Materials science ; Principal component analysis ; Dimensionality reduction ; Artificial intelligence ; Computer science ; Nanotechnology Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Computational Drug Discovery Methods
Authors: Elham Einabadi | Mahdi Mashkoori
Source: Scientific Reports (article, 2024-10-15)
Field: Chemistry — Analytical Chemistry

Predicting refractive index of inorganic compounds using machine learning Refractive index (RI) is one of the most important optical properties of materials. Due to the high importance of this physical parameter, there has always been a demand to find a method that provides the most optimal estimation. In this research, we utilize experimentally measured RI values of 272 inorganic compounds to build a machine learning model capable of predicting the RI of materials with low computational cost. Considering the significant relationship between the band gap and RI, we select this parameter as a predictor. In addition to the band gap, the atomic properties related to the building elements of the compounds form our data set in this work. To find the most optimal model and set of suitable predictors, we examine our data in four categories with 1, 5, 10, and 21 features. In addition, we compare the predicted RIs of 6 different independent regression methods, namely, ordinary least squares (OLSR), Gaussian process (GPR), support vector (SVR), random forest (RFR), gradient boosted trees (GBTR), and extremely randomized trees regression(ERTR). We notice that ERTR predicts RI with the highest accuracy compared to other regression methods. The prediction strength of our model excels in empirical relations and provides accurate results for a wide range of RIs. Thus, we demonstrate the high potential of machine learning methods for evaluating the RI, especially when it comes to providing an estimation of a desired physical quantity. Refractive index ; Index (typography) ; Computer science ; Machine learning ; Materials science ; World Wide Web ; Optoelectronics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Text and Document Classification Technologies
Authors: Arthur Hardiagon | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2024-03-12)
Field: Chemistry — Inorganic Chemistry

Multi-Scale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal and Adsorption Behavior Nanoporous frameworks are a large and diverse family of materials, with a key role in various industrial processes and applications such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both its physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multi-scale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption. For the study of the mechanical behavior of nanoporous materials, the last few years have seen a rapid acceleration of research. For example, computational resources have been pooled to created public large-scale databases of elastic constants: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models. The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such large scale. Tentative databases have been assembled at the DFT level on specific families of materials, like zeolites, but prediction at larger scale currently requires the use of transferable, classical force fields, whose accuracy can be limited. Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties, including thermodynamic quantities and transport properties. Finally, we stress the importance for data-based methods of addressing all sources of uncertainty. The Account concludes with some perspectives about the latest developments and open questions in data-based approaches, and the integration of computational and experimental data together in the materials discovery loop. Nanoporous ; Scale (ratio) ; Adsorption ; Materials science ; Nanotechnology ; Computer science ; Process engineering ; Engineering ; Chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Jihoon Chung | Junru Zhang | Amirul Islam Saimon | Yang Liu | Blake N. Johnson | Zhenyu Kong
Source: Research Square (preprint, 2024-01-23)
Field: Chemistry — Analytical Chemistry

Imbalanced Spectral Data Analysis using Data Augmentation based on the Generative Adversarial Network <title>Abstract</title> Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 7%, 5%, and 5% of the performance of the existing data augmentation methods regarding the classifier’s F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method’s broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering. Discriminator ; Computer science ; Classifier (UML) ; Artificial intelligence ; Generative grammar ; Artificial neural network ; Machine learning ; Pattern recognition (psychology) ; Data mining Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Fault Detection and Control Systems
Authors: Tawani Lorena Naide Acosta | Helena Cristina Vieira | Joielan Xipaia dos Santos | Washington Duarte Silva da Silva | Rafaela Stange | Deivison Venício Souza | Pedro Henrique González de Cademartori | Graciela Inês Bolzón de Muñiz | Silvana Nisgoski
Source: Wood Material Science and Engineering (article, 2024-03-18)
Field: Chemistry — Organic Chemistry

'Marfim pattern’ of wood species in the Southern Brazilian timber market: colorimetry and visible spectra Evaluating wood with similar characteristics is a challenging task. Taking into consideration the need for an efficient and rapid technique, the use of visible spectrophotometry to discriminate wood species is proposed. This study aimed to differentiate nine wood species with a similar yellowish color using visible spectrophotometry, associated with two classification models, to verify predictions. Color data were obtained from different anatomical sections of the wood, along with reflectance spectra in the range of 360–740 nm. Two machine learning approaches were applied: artificial neural network (ANN) and k-nearest neighbors (k-NN). In general, the mean spectra of the samples shared similar information, as they are materials of lignocellulosic origin. However, in all tests, the models built using the ANN algorithm demonstrated an accuracy in discriminating species exceeding 97%. Notably, when applying ANN to the spectra obtained solely from the longitudinal surface, superior results were obtained. Discriminative patterns can be obtained by visible spectra independent of the anatomical section. The integration of visible spectra and ANN proved to be a suitable approach to recognize "marfim pattern" wood. Spectrophotometry ; Reflectivity ; Discriminative model ; Colorimetry ; Artificial neural network ; Spectral line ; Artificial intelligence ; Pattern recognition (psychology) ; Range (aeronautics) ; Materials science ; Computer science ; Biological system ; Analytical Chemistry (journal) ; Remote sensing ; Optics ; Chemistry ; Computer vision ; Chromatography ; Composite material ; Geology ; Physics ; Biology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Wood Treatment and Properties
Authors: C. Ray | Yi Yao | Shelby L. Galinat | Bennett Addison | Volker Blüm | Annalise E. Maughan
Source: Chemistry of Materials (article, 2024-09-25)
Field: Chemistry — Inorganic Chemistry

