jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiao Yang | Liangcai Shu | Dongsheng Yang
Source:  (preprint, 2024-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchical Physics-Informed Neural Network Framework for 3D Magnetic Modeling of Medium Frequency Transformers Neural Network (NN) technology is revolutionizing the modeling paradigm in the engineering arena, enhancing simultaneously model precision and process speed. Nevertheless, the deficiency of physical interpretability in current NN models causes an unattainable demand for the quantity of training data, especially when dealing with high-dimension physical behaviours. In this work, a Hierarchical Physics-Informed Neural Network (HPINN) framework is proposed to address the 3D magnetic modeling issue of Medium Frequency Transformers (MFTs). By establishing the knowledge transfer channel between the cross section and the equivalent space, the inferior 2D model parameter search space, which is restricted by Dowell’s equation and accessible 2D magnetic data information, can be mapped into the superior 3D search space. Accordingly, with limited 3D training data, the performance of the HPINN framework is still guaranteed around the optimal state. The structural embedding of physics knowledge reduces the model’s reliance on 3D training data, thereby relieving the strict computing requirement at the data generation stage. Finally, according to the training results and experiment verifications, with only one-third computational burden used in the classic NN model, the average error of this proposed HPINN framework is reduced to 1%, and the maximum error does not exceed 10%. Transformer ; Artificial neural network ; Medium frequency ; Physics ; Computer science ; Statistical physics ; Artificial intelligence ; Quantum mechanics ; Astronomy ; Voltage Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Electric Motor Design and Analysis
Authors: Garnet Kin‐Lic Chan
Source: arXiv (Cornell University) (preprint, 2024-07-15)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum chemistry, classical heuristics, and quantum advantage We describe the problems of quantum chemistry, the intuition behind classical heuristic methods used to solve them, a conjectured form of the classical complexity of quantum chemistry problems, and the subsequent opportunities for quantum advantage. This article is written for both quantum chemists and quantum information theorists. In particular, we attempt to summarize the domain of quantum chemistry problems as well as the chemical intuition that is applied to solve them within concrete statements (such as a classical heuristic cost conjecture and a classification of different avenues for quantum advantage) in the hope that this may stimulate future analysis. Heuristics ; Quantum ; Quantum chemistry ; Computer science ; Theoretical physics ; Theoretical computer science ; Chemistry ; Statistical physics ; Quantum mechanics ; Physics ; Molecule History and advancements in chemistry
Authors: Viny Saajan Victor | Andre Schmeißer | Heike Leitte | Simone Gramsch
Source: arXiv (Cornell University) (preprint, 2024-04-15)
Field: Materials Science — Polymers and Plastics

Machine learning-based optimization workflow of the homogeneity of spunbond nonwovens with human validation In the last ten years, the average annual growth rate of nonwoven production was 4%. In 2020 and 2021, nonwoven production has increased even further due to the huge demand for nonwoven products needed for protective clothing such as FFP2 masks to combat the COVID19 pandemic. Optimizing the production process is still a challenge due to its high nonlinearity. In this paper, we present a machine learning-based optimization workflow aimed at improving the homogeneity of spunbond nonwovens. The optimization workflow is based on a mathematical model that simulates the microstructures of nonwovens. Based on trainingy data coming from this simulator, different machine learning algorithms are trained in order to find a surrogate model for the time-consuming simulator. Human validation is employed to verify the outputs of machine learning algorithms by assessing the aesthetics of the nonwovens. We include scientific and expert knowledge into the training data to reduce the computational costs involved in the optimization process. We demonstrate the necessity and effectiveness of our workflow in optimizing the homogeneity of nonwovens. Workflow ; Homogeneity (statistics) ; Computer science ; Artificial intelligence ; Statistics ; Machine learning ; Mathematics ; Database Textile materials and evaluations
Authors: Carl J. Lauer | Robert C. Blake | Jonathan B. Freund
Source: arXiv (Cornell University) (preprint, 2024-12-04)
Field: Materials Science — Surfaces, Coatings and Films

