jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: R. B. Garg | C. Allaire | A. Salzburger | Hadrien Grasland | L. Tompkins | E. Hofgard
Source: arXiv (Cornell University) (preprint, 2023-09-21)
Field: Chemistry — Spectroscopy

Potentiality of automatic parameter tuning suite available in ACTS track reconstruction software framework Particle tracking is among the most sophisticated and complex part of the full event reconstruction chain. A number of reconstruction algorithms work in a sequence to build these trajectories from detector hits. These algorithms use many configuration parameters that need to be fine-tuned to properly account for the detector/experimental setup, the available CPU budget and the desired physics performance. The most popular method to tune these parameters is hand-tuning using brute-force techniques. These techniques can be inefficient and raise issues for the long-term maintainability of such algorithms. The open-source track reconstruction software framework known as "A Common Tracking Framework (ACTS)" offers an alternative solution to these parameter tuning techniques through the use of automatic parameter optimization algorithms. ACTS comes equipped with an auto-tuning suite that provides necessary setup for performing optimization of input parameters belonging to track reconstruction algorithms. The user can choose the tunable parameters in a flexible way and define a cost/benefit function for optimizing the full reconstruction chain. The fast execution speed of ACTS allows the user to run several iterations of optimization within a reasonable time bracket. The performance of these optimizers has been demonstrated on different track reconstruction algorithms such as trajectory seed reconstruction and selection, particle vertex reconstruction and generation of simplified material map, and on different detector geometries such as Generic Detector and Open Data Detector (ODD). We aim to bring this approach to all aspects of trajectory reconstruction by having a more flexible integration of tunable parameters within ACTS. Computer science ; Detector ; Software ; Suite ; Maintainability ; Trajectory ; Tracking (education) ; Track (disk drive) ; Algorithm ; Bayesian optimization ; Function (biology) ; Event (particle physics) ; Software suite ; Computer engineering ; Real-time computing ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Radiation Detection and Scintillator Technologies
Authors: Isaac Foli
Source:  (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Characterization of WIPP Mineral Fragment Particle Size Distributions Fragment (logic) ; Characterization (materials science) ; Particle (ecology) ; Mineral ; Particle size ; Computer science ; Materials science ; Geology ; Engineering ; Chemical engineering ; Nanotechnology ; Metallurgy ; Algorithm Pickering emulsions and particle stabilization ; Minerals Flotation and Separation Techniques ; Proteins in Food Systems
Authors: В. В. Чумак | Михайло Анатолійович Коваленко | Оксана Леонідівна Тимощук | Андрій Сергійович Стулішенко | Євген Станіславович Ігнатюк
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Розробка багатоланкової системи розрахунків високочастотних процесів в електричних машинах зі всипними обмотками This study considers the design of a multi-link high-frequency circuit for calculating defective and defect-free insulation of mesh windings in order to increase the operational reliability of general-purpose electric machines. The object of research is mesh windings of electric machines. The problem to be solved: increasing the operational reliability of electric machines with general-purpose mesh windings by analyzing and modeling defects. The research results make it possible to model defects for the analysis of the state of insulation of electric machines with mesh windings. Special attention in the research was paid to induction motors with a worn-out resource, in particular, 4A80A4 UZ engine. The analysis includes phase-to-body capacitance, winding inductance, eddy current impedance, and winding impedance. Under the WN mode, a decrease in the impedance of the winding section from 3 % to the short-circuit mode was simulated; accordingly, the resonance frequencies for each case of 3 % were determined – 73990 Hz, short-circuit – 74450 Hz, as well as the corresponding input impedances 8938.7 and 8082.5. The corresponding voltage resonances and current resonances were also determined. Appropriate results were also given for the WG scheme. The results reported here relate to the design of the scheme and the research on the defects of the mesh winding. This contributes to the analysis of the state of insulation of mesh windings. The numerical data (resonant frequencies and input impedances) allow a better understanding of the behavior of windings in different states and degrees of defects. Given the research findings, it is possible to better identify and analyze potential defects in mesh windings, which in turn contributes to increasing the reliability of electric machines. The results of this study could be used in the field of diagnosis, service, and maintenance of electric machines with mesh windings Electromagnetic coil ; Reliability (semiconductor) ; Electrical impedance ; Eddy current ; Voltage ; Vibration ; Current (fluid) ; Cross section (physics) High voltage insulation and dielectric phenomena ; Diverse Industrial Engineering Technologies ; Electrical Fault Detection and Protection
Authors: Vittorio Del Tatto | Gianfranco Fortunato | Domenica Bueti | Alessandro Laio
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Chemistry — Electrochemistry

