jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chukwutoo Christopher Ihueze | Okoli Ndubuisi Celestine | Uchendu Onwusoronye Onwurah | Christian Emeka Okafor | Queeneth Adesuwa Kingsley-Omoyibo
Source: Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (article, 2023-04-12)
Field: Materials Science — Polymers and Plastics

Intelligent assessment of quality characteristics of <i>miscanthus</i> fiber-reinforced polypropylene for sustainable products development This study aims at determining the optimal material and process parameters that will maximize the quality characteristics of miscanthus fiber-reinforced polypropylene (MFRPP) composite. Taguchi L 16 was utilized in the design of experiment and larger-the-better signal-to-noise ratio was used in the analysis of the impact strength of MFRPP. Optimal combination of material and process parameters and the main effects were determined, and the significant variables were identified using analysis of variance. Artificial neural network (ANN) and extreme learning machine (ELM) were utilized on the Taguchi experimental data and used in the prediction of the impact strength of MFRPP. The results showed the optimum impact strength occurred at 25 wt% miscanthus fiber loading. The results of the predictions made revealed that both ANN and ELM are very efficient in predicting the impact strength of MFRPP as shown by the relative errors when compared with the experimental data. Both the performance metrics assessments and the quality of prediction show that ELM is a better machine learning technique than ANN. The high impact strength of MFRPP composite is a confirmation that MFRPP is a very useful material for industrial applications. Taguchi methods ; Miscanthus ; Artificial neural network ; Polypropylene ; Izod impact strength test ; Extreme learning machine ; Fiber ; Composite number ; Materials science ; Orthogonal array ; Computer science ; Process engineering ; Composite material ; Environmental science ; Machine learning ; Engineering ; Ultimate tensile strength ; Bioenergy ; Renewable energy Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Advanced Cellulose Research Studies
Authors: Ryan Jacobs | Jian Liu | Harry Abernathy | Dane Morgan
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Machine Learning Design of Perovskite Catalytic Properties Discovering new materials that efficiently catalyze the oxygen reduction and evolution reactions is critical for facilitating the widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) technologies. Here, we develop machine learning (ML) models to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surface exchange, oxygen diffusivity, and area specific resistance (ASR). The models are based on trivial-to-calculate elemental features and are more accurate and dramatically faster than the best models based on ab initio-derived features, potentially eliminating the need for ab initio calculations in descriptor-based screening. Our model of ASR enables temperature-dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross-validation reveals our model to be effective at discovering new promising materials prior to their initial discovery, demonstrating our model can make meaningful predictions. Using the SHapley Additive ExPlanations (SHAP) approach, we provide detailed discussion of different approaches of model featurization for ML property prediction. Finally, we use our model to screen more than 19 million perovskites to develop a list of promising cheap, earth-abundant, stable, and high performing materials, and find some top materials contain mixtures of less-explored elements (e.g., K, Bi, Y, Ni, Cu) worth exploring in more detail. Perovskite (structure) ; Ab initio ; Oxide ; Computer science ; Fuel cells ; Oxygen ; Catalysis ; Thermal diffusivity ; Materials science ; Artificial intelligence ; Thermodynamics ; Chemistry ; Chemical engineering ; Physics ; Metallurgy Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion
Authors: Xiaoli Chen | Phoebus Rosakis | Zhizhang Wu | Zhiwen Zhang
Source: Computer Methods in Applied Mechanics and Engineering (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach Maxima and minima ; Computation ; Energy minimization ; Minification ; Artificial neural network ; Deep learning ; Computer science ; Function (biology) ; Energy (signal processing) ; Mathematical optimization ; Applied mathematics ; Algorithm ; Artificial intelligence ; Mathematics ; Mathematical analysis ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Topology Optimization in Engineering
Authors: Chang Wang | Xinyu Liu | Yang Zhang | Yan Sun | Zeqing Yu | Zhenrong Zheng
Source: Nanomaterials (article, 2023-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO<sub>2</sub>) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between <i>x</i>-polarization and <i>y</i>-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on. Hyperspectral imaging ; Compressed sensing ; Spectral imaging ; Full spectral imaging ; Optics ; Materials science ; Computer science ; Iterative reconstruction ; Data cube ; Scanner ; Optoelectronics ; Artificial intelligence ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Random lasers and scattering media
Authors: Lakshmi Sujeeun | Nowsheen Goonoo | Kaylina Marie Moutou | Shakuntala Baichoo | Archana Bhaw‐Luximon
Source: Macromolecular Research (article, 2023-04-01)
Field: Materials Science — Biomaterials

Predictive modeling as a tool to assess polymer–polymer and polymer–drug interactions for tissue engineering applications Polymer ; Materials science ; Miscibility ; Polymer blend ; Biopolymer ; Fourier transform infrared spectroscopy ; Chemical engineering ; Biological system ; Biomedical engineering ; Composite material biodegradable polymer synthesis and properties ; Enzyme Production and Characterization ; Seaweed-derived Bioactive Compounds