Soft Checksums to Flag Untrustworthy Machine Learning Surrogate Predictions and Application to Atomic Physics Simulations Trained neural networks (NN) are attractive as surrogate models to replace costly calculations in physical simulations, but are often unknowingly applied to states not adequately represented in the training dataset. We present the novel technique of soft checksums for scientific machine learning, a general-purpose method to differentiate between trustworthy predictions with small errors on in-distribution (ID) data points, and untrustworthy predictions with large errors on out-of-distribution (OOD) data points. By adding a check node to the existing output layer, we train the model to learn the chosen checksum function encoded within the NN predictions and show that violations of this function correlate with high prediction errors. As the checksum function depends only on the NN predictions, we can calculate the checksum error for any prediction with a single forward pass, incurring negligible time and memory costs. Additionally, we find that incorporating the checksum function into the loss function and exposing the NN to OOD data points during the training process improves separation between ID and OOD predictions. By applying soft checksums to a physically complex and high-dimensional non-local thermodynamic equilibrium atomic physics dataset, we show that a well-chosen threshold checksum error can effectively separate ID and OOD predictions. Checksum ; Flag (linear algebra) ; Physics ; Computer science ; Machine learning ; Statistical physics ; Artificial intelligence ; Particle physics ; Mathematics ; Operating system Electron and X-Ray Spectroscopy Techniques
Authors: Simon León Krug | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2024-06-26)
Field: Materials Science — Materials Chemistry

Alchemical insights into approximately quadratic energies of iso-electronic atoms Accurate quantum mechanics based predictions of property trends are so important for materials design and discovery that even inexpensive approximate methods are valuable. We use the Alchemical Integral Transform (AIT) to study multi-electron atoms, and to gain a better understanding of the approximately quadratic behavior of energy differences between iso-electronic atoms in their nuclear charges. Based on this, we arrive at the following simple analytical estimate of energy differences between any two iso-electronic atoms, $ΔE \approx -(1 + 2γ\sqrt{N_e-1})\,ΔZ\, \bar{Z}$. Here, $γ\approx 0.3766 \pm 0.0020$ Ha corresponds to an empirical constant, and $N_e$, $ΔZ$, and $\bar{Z}$ respectively to electron number, and nuclear charge difference and average. We compare the formula's predictive accuracy using experimental numbers and non-relativistic, numerical results obtained via DFT (pbe0) for the entire periodic table up to Radon. A detailed discussion of the atomic Helium-series is included. Charge (physics) ; Quadratic equation ; Atomic physics ; Physics ; Effective nuclear charge ; Nuclear physics ; Mathematics ; Quantum mechanics ; Geometry ; Electron Graphite, nuclear technology, radiation studies ; Marine and environmental studies ; Nuclear physics research studies
Authors: Mingzhe Shen | Ruibin Liu | Jana Shen
Source: Biophysical Journal (article, 2024-02-01)
Field: Chemistry — Organic Chemistry

Protein pKa predictions with machine learning Ionization ; Stability (learning theory) ; Constant (computer programming) ; Molecular dynamics ; Chemistry ; Biological system ; Chemical physics ; Computer science ; Computational chemistry ; Ion ; Machine learning ; Biology Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dhena Ria Barleany | Desy Kumala Sari | Retno Sulistyo Dhamar Lestari | Y Raharja | A Gunawan
Source: International Journal of Innovation in Mechanical Engineering and Advanced Materials (article, 2024-01-15)
Field: Materials Science — Biomaterials

BIOPOLYMER-BASED FILM PREPARATION FOR POTENTIAL SMART FOOD PACKAGING MATERIAL APLLICATION Public interest in colorimetric films for food freshness monitoring has increased recently. In addition to extending the shelf life of packaged food products, packaging materials are also required to provide current information about the freshness of the food while ensuring food quality and safety. The current work aims to prepare smart biodegradable films based on biopolymer-containing color indicators to monitor the quality of Decapterus spp. The pH-sensing colorimetric film was developed from a chitosan biopolymer modified using polyvinyl alcohol (PVA) and glycerol, as well as methyl red, as an indicator of fish freshness. The effect of using PVA and stirring conditions (temperature and time) on film production was evaluated on its physical appearance, water vapor permeability, and mechanical properties. The results show that the use of PVA can increase the transparency of chitosan films. Incorporating PVA into the film results in brighter and clearer colors compared to films without PVA. The temperature used in the preparation of the film solution has an influence on the mechanical properties and the water vapor permeability. The increasing stirring temperature leads to the enhancement of Young's modulus and the barrier properties against water vapor and moisture, still concurrently impacting a decrease in the film's yield strength and strain. Additionally, the film also exhibits responsiveness to pH during fish spoilage, with a color change that occurs from pink to yellowish. This confirms that the pH-responsive film resulting from this research has great potential to be applied as a real-time indicator of fish freshness during storage. Biopolymer ; Food packaging ; Materials science ; Nanotechnology ; Composite material ; Food science ; Chemistry ; Polymer Nanocomposite Films for Food Packaging
Authors: Bharath Kumar Chagaleti | Kathiravan Muthu K
Source: Research Square (preprint, 2024-02-05)
Field: Chemistry — Organic Chemistry

Molecular modeling studies of Pyrazolopyrimidine Derivatives as potent Cyclin Dependent Kinase-2 inhibitors Abstract This study addresses the pressing need for innovative cancer treatments in the face of global challenges posed by the widespread occurrence of cancer and increasing treatment resistance. The study looks at cyclin-dependent kinase-2 (CDK2) and uses a methodical computer approach to find possible anticancer compounds with pyrazole and pyrimidine structures. (QSAR) quantitative structure-activity relationship has become crucial in lead optimization over the last three decades. A set of 45 pyrazolopyrimidine derivatives with known IC50 values were used to create and test models using QSARINS software. Model 4, with its high predictive performance (R2 = 0.9100, R2adj = 0.8900, LOF = 0.0394), emerges as the most reliable. The resulting QSAR model proves stable, predictive, and robust, effectively representing the original dataset. Active molecular descriptors are identified for predicting the structure-activity relationship. We used SAR analysis and model equation parameters to create sixty compounds and tested them for their predicted bioactivity using Model 4. These compounds are a series with pyrazolopyrimidine-fused piperidine and hybrid moieties, such as methanethione (20), ethenone (20), and benzamide (20). Among the designed series, 16 compounds exhibited pIC50 values exceeding 7, indicating that they were hit molecules represented as C1-C16. These obtained hit molecules undergo further screening with ADMET, molecular docking, and molecular dynamics simulations. C3 and C7, revealed in docking studies with low-energy conformations and sustained binding during simulations, consistently align their binding modes with the standard drug roscovitine. These compounds emerge as promising leads for targeting CDK2 in the development of groundbreaking cancer therapies. Kinase ; Chemistry ; Cyclin ; Cyclin-dependent kinase ; Computational biology ; Cancer research ; Biology ; Biochemistry ; Cell cycle ; Gene Synthesis and biological activity ; Cancer Mechanisms and Therapy ; Computational Drug Discovery Methods
Authors: Matthew Hayman | Robert A. Stillwell | Adam Karboski | Willem Marais | Scott M. Spuler
Source: arXiv (Cornell University) (preprint, 2024-02-27)
Field: Chemistry — Spectroscopy

Global Estimation of Range Resolved Thermodynamic Profiles from MicroPulse Differential Absorption Lidar We demonstrate thermodynamic profile estimation with data obtained using the MicroPulse DIAL such that the retrieval is entirely self contained. The only external input is surface meteorological variables obtained from a weather station installed on the instrument. The estimator provides products of temperature, absolute humidity and backscatter ratio such that cross dependencies between the lidar data products and raw observations are accounted for and the final products are self consistent. The method described here is applied to a combined oxygen DIAL, potassium HSRL, water vapor DIAL system operating at two pairs of wavelengths (nominally centered at 770 and 828 nm). We perform regularized maximum likelihood estimation through the Poisson Total Variation technique to suppress noise and improve the range of the observations. A comparison to 119 radiosondes indicates that this new processing method produces improved temperature retrievals, reducing total errors to less than 2 K below 3 km altitude and extending the maximum altitude of temperature retrievals to 5 km with less than 3 K error. The results of this work definitively demonstrates the potential for measuring temperature through the oxygen DIAL technique and furthermore that this can be accomplished with low-power semiconductor-based lidar sensors. Lidar ; Remote sensing ; Range (aeronautics) ; Environmental science ; Differential (mechanical device) ; Absorption (acoustics) ; Estimation ; Atmospheric sciences ; Geology ; Materials science ; Optics ; Physics ; Thermodynamics ; Engineering Spectroscopy and Laser Applications ; Advanced Optical Sensing Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ganesha Rai | Adam Yasgar | Daniel Korn | Alexander Tropsha | Alexey Zakharov | Kyle H. Bowler | Rodolpho C. Braga | Stephen J. Capuzzi | Eugene Muratov | Anton Simeonov | Vinícius M. Alves | Joshua E. Hochuli
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2024-07-27)
Field: Chemistry — Spectroscopy

SCAM Detective: Accurate Predictor of Small, Colloidally Aggregating Molecules Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC β-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by ~53% in the β-lactamase assay and by ~46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https://scamdetective.mml.unc.edu/). Chemistry ; Computer science Mass Spectrometry Techniques and Applications ; Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Zaid Haddadin | Anna My Nguyen | Lisa V. Poulikakos
Source: arXiv (Cornell University) (preprint, 2024-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Corner cutting connects chiral colorimetry to net electric flux in lossless all-dielectric metasurfaces All-dielectric metasurfaces can produce structural colors, but the most advantageous design criteria are still being investigated. This work numerically studies how the two-dimensional shape of nanoparticles affects the colorimetric response under circularly polarized light (CPL) to develop a sensor distinguishing CPL orientations. Using lossless dielectric materials (silicon nitride on silicon dioxide), we achieve far-field dichroism by modifying oblong nanoparticles into L-shaped structures through corner cuts. This design suppresses one electric dipole under CPL illumination, leading to differential colorimetric responses. We link these responses to a decoupling effect in the near-field net electric flux. Our findings provide design guidelines for all-dielectric, lossless colorimetric sensors of chiral light. Lossless compression ; Net (polyhedron) ; Dielectric ; Flux (metallurgy) ; Materials science ; Colorimetry ; Optics ; Composite material ; Physics ; Optoelectronics ; Mathematics ; Geometry ; Algorithm Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Syed Atir Raza Shirazi | Nafeesa Yousaf
Source: NUST Journal of Engineering Sciences (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model Red rot in sugarcane is a devastating fungal disease caused by Colletotrichum falcatum. It leads to wilting, red discoloration, and yield losses, which can impact sugar production globally. Red rot hampers sugarcane production, causing yield reductions, and economic losses, and affecting the nation’s sugar industry demanding stringent disease management strategies. There is a need of a system that detects the red rot disease at an early stage before it spreads. Transfer learning enhances agricultural models, leveraging pre-trained data for improved crop predictions, resource optimization, and sustainability. In this article, we have proposed a transfer learning-based VGG16 model for the detection of red rot disease in sugarcane on a large dataset. The model achieves a remarkable 98.85% accuracy and an F1 score of 0.93 in early red rot detection in sugarcane. This work underscores the potency of leveraging re-trained models for crop disease identification, offering a promising avenue for proactive disease management. This research marks a substantial step towards enhancing crop yield and sustainability, presenting an accessible and impactful technological solution for early disease detection in sugarcane, ultimately benefiting agricultural practices. Transfer of learning ; Biology ; Botany ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Sugarcane Cultivation and Processing
Authors: Mohd Syahir Anwar Hamzah | Nadirul Hasraf Mat Nayan | Norhana Jusoh
Source: Journal of Advanced Research in Applied Mechanics (article, 2024-10-02)
Field: Materials Science — Biomaterials

Optimization of Polyvinylidene Fluoride/Polyvinyl Alcohol Electrospun using Taguchi Method for Tissue Engineering Application Composite nanofibers for tissue engineering applications were prepared with polyvinylidene fluoride and polyvinyl alcohol via a horizontal electrospinning process. The effect of the processing parameters (polymer concentration, voltage, feed rate and tip-to-collector distance) on the fibre diameter was studied using L9 orthogonal arrays of the Taguchi Method, S/N ratio, and ANOVA to produce uniform and finest fibre. The optimum parameters were at 90 wt% PVDF ratios for polymer concentration, applied voltage at 10 kV, feed rate at 0.5 mL/h and tip-to-collector distance at 15 cm. The PVDF/PVA electrospun compromises 171.60 nm nanofiber diameter with homogenous morphology and no bead formed. MTT and live/dead kit assay analysis toward human (HSF) cells confirmed the potential of the PVDF/PVA electrospun to be used as an alternative tissue template to allow new cell or tissue formation in the future. Polyvinylidene fluoride ; Polyvinyl alcohol ; Taguchi methods ; Materials science ; Electrospinning ; Tissue engineering ; Composite material ; Biomedical engineering ; Polymer ; Engineering Electrospun Nanofibers in Biomedical Applications
Authors: Kien Nguyen-Cong | Jonathan Willman | Joseph M. Gonzalez | Ashley S. Williams | A. B. Belonoshko | Stan Moore | Aidan P. Thompson | Mitchell Wood | J. H. Eggert | M. Millot | Luis A. Zepeda-Ruiz | Ivan Oleynik
Source: arXiv (Cornell University) (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

Extreme Metastability of Diamond and its Transformation to BC8 Post-Diamond Phase of Carbon Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synthesis and recovery of the theoretically predicted post-diamond BC8 phase remains elusive. Through quantum accurate, multi-million atom molecular dynamics (MD) simulations, we have uncovered the extreme metastability of diamond at very high pressures, significantly exceeding its range of thermodynamic stability. We predict the post-diamond BC8 phase to be experimentally accessible only within a narrow high pressure-temperature region of the carbon phase diagram. The diamond to BC8 transformation proceeds through pre-melting followed by BC8 nucleation and growth in the metastable carbon liquid. We propose a double-shock compression pathway to achieve BC8 synthesis, which is currently being explored in theory-inspired experiments at the National Ignition Facility. Diamond ; Metastability ; Nucleation ; Phase diagram ; Materials science ; Carbon fibers ; Phase (matter) ; Thermodynamics ; Condensed matter physics ; Physics ; Quantum mechanics ; Metallurgy Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Boron and Carbon Nanomaterials Research