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data. Causality (physics) ; Transfer entropy ; Predictability ; Granger causality ; Computer science ; Inference ; Benchmark (surveying) ; Entropy (arrow of time) ; Dynamical systems theory ; Causal inference ; Statistical hypothesis testing ; Data mining ; Artificial intelligence ; Econometrics ; Machine learning ; Mathematics ; Principle of maximum entropy ; Statistics ; Physics Electrochemical Analysis and Applications ; Neural dynamics and brain function ; Fractal and DNA sequence analysis
Authors: Shuhao Jiao | Ngai Hang Chan
Source: arXiv (Cornell University) (preprint, 2023-12-04)
Field: Chemistry — Analytical Chemistry

Coefficient Shape Alignment in Multiple Functional Linear Regression In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offering a parsimonious approach to modeling multivariate functional data. In this paper, a novel grouped multiple functional regression model with a new regularization approach termed {\it ``coefficient shape alignment"} is developed to tackle functional covariates homogeneity. The modeling procedure includes two steps: first aggregate covariates into disjoint groups using the new regularization approach; then the grouped multiple functional regression model is established based on the detected grouping structure. In this grouped model, the coefficient functions of covariates in the same group share the same shape, invariant to scaling. The new regularization approach works by penalizing differences in the shape of the coefficients. We establish conditions under which the true grouping structure can be accurately identified and derive the asymptotic properties of the model estimates. Extensive simulation studies are conducted to assess the finite-sample performance of the proposed methods. The practical applicability of the model is demonstrated through real data analysis in the context of sugar quality evaluation. This work offers a novel framework for analyzing the homogeneity of functional covariates and constructing parsimonious models for multivariate functional data. Covariate ; Multivariate statistics ; Homogeneity (statistics) ; Functional data analysis ; Mathematics ; Regression ; Regression analysis ; Regularization (linguistics) ; Statistics ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses
Authors: Guillaume Marrelec | Alain Giron
Source: arXiv (Cornell University) (preprint, 2023-06-22)
Field: Chemistry — Physical and Theoretical Chemistry

Inferring the finest pattern of mutual independence from data For a random variable $X$, we are interested in the blind extraction of its finest mutual independence pattern $μ( X )$. We introduce a specific kind of independence that we call dichotomic. If $Δ( X )$ stands for the set of all patterns of dichotomic independence that hold for $X$, we show that $μ( X )$ can be obtained as the intersection of all elements of $Δ( X )$. We then propose a method to estimate $Δ( X )$ when the data are independent and identically (i.i.d.) realizations of a multivariate normal distribution. If $\hatΔ ( X )$ is the estimated set of valid patterns of dichotomic independence, we estimate $μ( X )$ as the intersection of all patterns of $\hatΔ ( X )$. The method is tested on simulated data, showing its advantages and limits. We also consider an application to a toy example as well as to experimental data. Independence (probability theory) ; Computer science ; Mathematics ; Statistics History and advancements in chemistry
Authors: M. S. A. Alam Khan
Source: arXiv (Cornell University) (preprint, 2023-11-11)
Field: Chemistry — Spectroscopy

Renormalization Group Summation at High Orders and Implications to the Determination of Some Standard Model Parameters We use renormalization group summed perturbation theory (RGSPT) to improve perturbation series in quantum chromodynamics in the determination of some of the standard model parameters. Renormalization group ; Quantum chromodynamics ; Perturbation theory (quantum mechanics) ; Renormalization ; Perturbation (astronomy) ; Mathematical physics ; Divergent series ; Physics ; Mathematics ; Quantum electrodynamics ; Particle physics ; Quantum mechanics ; Pure mathematics ; Summation by parts Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Seyyedfaridoddin Fattahpour | Sara Kadkhodaei
Source: arXiv (Cornell University) (preprint, 2023-07-03)
Field: Materials Science — Materials Chemistry

Enhancing ab initio diffusion calculations in materials through Gaussian process regression Saddle point search schemes are widely used to identify the transition state of different processes, like chemical reactions, surface and bulk diffusion, surface adsorption, and many more. In solid-state materials with relatively large numbers of atoms, the minimum mode following schemes such as dimer are commonly used because they alleviate the calculation of the Hessian on the high-dimensional potential energy surface. Here, we show that the dimer search can be further accelerated by leveraging Gaussian process regression (GPR). The GPR serves as a surrogate model to feed the dimer with the required energy and force input. We test the GPR- accelerated dimer method for predicting the diffusion coefficient of vacancy-mediated self-diffusion in bcc molybdenum and sulfur diffusion in hexagonal molybdenum disulfide. We use a multi-task learning approach that utilizes a shared covariance function between energy and force input, and we show that the multi-task learning significantly improves the performance of the GPR surrogate model compared to previously used learning approaches. Additionally, we demonstrate that a translation-hop sampling approach is necessary to avoid over-fitting the GPR surrogate model to the minimum-mode-following pathway and thus succeeding in locating the saddle point. We show that our method reduces the number of evaluations to a fraction of what a conventional dimer requires. Kriging ; Ab initio ; Diffusion ; Saddle point ; Materials science ; Statistical physics ; Computer science ; Mathematics ; Physics ; Quantum mechanics ; Machine learning ; Geometry Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Felix Thelen | Lars Banko | Rico Zehl | Sabrina Baha | Alfred Ludwig
Source: arXiv (Cornell University) (preprint, 2023-06-29)
Field: Materials Science — Materials Chemistry

Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements High-throughput experimentation enables efficient search space exploration for the discovery and optimization of new materials. However, large search spaces of, e.g., compositionally complex materials, require decreasing characterization times significantly. Here, an autonomous measurement algorithm was developed, which leverages active learning based on a Gaussian process model capable of iteratively scanning a materials library based on the highest uncertainty. The algorithm is applied to a four-point probe electrical resistance measurement device, frequently used to obtain indications for regions of interest in materials libraries. Ten materials libraries with different complexities of composition and property trends are analyzed to validate the model. By stopping the process before the entire library is characterized and predicting the remaining measurement areas, the measurement efficiency can be improved drastically. As robustness is essential for autonomous measurements, intrinsic outlier handling is built into the model and a dynamic stopping criterion based on the mean predicted covariance is proposed. A measurement time reduction of about 70-90% was observed while still ensuring an accuracy of above 90%. Outlier ; Robustness (evolution) ; Computer science ; Covariance ; Throughput ; Gaussian process ; Characterization (materials science) ; Process (computing) ; Gaussian ; Algorithm ; Artificial intelligence ; Materials science ; Nanotechnology ; Mathematics ; Physics Machine Learning in Materials Science ; Machine Learning and Algorithms
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases Bayesian probability ; Bayesian optimization ; Noise (video) ; Bayesian inference ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Catalysis and Oxidation Reactions
Source: Research Square (preprint, 2023-02-02)
Field: Materials Science — Materials Chemistry

WITHDRAWN: A transferable force field for gallium nitride crystal growth from the melt using on-the-fly active learning Preprint ; Field (mathematics) ; Gallium nitride ; Crystal (programming language) ; Wish ; Work (physics) ; Computer science ; Materials science ; Nanotechnology ; Engineering ; Mechanical engineering ; Sociology ; Mathematics ; World Wide Web ; Programming language ; Pure mathematics Electronic and Structural Properties of Oxides ; Nanowire Synthesis and Applications ; Force Microscopy Techniques and Applications
Authors: William Millsaps | Jonathan Schwartz | Zichao Wendy Di | Yi Jiang | Robert Hovden
Source: arXiv (Cornell University) (preprint, 2023-07-21)
Field: Materials Science — Surfaces, Coatings and Films

Autonomous Electron Tomography Reconstruction with Machine Learning Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, compressed sensing tomography creates overly smoothed 3D reconstructions. Adding momentum to the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that compressed sensing is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based compressed sensing greatly reduces the required compute time$-$an 80% reduction was observed for the 3D reconstruction of SrTiO$_3$ nanocubes. Automated parameter selection is necessary for large scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials. Tomography ; Electron tomography ; Computer science ; Artificial intelligence ; Materials science ; Physics ; Nanotechnology ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Sai Prasad Gedam | Siva Chiriki | D. A. Padmavathi
Source: Journal of the Indian Chemical Society (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Advanced machine learning based global optimizations for Pt nanoclusters Nanoclusters ; Ground-penetrating radar ; Chemistry ; Bayesian optimization ; Gaussian ; Kriging ; Energy (signal processing) ; Chemical physics ; Nanotechnology ; Biological system ; Artificial intelligence ; Statistical physics ; Computer science ; Machine learning ; Computational chemistry ; Physics ; Materials science ; Radar ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalytic Processes in Materials Science
Authors: Linshuang Zhang | Manyi Yang | Shiwei Zhang | Haiyang Niu
Source: arXiv (Cornell University) (preprint, 2023-05-29)
Field: Materials Science — Materials Chemistry

Ab initio investigation of the crystallization mechanism of cadmium selenide Cadmium selenide (CdSe) is an inorganic semiconductor with unique optical and electronic properties that made it useful in various applications, including solar cells, light-emitting diodes, and biofluorescent tagging. In order to synthesize high-quality crystals and subsequently integrate them into devices, it is crucial to understand the atomic scale crystallization mechanism of CdSe. Unfortunately, such studies are still absent in the literature.To overcome this limitation, we employed an enhanced sampling-accelerated active learning approach to construct a deep neural potential with ab initio accuracy for studying the crystallization of CdSe.Our brute-force molecular dynamics simulations revealed that a spherical-like nucleus formed spontaneously and stochastically, resulting in a stacking disordered structure where the competition between hexagonal wurtzite and cubic zinc blende polymorphs is temperature-dependent. We found that pure hexagonal crystal can only be obtained approximately above 1430 K, which is 35 K below its melting temperature. We observed that the solidification dynamics of Cd and Se atoms were distinct due to their different diffusion coefficients. The solidification process was initiated by lower mobile Se atoms forming tetrahedral frameworks, followed by Cd atoms occupying these tetrahedral centers and settling down until the third-shell neighbor of Se atoms sited on their lattice positions. Therefore, the medium-range ordering of Se atoms governs the crystallization process of CdSe. Our findings indicate that understanding the complex dynamical process is the key to comprehending the crystallization mechanism of compounds like CdSe, and can shed lights in the synthesis of high-quality crystals. Crystallization ; Wurtzite crystal structure ; Materials science ; Chemical physics ; Cadmium selenide ; Ab initio ; Crystallography ; Atom (system on chip) ; Nanotechnology ; Quantum dot ; Chemistry ; Hexagonal crystal system ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Quantum Dots Synthesis And Properties
Authors: special to C EN Nina Notman
Source: C&EN Global Enterprise (article, 2023-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

James Flack Norris Award to Renée Cole, Juliette Lantz, and Suzanne Ruder Renée Cole from the University of Iowa, Juliette Lantz from Drew University, and Suzanne Ruder from Virginia Commonwealth University share the 2023 James Flack Norris Award for Outstanding Achievement in the Teaching of Chemistry. The trio are recognized for their collaborative work on the Enhancing Learning by Improving Process Skills in STEM (ELIPSS) project. The purpose of ELIPSS is to enable assessment and self-assessment of student-centered active learning for a variety of professional skills, such as teamwork, interpersonal communication, critical thinking, and problem-solving. The three awardees are also active participants in the Process Oriented Guided Inquiry Learning Project . The James Flack Norris Award has been awarded annually by the American Chemical Society Northeastern Section since 1950. Further information can be found on the section website, www.nesacs.org . Teamwork ; Commonwealth ; Psychology ; Interpersonal communication ; Library science ; Medical education ; Management ; Medicine ; Political science ; Computer science ; Social psychology Various Chemistry Research Topics
Authors: Sergei Manzhos | Manabu Ihara
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Kernel methods such as kernel ridge regression and Gaussian process regressions with Matern type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel tends to become so large that the method effectively degenerates into a low-order polynomial regression and therefore loses any advantage over such regression. This is demonstrated theoretically as well as numerically on the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium size molecules and on the importance of orders-of-coupling based models for preserving the advantages of kernel methods with Matern type kernels or on the use of physically-motivated (reproducing) kernels. Kernel (algebra) ; Polynomial regression ; Mathematics ; Kernel method ; Curse of dimensionality ; Polynomial ; Applied mathematics ; Polynomial kernel ; Kernel regression ; Dimension (graph theory) ; Exponential function ; Regression ; Mathematical optimization ; Statistics ; Computer science ; Artificial intelligence ; Mathematical analysis ; Support vector machine ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies