jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholas Gao | Eike Eberhard | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Surfaces, Coatings and Films

Learning Equivariant Non-Local Electron Density Functionals The accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To address these issues, we introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant graph neural networks (GNNs). Where previous works relied on semi-local functionals or fixed-size descriptors of the density, we compress the electron density into an SO(3)-equivariant nuclei-centered point cloud for efficient non-local atomic-range interactions. By applying an equivariant GNN on this point cloud, we capture molecular-range interactions in a scalable and accurate manner. To train EG-XC, we differentiate through a self-consistent field solver requiring only energy targets. In our empirical evaluation, we find EG-XC to accurately reconstruct `gold-standard' CCSD(T) energies on MD17. On out-of-distribution conformations of 3BPA, EG-XC reduces the relative MAE by 35% to 50%. Remarkably, EG-XC excels in data efficiency and molecular size extrapolation on QM9, matching force fields trained on 5 times more and larger molecules. On identical training sets, EG-XC yields on average 51% lower MAEs. Equivariant map ; Electron ; Physics ; Mathematics ; Pure mathematics ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Isaac W. Beaglehole | Miles J. Pemberton | Elliot H. E. Farrar | Matthew N. Grayson
Source: ChemRxiv (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Machine learning for transition state geometry prediction The calculation of transition state (TS) geometries is essential for understanding reaction mechanisms and rational synthetic methodology design. However, traditional methods like density functional theory (DFT) are often too computationally expensive for large-scale TS identification and are significantly slower than high-throughput experimental screening methods. Recent advancements in machine learning (ML) offer promising alternatives, enabling the direct prediction of TS geometries, reducing the reliance on expensive quantum mechanical (QM) calculations and affording predictions ahead of experiment. The works explored here include the broader application of ML in reaction property prediction, emphasising how accurate TS geometries can serve as vital input data to improve model accuracy. A comprehensive review of ML methods developed to explicitly predict TS geometries are then presented, with attention to their application in downstream tasks, such as energy barrier calculations, and their use as initial structures for further optimisation via QM methods. Finally, a critical evaluation of the accuracy and limitations of existing TS prediction methods are discussed, highlighting challenges that impede wider adoption and areas where further research is needed. Computer science ; Scale (ratio) ; Identification (biology) ; Property (philosophy) ; Density functional theory ; Quantum chemical ; Throughput ; Machine learning ; Quantum ; Artificial intelligence ; Computational chemistry ; Chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Wenlin Jiang | Jianan Nie | Liang Bian | Zheng Ge | Faqin Dong | Mianxin Song | Fei Wang | Xiaobin Gu | Xiaofeng Zhao | T. R. Arslanov | Qing Song
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Mechanistic Study on U(Vi) Reduction Enhanced by Machine Learning Under the Synergistic Interaction of Ferruginous Montmorillonite and Shewanella Montmorillonite ; Reduction (mathematics) ; Shewanella ; Chemistry ; Chemical engineering ; Geology ; Engineering ; Bacteria ; Mathematics Radioactive element chemistry and processing ; Metal Extraction and Bioleaching ; Extraction and Separation Processes
Authors: Milad Razbin | Mortaza Salehian | Ali Akbar Gharehaghaji
Source: Research Square (preprint, 2024-08-07)
Field: Materials Science — Polymers and Plastics

A Viscoelastic-Plastic Model for the Core of Various Close- Packings of Multifilament Polyamide-6 Yarns Viscoelasticity ; Polyamide ; Core (optical fiber) ; Composite material ; Materials science ; Polymer science Textile materials and evaluations ; Mechanical Behavior of Composites ; Dyeing and Modifying Textile Fibers
Authors: Safitri Diah Ayu Ariani | Inggit Kresna Maharsih | Dimas Firmanda Al Riza
Source: Journal of Intelligent System and Computation (article, 2024-05-02)
Field: Chemistry — Analytical Chemistry