Site Disorder Drives Cyanide Dynamics and Fast Ion Transport in Li<sub>6</sub>PS<sub>5</sub>CN Halide argyrodite solid-state electrolytes of the general formula Li<sub>6</sub>PS<sub>5</sub> <i>X</i> exhibit complex static and dynamic disorder that plays a crucial role in ion transport processes. Here, we unravel the rich interplay between site disorder and dynamics in the plastic crystal argyrodite Li<sub>6</sub>PS<sub>5</sub>CN and the impact on ion diffusion processes through a suite of experimental and computational methodologies, including temperature-dependent synchrotron powder X-ray diffraction, AC electrochemical impedance spectroscopy, <sup>7</sup>Li solid-state NMR, and machine learning-assisted molecular dynamics simulations. Sulfide and (pseudo)halide site disorder between the two anion sublattices unilaterally improves long-range lithium diffusion irrespective of the (pseudo)halide identity, which demonstrates the importance of site disorder in dictating bulk ionic conductivity in the argyrodite family. Furthermore, we find that anion site disorder modulates the presence and time scales of cyanide rotational dynamics. Ordered configurations of anions enable fast, quasi-free rotations of cyanides that occur on time scales of 10<sup>11</sup> Hz at <i>T</i> = 300 K. In contrast, we find that cyanide dynamics are slow or frozen in Li<sub>6</sub>PS<sub>5</sub>CN when site disorder between the cyanide and sulfide sublattices is present at <i>T</i> = 300 K. We rationalize the observed differences in cyanide dynamics in the context of elastic dipole interactions between neighboring cyanide anions and local strain induced by the configurations of site disorder that may impact the energetic landscape for cyanide rotational dynamics. Through this study, we find that anion disorder plays a decisive role in dictating the extent and time scales of both lithium ion and cyanide dynamics in Li<sub>6</sub>PS<sub>5</sub>CN. Halide ; Ion ; Cyanide ; Electrolyte ; Inorganic chemistry ; Fast ion conductor ; Materials science ; Solid-state ; Chemical physics ; Chemistry ; Physical chemistry ; Electrode ; Organic chemistry Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Abuchi Elebo | Uba Sani | Patricia A. Ekwumemgbo | Victor O. Ajibola
Source: Biosensors and nanotheranostics. (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Green Synthesis and Zinc-Oxide Nanoparticles for Corrosion Inhibition and Modeling Corrosion Inhibition of Mild Steel in HCl Solutions Background: The expanding field of nanotechnology has led to significant interest in green chemistry approaches for synthesizing metal nanoparticles, which offer environmentally friendly, cost-effective, and recyclable solutions. Among these, zinc-oxide nanoparticles (ZnONPs) have gained attention for their potential as corrosion inhibitors. This study introduces a novel green synthesis of ZnONPs using Opuntia fragilis leaves (OFL) and explores their effectiveness in inhibiting the corrosion of mild steel in hydrochloric acid (HCl) solutions. Methods: ZnONPs were biosynthesized using OFL, and their physicochemical properties were characterized through SEM, TEM, XRD, EDX, UV-Vis, and FT-IR spectroscopy. The nanoparticles' average size, shape, elemental composition, and crystallinity were determined. The corrosion inhibition potential of OFL-ZnONPs on mild steel in various HCl concentrations was evaluated using weight loss measurements, electrochemical impedance spectroscopy, and potentiodynamic polarization. Numerical optimization was performed using response surface methodology (RSM) and artificial neural network (ANN) models to predict and optimize the process parameters influencing corrosion inhibition. Results: The characterization revealed ZnONPs with an average internal size of 15 nm, external size of 25 nm, hexagonal shape, and a crystallinity of 68.14%. Elemental analysis showed high zinc and oxygen content (75.23% and 23.45%, respectively). Corrosion inhibition studies indicated that as the inhibitor concentration increased, weight loss decreased, resulting in higher inhibition efficiency. RSM optimization yielded a maximum inhibition efficiency of 77.31% under specific conditions (2 M HCl, 4.75 hours, 0.4 g/L inhibitor concentration, and 324.5 K). ANN optimization identified the optimal neuron number as 9, with a mean squared error (MSE) of 0.6053, confirming the robustness of the model. Conclusion: This study demonstrates the successful green synthesis of ZnONPs using OFL and their effective application as corrosion inhibitors for mild steel in HCl solutions. The use of RSM and ANN for process optimization highlighted the robustness and predictive accuracy of these models, paving the way for environmentally friendly and efficient corrosion inhibition strategies in industrial applications. Zinc ; Corrosion ; Nanoparticle ; Corrosion inhibitor ; Chemistry ; Nuclear chemistry ; Metallurgy ; Materials science ; Inorganic chemistry ; Nanotechnology Corrosion Behavior and Inhibition ; Magnesium Oxide Properties and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Hartmut Maennel | Oliver T. Unke | Klaus-Robert Müller
Source: The Journal of Physical Chemistry Letters (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties When physical properties of molecules are being modeled with machine learning, it is desirable to incorporate <i>SO</i>(3)-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness properties for higher order methods and show that 6<i>k</i> - 5 of these features are enough for up to <i>k</i> atoms. We also find that the Clebsch-Gordan operations commonly used in these methods can be replaced by matrix multiplications without sacrificing completeness, lowering the scaling from <i>O</i>(<i>l</i><sup>6</sup>) to <i>O</i>(<i>l</i><sup>3</sup>) in the degree of the features. We apply this to quantum chemistry, but the proposed methods are generally applicable for problems involving three-dimensional point configurations. Point (geometry) ; Quantum ; Statistical physics ; Theoretical physics ; Computer science ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Ihtesham Ibn Malek | Koushik Sarkar | Ahmed Zubair
Source:  (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

Efficient prediction of mechanical characteristics of single wall carbon nanotubes Single wall carbon nanotubes (SWCNTs) have garnered significant attention due to their exceptional mechanical properties, which vary with chiral indices (n, m). While molecular dynamics (MD) simulations provide accurate assessments, they are computationally expensive. Machine learning (ML) methods offer a faster alternative while maintaining high predictive accuracy. In this study, we employed a random forest (RF) model on two datasets of pristine SWCNTs, utilizing the chiral index (n) and chiral angle $(\theta)$ as features. The RF model applied to the first dataset achieved test root mean squared error (RMSE) values of 0.0028, 0.0056, and 0.0029 for predicting Young’s modulus, fracture strain, and ultimate strength, respectively. In contrast, the RF model on the second dataset yielded RMSE values of 0.0027,0.0055, and 0.003 for these parameters. By fitting the predicted values of the second dataset into a second-order polynomial, we obtained stressstrain curves with an average $R^{2}$ of 0.9999 and an RMSE of 0.006, surpassing prior neural network models while significantly reducing computational time. Carbon nanotube ; Materials science ; Nanotechnology Carbon Nanotubes in Composites ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Zhihao Qu | Kexin Cheng | Xue Jiang | Zhu Wang | Yanjing Su | Lei Zhang
Source: Materials Today Communications (article, 2024-07-22)
Field: Materials Science — Metals and Alloys

A hybrid machine learning strategy for pitting probability prediction of stainless steels Materials science ; Metallurgy ; Pitting corrosion ; Machine learning ; Alloy ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Fatigue and fracture mechanics
Authors: Yifeng Bie | Shuai You | Xinrui Li | Xuekui Zhang | Tao Lű
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence (article, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. First, a typical machine learning model requires a large number of training samples that physical systems can not provide. Second, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction. Principal component analysis ; Computer science ; Artificial intelligence ; Training (meteorology) ; Pattern recognition (psychology) ; Principal (computer security) ; Machine learning ; Geography Spectroscopy and Chemometric Analyses
Authors: Jacob P. Tavenner | Ankit Gupta | Gregory B. Thompson | Edward M. Kober | Garritt J. Tucker
Source: Communications Materials (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Learning grain boundary segregation behavior through fingerprinting complex atomic environments Although continuum-scale segregation is a well-documented behavior in multi-species materials, detailed site-specific behavior remains largely unexplored. This is partially due to the complexity of analyzing materials at the requisite time and length scales for describing segregation with full atomic accuracy. Here, we better evaluate the segregation behavior of disordered grain boundary (GB) atomic environments through leveraging a set of Strain Functional Descriptors (SFDs) to generate an atomic descriptor (i.e., fingerprint). Using this atomic fingerprint, we resolve key relationships between atomic structure and segregation energy. Machine learning (ML) techniques are utilized in concert with this SFD fingerprint to elucidate complex relationships relating segregation potential to changes in specific features of the local Gaussian density captured by the SFDs. Finally, we identify relationships that indicate both individual and joint structure-property correlations. Linking atomic segregation energy to key structural features demonstrates the value of higher-order descriptors for uncovering complex structure-property relationships at an atomic scale. Describing site-specific segregation in multi-species materials is a computationally complex task that typically requires model simplification, at the expense of atomic accuracy, or limitation to small samples. Here, the relationships between local atomic environments at grain boundaries and their segregation energies are investigated by developing suitable machine learning atomic descriptors. Grain boundary ; Materials science ; Metallurgy ; Microstructure Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis