jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ganesh Arkanath | Nishad Gupta | Hasan Kurban | Parichit Sharma | Krishna Madhavan | Elham Khorasani Buxton | Mehmet Dalkılıç
Source:  (article, 2023-12-15)
Field: Chemistry — Physical and Theoretical Chemistry

Novel NBA Fantasy League driven by Engineered Team Chemistry and Scaled Position Statistics Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent players actually played in a team. Using individual player and team statistics, models can be trained to predict an outcome. But fans are left wanting more. To achieve a more realistic outcome, aspects of what makes live teams win need to be included: (1) transforming player statistics to reflect their relative importance with respect to a player position; (2) team chemistry (TC). In this work, we show a novel characterization of relative position statistics and a new description of TC. Drawn from the NBA’s API, we form a data set to determine whether a fantasy team makes the playoffs using almost two dozen features, including TC. Various Machine Learning models are trained on this data and the best-performing model is offered to the users through a web service. Users can not only inspect fantasy teams and their TC but can also simulate their match-ups with existing 2023 NBA teams and utilize performance visualizations to help improve their team creation process. Our web service can be accessed at https://dalkilic.luddy.indiana.edu/fantasyleague/, and the source code can be found at https://github.com/gany-15/nbafan. League ; Fantasy ; Position (finance) ; Computer science ; Artificial intelligence ; Physics ; Business Various Chemistry Research Topics ; History and advancements in chemistry ; Chemistry and Stereochemistry Studies
Authors: Binghua Shu
Source:  (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Research on the Intelligent Technology of Crystallization Separation in the Pharmaceutical Engineering The pharmaceutical industry relies heavily on crystallization as a pivotal separation unit operation crucial for enhancing drug quality. Despite its significance, the intricate nature of the crystallization process poses challenges for comprehensive description through conventional mechanistic models. Recognizing the pervasive influence of artificial intelligence (AI) across diverse domains, its integration into intelligent pharmaceutical production, particularly in the context of crystallization operations, assumes paramount importance. This research embarks on an exploration of cutting-edge applications involving process analysis techniques and machine learning algorithms in the context of crystallization operations. Through an in-depth examination of the crystallization process, the study aims to address the complexities associated with this critical unit operation. The objective extends beyond mere technological innovation; it aspires to revolutionize pharmaceutical production efficiency. By tapping into the power of AI, this research introduces a novel avenue for optimizing crystallization operations, thereby enhancing drug quality. The implications of this study extend beyond immediate applications; they serve as a foundational reference for the future intelligent evolution of the Chinese pharmaceutical industry. Anticipated outcomes include not only crucial insights for refining the crystallization operation process but also a catalyst for elevating the entire pharmaceutical sector towards intelligent, data-driven practices. The findings are poised to furnish essential support in the ongoing pursuit of operational efficiency, superior drug quality, and the overarching goal of transforming the pharmaceutical industry into an intelligent and technologically advanced domain. Context (archaeology) ; Pharmaceutical industry ; Computer science ; Process (computing) ; Process analytical technology ; Quality (philosophy) ; Data science ; Work in process ; Engineering ; Medicine ; Operations management Crystallization and Solubility Studies
Authors: Johannes J. Cartus | Andreas Jeindl | Anna Werkovits | Lukas Hörmann | Oliver T. Hofmann
Source: ChemRxiv (preprint, 2023-03-16)
Field: Materials Science — Materials Chemistry

Polymorphism mediated by electric fields: A first principles study on organic/inorganic interfaces Organic/inorganic interfaces are known to exhibit rich polymorphism, where different polymorphs often possess significantly different properties. Which polymorph forms during an experiment depends strongly on environmental parameters such as deposition temperature and partial pressure of the molecule to be adsorbed. To prepare desired polymorphs these parameters are varied. However, many polymorphs are difficult to access with the experimentally available temperature- pressure ranges. In this contribution, we investigate how electric fields can be used as an additional lever to make certain structures more readily accessible. On the example of tetracyanoethylene (TCNE) on Cu(111), we analyze how electric fields change the energy landscape of interface systems. TCNE on Cu(111) can form either lying or standing polymorphs, which exhibit significantly different work functions. We combine first-principles calculations with a machine-learning based structure search algorithm and ab-initio thermodynamics to demonstrate that electric fields can be exploited to shift the temperature of the phase transition between standing and lying polymorphs by up to 100 K. Polymorphism (computer science) ; Electric field ; Chemical physics ; Tetracyanoethylene ; Ab initio ; Chemistry ; Phase transition ; Molecule ; Materials science ; Crystallography ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; nanoparticles nucleation surface interactions