Prediction of Physico-Chemical Characteristics in Batu Tangerine 55 Based on Reflectance-Fluorescence Computer Vision Oranges (Citrus sp.) are one of the most abundant agricultural commodities in Indonesia. One of the popular local citruses is Batu Tangerine 55. Harvesting tangerines begins 252 days after the flowers bloom. Conventionally, we still determine the level of maturity by observing the color, shape, and hardness. The results of manual grouping tend to be subjective and less accurate. Destructive testing could be carried out and provide objective results; however, it would require sampling and damaging the fruits. Computer vision could be used to evaluate the maturity level of the fruit non-destructively. Dual imaging computer vision, i.e., reflectance-fluorescence mode, could be used to enhance the accuracy of the prediction. This study aims to develop a classification model and predict the physico-chemical characteristics of Batu Tangerine 55. Destructive testing is still being carried out to determine the value of TPT, the degree of acidity, and the firmness of the fruit. Non-destructive testing was carried out to obtain reflectance and fluorescence images. Once we obtain the destructive and non-destructive data, we will incorporate them into the classification and prediction models. The machine learning method for maturity classification uses three models, namely KNN, SVM, and Random Forest. The best results on the reflectance data (RGB) SVM model resulted in an accuracy of 1 for training data and 0.97 for testing data. The maturity parameter prediction method uses the PLS method. The best results for the predicted Brix/Acidity ratio R2 parameter are 0.81 and RMSE 3.4. Reflectivity ; Fluorescence ; Chemistry ; Computer science ; Biological system ; Artificial intelligence ; Computer vision ; Optics ; Materials science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Food and Agricultural Sciences ; Smart Agriculture and AI
Authors: Yifei Hao
Source: Applied and Computational Engineering (article, 2024-11-26)
Field: Chemistry — Spectroscopy

The Advantages and Challenges of the Future Development of Intelligent and Automatically Adjustable Prescription Glasses Abstract. The latest intelligent and automatically adjustable prescription glasses are mainly a combination of smart glasses and AI algorithms - adding some basic information about the customer's eyes, such as left and right eye vision, eye curvature, eye wheelbase, etc., to the device bound to the smart glasses. AI will use this basic information to estimate the approximate monthly increase in prescription, and automatically change the thickness of the lens through a driver to achieve the function of automatically adjusting prescription for the customer. In the future - an era where the population of myopia is rapidly growing and the cost of corrective lenses becomes a burden for many people - smart adjustable degree glasses not only have cost-effectiveness and adaptability to the times, but can also monitor eye health and provide real-time feedback to users. This has the great potential to change the game rules for managing myopia progression, especially for young people at the highest risk. But to achieve this vision, the technological challenges are also extremely high - the current development status of machine learning, sensor technology, and miniaturization technology is not sufficient to support the development of intelligent adjustable degree glasses; In addition, significant technological improvements are needed for battery life, comfort, data accuracy, and the adaptability of AI models to different user needs. Medical prescription ; Computer science ; Engineering management ; Data science ; Process management ; Engineering ; Medicine ; Nursing Aerogels and thermal insulation
Authors: Jeremy A. McCulloch | Ellen Kuhl
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-29)
Field: Materials Science — Polymers and Plastics

Automated model discovery for textile structures: The unique mechanical signature of warp knitted fabrics Abstract Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 2 14 =16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics–woven or knitted, weft knit or warp knit, polymeric or metallic–and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery as a novel strategy for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN . Textile ; Orthotropic material ; Computer science ; Flexibility (engineering) ; Stiffening ; Constitutive equation ; Nonlinear system ; Anisotropy ; Materials science ; Mechanical engineering ; Structural engineering ; Engineering ; Composite material ; Physics ; Mathematics Textile materials and evaluations ; Elasticity and Material Modeling ; Mechanical Behavior of Composites
Authors: Y. Zhang | Zhongqian Sun | Haoming Li | Qingxia Kong | Xuezheng Zhang | Chengde Li
Source: medRxiv (preprint, 2024-12-06)
Field: Chemistry — Analytical Chemistry