Authors: Di Luo | Aidan P. Reddy | Trithep Devakul | Liang Fu
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Artificial intelligence for artificial materials: moiré atom Moiré engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moiré superlattice potential minimum (the moiré atom) using a 2D fermionic neural network. We show that strong Coulomb interactions in combination with the anisotropic moiré potential lead to striking ``Wigner molecule" charge density distributions observable with scanning tunneling microscopy. Moiré pattern ; van der Waals force ; Superlattice ; Atom (system on chip) ; Condensed matter physics ; Scanning tunneling microscope ; Quantum tunnelling ; Electron ; Quantum ; Heterojunction ; Coulomb ; Observable ; Physics ; Materials science ; Nanotechnology ; Quantum mechanics ; Molecule ; Optics ; Computer science Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Xiaoyang Wang | Zhenyu Wang | Pengyue Gao | Chengqian Zhang | Jian Lv | Handong Wang | Haifeng Liu | Yanchao Wang | Yanming Ma
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Complex Crystal Structures of Dense Lithium Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for new yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal phases containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the newly observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine-learning potentials. Crystal structure prediction ; Phase diagram ; Crystal structure ; Lithium (medication) ; Crystal (programming language) ; Materials science ; Phase (matter) ; Chemical physics ; Crystallography ; Computer science ; Chemistry Machine Learning in Materials Science ; History and advancements in chemistry ; Data Quality and Management
Authors: Vishal Sorathiya | Umang Soni | Vipul Vekariya | Jaysheel Golani | Abdulkarem H. M. Almawgani | Adam R. H. Alhawari
Source: Plasmonics (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Borophene-Ge2Sb2Te5 (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning Materials science ; Refractive index ; Mean squared error ; Optics ; Grating ; Wavelength ; Root mean square ; Artificial neural network ; Optoelectronics ; Physics ; Computer science ; Mathematics ; Machine learning Phase-change materials and chalcogenides ; Photonic and Optical Devices
Authors: Suyuan Chen | Chunguang Liang | Hongli Li | Weimeng Yu | Michaela Prothiwa | Dominik Kopczynski | Stefan Loroch | Marc Fransen | Steven H. L. Verhelst
Source: ACS Chemical Biology (article, 2023-03-15)
Field: Chemistry — Organic Chemistry

Pepstatin-Based Probes for Photoaffinity Labeling of Aspartic Proteases and Application to Target Identification Aspartic proteases are a small class of proteases implicated in a wide variety of human diseases. Covalent chemical probes for photoaffinity labeling (PAL) of these proteases are underdeveloped. We here report a full on-resin synthesis of clickable PAL probes based on the natural product inhibitor pepstatin incorporating a minimal diazirine reactive group. The position of this group in the inhibitor determines the labeling efficiency. The most effective probes sensitively detect cathepsin D, a biomarker for breast cancer, in cell lysates. Moreover, through chemical proteomics experiments and deep learning algorithms, we identified sequestosome-1, an important player in autophagy, as a direct interaction partner and substrate of cathepsin D. Pepstatin ; Proteases ; Biochemistry ; Chemistry ; Photoaffinity labeling ; Diazirine ; Cathepsin ; Cathepsin D ; Cathepsin O ; Natural product ; Cathepsin A ; Affinity label ; Protease ; Enzyme ; Receptor Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Peptidase Inhibition and Analysis
Authors: Ming‐Sheng Zheng | Shaojie Zhou | Xinmo Wang | Lei Gao
Source: Journal of Applied Physics (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Understanding epitaxy of graphene: From experimental observation to density functional theory and machine learning Due to enormous applications of large-area graphene with high quality, the epitaxial growth strategies have drawn a plethora of attention. However, the bottleneck in the production of graphene has caused delayed development in recent years, which is owing to the poor understanding of interaction mechanisms between graphene and the underlying metallic and non-metallic substrate. To understand the thermodynamics of graphene–substrate interface and growth kinetics, accurate density functional theory (DFT) calculations have been proved as an effective way, in terms of cost and time, compared with traditional experimental methods, which can calculate the interaction between graphene and substrates, helping us to better understand the practical phenomena. Here, we show the use of DFT methods to evaluate both van der Waals interaction and covalent bonding. Many of computational results fit well with the experimental observations. To address the relative low accuracy and small computation capacity (number of atoms) of common DFT models, we suggest that the machine learning (ML) methods will be a fresh impetus for epitaxial growth strategy of graphene, which put forward effective interpretations for complicated interconnections and correlations among the properties, thereby enabling ML a promising strategy for understanding, design, and synthesis of graphene over other 2D materials. Graphene ; Density functional theory ; Materials science ; van der Waals force ; Bottleneck ; Substrate (aquarium) ; Nanotechnology ; Computation ; Epitaxy ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Molecule ; Algorithm ; Quantum mechanics Graphene research and applications ; Advancements in Battery Materials ; 2D Materials and Applications
Authors: Aayushi Arya
Source: Engineering Technology & Applied Science Research (article, 2023-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of a Graphical User Interface for Reflection Loss Calculation in Perovskite-RGO based Microwave Absorbing Composites In this paper, a novel method is investigated wherein the theoretical and mathematical analysis of the perovskite-Reduced Graphene Oxide (RGO) based composite microwave absorber is used to form a machine learning model using linear regression to predict the reflection loss and the effective dielectric permittivity of a selected perovskite compound in an RGO-based composite. At first, the theoretical derivation is carried out to find a mathematical relationship between the reflection loss and the dielectric permittivity of the composite and the cationic radii of the perovskite structure, which is then used to form the base for the machine learning model to directly calculate the microwave absorption characteristics from the atomic parameters of the given composite structure. Linear regression is used for the machine learning algorithm which is verified with an R2 of 0.869 with the atomic radii as the input parameters. The model is further used to develop a Graphical User Interface (GUI) to make the prediction more appealing and user-friendly. The current paper provides a new approach to the integration of theoretical knowledge with advanced computing tools to form innovative predictive tools for current microwave-absorbing materials. Permittivity ; Perovskite (structure) ; Microwave ; Reflection (computer programming) ; Materials science ; Graphene ; Composite number ; Dielectric ; Reflection loss ; Graphical user interface ; Dielectric loss ; Composite material ; Computer science ; Machine learning ; Algorithm ; Optoelectronics ; Nanotechnology ; Chemical engineering ; Engineering Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Structural Behavior of Reinforced Concrete
Authors: Dong‐Hwan Yang | Yuseong Chu | Odongo Francis Ngome Okello | Seung‐Young Seo | Gunho Moon | Kwang Ho Kim | Moon‐Ho Jo | Dongwon Shin | Teruyasu Mizoguchi | Sejung Yang | Si‐Young Choi
Source: Materials Horizons (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe<sub>2</sub>: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into two unit cells elaborately cropped the unit cells: further separating a unit cell input into the Te<sub>2</sub>/Mo column part remarkably increased the defect classification accuracies. The concentrations of identified point defects were 7.16 × 10<sup>20</sup> cm<sup>2</sup> of Te monovacancies, 4.38 × 10<sup>19</sup> cm<sup>2</sup> of Te divacancies and 1.46 × 10<sup>19</sup> cm<sup>2</sup> of Mo monovacancies generated during an exfoliation process for TEM sample-preparation. These revealed defects correspond to the n-type character mainly originating from Te monovacancies, statistically. Our deep learning-oriented platform combined with atomic structural imaging provides the most intuitive and precise way to analyse point defects and, consequently, insight into the defect-property correlation based on deep learning in 2D materials. Dual mode ; Dual (grammatical number) ; Transition metal ; Transition point ; Point (geometry) ; Algorithm ; Automation ; Materials science ; Mode (computer interface) ; Nanotechnology ; Computer science ; Artificial intelligence ; Engineering ; Electronic engineering ; Mechanical engineering ; Chemistry ; Mathematics ; Aerospace engineering ; Geometry 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Merlin Knaeble | Gabriel Sailer | Zihan Chen | Thorsten Schwarz | Kailun Yang | Mario Nadj | Rainer Stiefelhagen | Alexander Maedche
Source:  (article, 2023-04-22)
Field: Materials Science — Materials Chemistry

AutoChemplete - Making Chemical Structural Formulas Accessible Despite their interests, blind and low vision students avoid STEM subjects. Research attributes this to a lack of accessible material. Annotating STEM content such as chemical structural formulas requires expert domain knowledge, is time consuming, and frustrating. We conduct interviews with blind and low vision chemists and accessibility professionals to derive requirements for tool support. On this basis, we develop AutoChemplete, an interactive labeling tool for chemical structural formulas. It ingests images and uses machine learning to predict the molecule. With a similarity search in the solution space, we enable even novices to simply pick from options. From this we are able to generate accessible representations. We conduct fifteen think-aloud sessions with participants of varying domain expertise and find support of different annotation styles simultaneously. Not only does AutoChemplete strike a balance in skill-support, participants even find it entertaining. Domain (mathematical analysis) ; Similarity (geometry) ; Computer science ; Space (punctuation) ; Table (database) ; Human–computer interaction ; Annotation ; Domain knowledge ; Artificial intelligence ; Information retrieval ; Image (mathematics) ; Data mining ; Mathematics Machine Learning in Materials Science ; Online Learning and Analytics ; Web Data Mining and Analysis
Authors: Pietro Guccione | Domenico Diacono | Stefano Toso | Rocco Caliandro
Source: IUCrJ (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Towards the extraction of the crystal cell parameters from pair distribution function profiles The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%. Extraction (chemistry) ; Function (biology) ; Computer science ; Distribution (mathematics) ; Biological system ; Computational biology ; Data mining ; Chemistry ; Mathematics ; Biology ; Cell biology ; Chromatography ; Mathematical analysis X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; Crystal Structures and Properties