Authors: S. Patra | S. Dhar | Sanjib Kumar Acharyya
Source: Journal of The Institution of Engineers (India) Series D (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Finite Element Implementation of ANN-Based Constitutive Models for DSA in SS304 Viscoplasticity ; Finite element method ; Artificial neural network ; Range (aeronautics) ; Focus (optics) ; Computer science ; Materials science ; Software ; Constitutive equation ; Structural engineering ; Mechanical engineering ; Artificial intelligence ; Engineering ; Composite material ; Physics High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Numerical methods in engineering
Authors: Annabella Selloni
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) Oxide-Water Interfaces from Ab Initio Based Deep Neural Network Simulations Photo-electrocatalysis involving complex oxide-water interfaces is a highly promising technology for the sustainable production of fuels. However, probing these complex interfaces and gaining atomistic insights is still very challenging for current experimental methods, and is often only possible through accurate computational simulations. While first principles-based simulations provide the best accuracy, they are also limited to small system sizes and time-scales due to their high computational cost. Machine learned potentials that reproduce the accuracy of first-principles methods, while at the same time allowing the exploration of larger systems and timescales, are a promising approach to circumvent this issue. In this talk, I will discuss recent applications of deep neural network based molecular dynamics simulations to understand the structure and dynamics of interfacial water on photoelectrochemically relevant oxide surfaces. Focusing on TiO 2 and IrO 2 surfaces, we examine interfacial proton transfer and discuss how the water dissociation fraction and hydroxyl lifetimes depend on the surface atomic and electronic structures. Dissociation (chemistry) ; Oxide ; Computer science ; Ab initio ; Molecular dynamics ; Artificial neural network ; Electronic structure ; Nanotechnology ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physical chemistry Machine Learning in Materials Science
Authors: Víctor Pacheco‐Peña | Nader Engheta
Source:  (article, 2023-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

How to Exploit Light-Matter Interactions in Space (x,y,z) and Time (t) to Enable Spatiotemporal Effective Materials? Research and innovation for a full control of light-matter interactions has been facilitated by the fields of metamaterials, optics, and photonics over the past few years. With metamaterials and metasurfaces, for instance, it is possible to make use of subwavelength unit cells arranged in a periodic (or aperiodic) lattice and to achieve effective parameters of permittivity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon_{reff})$</tex> and permeability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mu_{reff})$</tex>. Such feature has opened new avenues in light-matter manipulation as it is possible to engineer effective parameters with extreme values including near-zero or negative [1–6]. Controlling electromagnetic waves with metamaterials and photonic structures has been an active research topic with many applications being explored such as sensing, waveguiding, machine learning based designs, computing and quantum technologies, among others [7–14]. All these applications have been developed in the time-harmonic scenario exploiting spatial inhomogeneities of material parameters without considering temporal variations of the relative values of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon_{r}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu_{r}$</tex> of the materials where the wave is traveling. Aperiodic graph ; Metamaterial ; Photonics ; Physics ; Computer science ; Matter wave ; Artificial intelligence ; Quantum ; Optoelectronics ; Mathematics ; Quantum mechanics ; Combinatorics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Lewis Y. Geer | Joel Lapin | Douglas J. Slotta | Tytus D. Mak | Stephen E. Stein
Source: arXiv (Cornell University) (preprint, 2023-05-16)
Field: Chemistry — Spectroscopy

AIomics: exploring more of the proteome using mass spectral libraries extended by AI The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides. False positive paradox ; Computer science ; Proteome ; Spectral line ; Sequence (biology) ; Matching (statistics) ; Computational biology ; Database search engine ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Data mining ; Algorithm ; Information retrieval ; Bioinformatics ; Physics ; Biology ; Search engine ; Mathematics ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Yiyang Zhang | Yan Ma | Yao Zhang | Xingwu Tian | Siyan Ma | Jing Wang | Ling Ma | Longguo Wu
Source: Research Square (preprint, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Determination of soluble solids content and organic acid content in tomatoes with different nitrogen levels based on hyperspectral imaging technique Abstract Tomato is sweet and sour and has high nutritional value. Soluble solids content (SSC) and organic acid content are important quality indexes of tomato fruit. The exogenous supply of different forms of nitrogen can have different effects on plant growth and development and physiological and metabolic processes because of the different mechanisms of nitrogen uptake and assimilation in plants. In the paper, different concentrations of nitrogen were used to study tomatoes' physical and chemical characteristics and appearance. Hyperspectral imaging (HSI) technology was employed to predict tomatoes' SSC and acid content. Competitive adaptive reweighed sampling (CARS), uninformative variable elimination (UVE),variable combination population analysis (VCPA), iteratively retaining informative variables (IRIV), and interval variable iterative spatial shrinkage analysis (IVISSA) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC and organic acid content were established by partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR). Then a custom convolutional neural network (CNN) model was constructed and optimised. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration, and the highest organic acid content was recorded under the T4 treatment. For tomatoes treated with different nitrogen concentrations, the CARS-PLSR model showed the best results for tomato SSC, with R C and R P of 0.8589 and 0.8499 and RMSEC and RMSEP of 0.3180 and 0.3407. The IRIV-PCR model for organic acids was the best, with R C and R P reaching 0.8011 and 0.7760 and RMSEC and RMSEP reaching 0.6181 and 0.7055. Among all the models, the performance obtained by the CNN model was satisfactory. This study provides technical support for future online nondestructive testing of tomato quality. Partial least squares regression ; Hyperspectral imaging ; Nitrogen ; Principal component analysis ; Chemistry ; Food science ; Mathematics ; Artificial intelligence ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Junyan Wang | Ziyan Lian | Feiyang Kang | Jinkai Chen
Source:  (article, 2023-11-17)
Field: Chemistry — Organic Chemistry

RepCFT: A Fusion Model of Reparameterized Convolutional Neural Network and Visual Transformer for Wood Species Recognition Wood is an extremely important foundational building material. Rapid and accurate wood species recognition is crucial for the rational application of wood and for preventing economic losses caused by the misidentification of wood species. Current research primarily focuses on image preprocessing and the use of Convolutional Neural Networks (CNNs) for wood species recognition. Most of the existing methods employ non-lightweight CNN models, making it difficult for the models to achieve rapid recognition. Addressing the above issues, this paper introduces a hybrid model that combines Reparameterized CNN and Visual Transformer, which we refer to as RepCFT. We have designed a new module, RepLG, with strong local and global information extraction capabilities. RepCFT, constructed from RepLG, achieves a recognition accuracy of 98.7% without any data augmentation or training strategies, while using only 4.3 million parameters and 0.6 billion Multiply-Accumulate operations (MACs). Its performance is on par with ResNet50 and surpasses some existing lightweight models. At the same time, it achieves a recognition speed of up to 601.46 frames per second, effectively meeting the need for rapid and accurate recognition in wood species recognition tasks. All code and models have been uploaded to https://github.com/OnlyForWW/RepCFT. Computer science ; Convolutional neural network ; Preprocessor ; Upload ; Transformer ; Artificial intelligence ; Feature extraction ; Code (set theory) ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Voltage ; Engineering Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: М.Г. Баширов | E.M. Bashirova | I.G. Yusupova | D.Sh. Akchurin | R.T. Yulberdin
Source: Nanotechnologies in Construction A Scientific Internet-Journal (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Identification of the stress-strain state and damage of metal structures of building constructions with nanoparticle coatings using the electromagnetic-acoustic method Introduction. Approximately 20% of accidents in buildings and structures are associated with the destruction of metal structures. Nanocoatings applied to metal structures significantly improve their operational properties, but at the same time make it difficult to use traditional non-destructive testing methods that require access to the surface of the base metal. The quality of the nanocoating is controlled during its formation. To prevent the destruction of metal structures, it is necessary to identify structural heterogeneities of the base metal in micro-and sub-micron sizes, which arise in areas of increased mechanical stress concentration and transform into macrodefects leading to structural failure. Methods and tools are required to non-destructively identify the stress-strain state and damage of the base metal structure through the layer of nanocoating. Electromagnetic-acoustical (EMA) transformation, is considered promising for solving this problem. Existing EMA tools do not fully utilize the potential of EMA conversion, have insufficient sensitivity and the lack of informativity. Methods and materials. For experimental research, we selected widely used in construction structures steels. Research was conducted on the interrelationship of structural alterations in standard samples of metals and the parameters of the electro-magnetic (EMA) transducer under static and cyclic loads. Results. Load diagrams of metal samples were obtained, with accompanying photographs of microstructure in control points, along with their frequency models derived through spectral analysis of the signal from EMA transducer. Discussion. Changes in the stress-strain state of the metal and accumulation of damage in its structure result in a complex interrelated set of changes in mechanical, acoustic, and electro-physical properties, all of which are reflected in the changes in the parameters of the frequency model. Conclusion. Based on the results of the conducted research, the use of a frequency model has been proposed as an integral parameter for identifying the stress-strain state and damage of metal in the equipment. The use of an artificial neural network for analyzing frequency model parameters simplifies the process of identifying the stress-strain state and damage of metal structures and increases its reliability. Materials science ; Stress (linguistics) ; Strain (injury) ; Stress–strain curve ; Nanoparticle ; Metal ; Composite material ; Identification (biology) ; Structural engineering ; Acoustics ; Engineering ; Nanotechnology ; Metallurgy ; Physics ; Deformation (meteorology) Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Jingrui Wei | Kalani Moore | Benjamin Bammes | Barnaby D.A. Levin | Dane Morgan | Paul M. Voyles
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning Approach for High-accuracy Electron Counting of Direct Electron Detectors at Increased Electron Dose Journal Article Deep Learning Approach for High-accuracy Electron Counting of Direct Electron Detectors at Increased Electron Dose Get access Jingrui Wei, Jingrui Wei Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States of America Search for other works by this author on: Oxford Academic Google Scholar Kalani Moore, Kalani Moore Direct Electron L.P., San Diego, CA, United States of America Search for other works by this author on: Oxford Academic Google Scholar Benjamin Bammes, Benjamin Bammes Direct Electron L.P., San Diego, CA, United States of America Search for other works by this author on: Oxford Academic Google Scholar Barnaby D A Levin, Barnaby D A Levin Direct Electron L.P., San Diego, CA, United States of America Search for other works by this author on: Oxford Academic Google Scholar Dane Morgan, Dane Morgan Search for other works by this author on: Oxford Academic Google Scholar Paul M Voyles Paul M Voyles Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, WI, United States of America Corresponding author: paul.voyles@wisc.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 702–704, https://doi.org/10.1093/micmic/ozad067.346 Published: 22 July 2023 Library science ; History ; Computer science Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Michał Bogdan | Pineda, Jesus | Mihir Durve | L. Jurkiewicz | Sauro Succi | Volpe, Giovanni | Jan Guzowski
Source: arXiv (Cornell University) (preprint, 2023-02-10)
Field: Materials Science — Materials Chemistry

Crystallization and topology-induced dynamical heterogeneities in soft granular clusters Soft-granular media, such as dense emulsions, foams or tissues, exhibit either fluid- or solid-like properties depending on the applied external stresses. Whereas bulk rheology of such materials has been thoroughly investigated, the internal structural mechanics of finite soft-granular structures with free interfaces is still poorly understood. Here, we report the spontaneous `crystallization' and `melting' inside a model soft granular cluster -- a densely packed aggregate of $N\sim 30-40$ droplets engulfed by a fluid film -- subject to a varying external flow. We develop new machine learning tools to track the internal rearrangements in the quasi-2D cluster as it transits a sequence of constrictions. As the cluster relaxes from a state of strong mechanical deformations, we find differences in the dynamics of the grains within the interior of the cluster and those at its rim, with the latter experiencing larger deformations and less frequent rearrangements, effectively acting as an elastic membrane around a fluid-like core. We conclude that the observed structural-dynamical heterogeneity results from an interplay of the topological constrains, due to the presence of a closed interface, and the internal solid-fluid transitions. We discuss universality of such behavior in various types of finite soft granular structures, including biological tissues. Granular material ; Cluster (spacecraft) ; Rheology ; Crystallization ; Porous medium ; Chemical physics ; Materials science ; Fluidization ; Relaxation (psychology) ; Porosity ; Nanotechnology ; Mechanics ; Chemistry ; Physics ; Composite material ; Thermodynamics ; Fluidized bed Pickering emulsions and particle stabilization ; Slime Mold and Myxomycetes Research ; Biocrusts and Microbial Ecology