Real-world safety of zonisamide: Mining and analysis of adverse events related to zonisamide based on FAERS database ABSTRACT Purpose To investigate the real-world adverse event signals associated with zonisamide and provide a foundation for its safe clinical use. Methods Adverse event reports involving zonisamide as the primary suspected drug were collected from the FDA Adverse Event Reporting System (FAERS) database, covering the period from the first quarter of 2004 to the fourth quarter of 2023. The data were analyzed utilizing the Reporting Odds Ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) methods of the proportional imbalance technique. Results A total of 3205 adverse event reports involving zonisamide as the primary suspected drug were identified, resulting in 260 positive signals for preferred terms (PTs). These signals, derived from both the ROR and BCPNN methods, encompassed 27 systems and organs (SOCs), with a predominant focus on nervous system disorders and skin and subcutaneous tissue disorders. The most frequently reported PTs included seizures, drug reactions with eosinophilia and systemic symptoms, Stevens-Johnson syndrome, rash, and dizziness. Notably, the top PTs in terms of signal intensity included perinephritis, epilepsy with myoclonic-atonic, ocular mucocutaneous syndrome, ocular choroidal leakage, tonsillar exudate, and ovarian granulosa vesicular cell tumor. Interestingly, ten of the top 30 risk signals for adverse events, based on signal strength, were not detailed in the package inserts. Thses included perinephritis, myoclonic dystonic epilepsy, ovarian granulosa vesicular cell tumor, positive human herpesvirus 6 serology, and positive lymphocyte stimulation test. Conclusion Common adverse reactions to zonisamide in real-world settings are generally in line with the established specification, with the most frequently observer signals related to neurological, skin, and subcutaneous tissue disorders. However, several newly suspected adverse reactions have been identified, including perinephritis, infectious pneumonitis, ovarian granulosa vesicular cell tumor, positive serology for human herpesvirus 6, and positive lymphocyte stimulation test. These findings indicate that these potential adverse reactions should be closely monitored in clinical practice. Zonisamide ; Database ; Medicine ; Business ; Computer science ; Epilepsy ; Psychiatry Analytical Methods in Pharmaceuticals ; Safe Handling of Antineoplastic Drugs
Authors: Danqing Wang | Antonis Antoniades | Kha-Dinh Luong | Edwin Zhang | Mert Kosan | Jiachen Li | Ambuj K. Singh | William Yang Wang | Lei Li
Source:  (article, 2024-08-24)
Field: Materials Science — Materials Chemistry

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX. Counterfactual thinking ; Reinforcement learning ; Computer science ; Artificial intelligence ; Psychology ; Social psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Sakshi Braroo | Xingsheng Sun | K.T. Ramesh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Ceramics and Composites

An Uncertainty Quantification Guided Approach to Modeling High-Velocity Impact into Advanced Ceramics Ceramic ; Uncertainty quantification ; Computer science ; Environmental science ; Petroleum engineering ; Engineering ; Materials science ; Metallurgy ; Machine learning Advanced ceramic materials synthesis ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Qiangqiang Yuan | Haiqing Yin | Yongwei Wang | Ruixia Sun | Zheqi Qiao | Cong Zhang | Ruijie Zhang | Dil Faraz Khan | Dong Sheng Li | Jingbin Liang | Xuanhui Qu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Eutectic Carbide Inhomogeneity in M42 High-Speed Steel Through Machine Learning and Finite Element Modeling Eutectic system ; Carbide ; Finite element method ; Materials science ; High-speed steel ; Metallurgy ; Mechanical engineering ; Composite material ; Structural engineering ; Engineering ; Microstructure Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Raseswar Sahoo | Satyajit Panigrahy | Subrata Karmakar
Source:  (article, 2024-08-18)
Field: Materials Science — Materials Chemistry

Aging State Recognition of a Crosslinked Polyethylene Power Cable Insulation using Machine Learning and Fourier Transform Infrared Spectroscopy Electrical aging is one of the primary contributors to the insulation degradation of a power cable, along with the initiation and progression of partial discharge. Henceforth, the study of partial discharge is an important indicator to evaluate the degree of degradation of the insulation. In this work, a XLPE insulation sample was electrically aged for a total of 80 hours at 28 kV, and PD signals were collected at two distinct instants, i.e., after 40 and 80 hours. From the collected signals, a total of nine statistical features were extracted to train different machine learning techniques like Logistic Regression, K-Nearest Neighbors, Decision Tree, and XgBoost for the classification of the moderately and highly aged condition of the XLPE cable insulation. Furthermore, FTIR was used to investigate the insulation’s material characterization. The experimental findings indicated that the XgBoost method showed effectiveness as compared to others, with an accuracy of 93.33%. FTIR results indicated an increase in the oxidative degradation with the progression of aging. Fourier transform infrared spectroscopy ; Polyethylene ; Materials science ; Fourier transform ; Infrared spectroscopy ; Infrared ; Fourier transform spectroscopy ; Spectroscopy ; Power (physics) ; Power cable ; State (computer science) ; Computer science ; Composite material ; Engineering ; Chemistry ; Chemical engineering ; Optics ; Physics ; Organic chemistry ; Algorithm High voltage insulation and dielectric phenomena ; Industrial Vision Systems and Defect Detection
Authors: Talha Manzoor | Fayyaz Ahmad | Aqsa Zulfiqar | Muhammad Bilal Ijaz | Faiza Jan Iftikhar | Rabia Sami | Muhammad Qamar Khan | Ick Soo Kim
Source: Materials Today Communications (article, 2024-09-26)
Field: Materials Science — Polymers and Plastics

Machine learning prediction model for the seam strength of five pocket denim jeans for lock stitch This study develops a CatBoost-based computational model to predict the seam strength of five-pocket denim jeans, a key factor in garment durability. It explores the effects of thread count, stitch density (SPI), fabric weight, and seam types on seam strength. Findings show that thread count has the most significant impact, followed by SPI and fabric weight. Lapped seams exhibit higher strength compared to superimposed seams with the same parameters. The CatBoost model outperforms traditional polynomial regression, achieving a mean relative absolute error of 2.27 % and 1.17 % for superimposed and lapped seams, respectively, with maximum errors of 5.87 % and 2.51 %. The model explains over 95 % of data variance, confirmed by K-fold cross-validation. This tool helps manufacturers optimize sewing parameters, reduce material waste, and improve denim quality. The research highlights the efficacy of advanced machine learning algorithms like CatBoost in enhancing predictive accuracy and manufacturing processes. Denim ; Materials science ; Lock (firearm) ; Composite material ; Forensic engineering ; Mechanical engineering ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Consumer Perception and Purchasing Behavior
Authors: Jiahui Zhang | J. Zhao | Jesper Byggmästar | Erkka J. Frankberg | A. Kuronen
Source: arXiv (Cornell University) (preprint, 2024-04-26)
Field: Materials Science — Materials Chemistry

Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate the glass transition and mechanical behavior of amorphous Ga2O3 (a-Ga2O3). During the quenching simulations, amorphization of gallium oxide melt is observed at ultrahigh cooling rates, including a distinct glass transition. The final densities at room temperature have up to 4% variance compared to experiments. The glass transition temperature is evaluated to range from 1234 K to 1348 K at different cooling rates. Structural analysis of the amorphous structure shows evident similarities in structural properties between a-Ga2O3 and amorphous alumina (a-Al2O3), such as radial distribution function, coordination distribution, and bond angle distribution. An amorphous gallium oxide structure that contains approximately one million atoms is prepared for the tension simulation. A highly plastic behavior is observed at room temperature in the tension simulations, comparable to amorphous alumina. With quantitative characterization methods, we show that a-Ga2O3 can possibly has a higher nucleation rate of localized plastic strain events compared to a-Al2O3, which can increase the material's resistance to shear banding formation during deformation. Scale (ratio) ; Atomic units ; Amorphous solid ; Gallium oxide ; Materials science ; Plasticity ; Oxide ; Gallium ; Amorphous silica ; Nanotechnology ; Metallurgy ; Chemical engineering ; Composite material ; Chemistry ; Physics ; Engineering ; Crystallography Machine Learning in Materials Science
Authors: Friedrich Fink | Tomasz M. Stawski | Franziska Emmerling | Jana Falkenhagen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Novel Machine-Learning Approach to Unlock Technical Lignin Classification by Nir Spectroscopy - Bench to Handheld Mobile device ; Computer science ; Spectroscopy ; Artificial intelligence ; Materials science ; Machine learning ; World Wide Web ; Physics Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications