jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhonghai He | Kun Shen | Xiaofang Zhang
Source: Journal of Near Infrared Spectroscopy (article, 2023-11-08)
Field: Chemistry — Analytical Chemistry

Active learning sample selection based on multicriteria In multivariate calibration problems, model performance is affected significantly by the calibration samples used during model building. In recent years, active learning methods have become one of the best methods for sample selection. However, most active learning methods only select instances from prediction uncertainty or sample space distance, and these single-criteria methods tend to select undesired samples. In addition, sample density characterizes the spatial information carried by the sample, but few studies in quantitative analysis utilize sample density alone to select calibration samples. Considering these issues, based on the k-means clustering algorithm, this paper proposes an active learning sample selection method (Diversity Informativeness Density Active Learning, DIDAL), which combines the three criteria of diversity, informativeness and sample density. The most representative sample is iteratively selected for - addition to the calibration set for modeling and estimating the chemical concentration of analytes. Soybean meal and soy sauce samples were analyzed by DIDAL and compared with existing sample selection methods. The prediction results show that the DIDAL algorithm significantly outperforms several existing algorithms and is close to the performance of full-sample modeling. A model with high prediction accuracy can be constructed by selecting only a few samples using the DIDAL method. Sample (material) ; Calibration ; Selection (genetic algorithm) ; Computer science ; Cluster analysis ; Sample size determination ; Data mining ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Analytical Chemistry and Chromatography
Authors: Haruki Ohashi | Masaaki Baba | Masatoshi Takeda | Noboru Yamada
Source: The Proceedings of the Thermal Engineering Conference (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Study of Carnot Battery System with Thermoelectric Module and Thermal Energy Storage Material This paper describes the experimental results of Carnot battery system using thermoelectric modules (TEM) and phase change materials (PCM). In this system, TEM is driven as a heat pump to convert electricity into heat which is stored in PCM in the charging mode. Then, electricity is generated by TEM from the temperature difference between the PCM and the heat sink in the discharging mode. The experimental system performed low round-trip efficiency of 0.33%. The effect of thermal properties of PCM on the round-trip efficiency was investigated via Bayesian optimization, showing that the round-trip efficiency can improve by a factor of 1.4 with the best set of the given thermal properties, however it is still below 1%. Further study to clarify the energy loss mechanism is necessary. Computer science Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Advanced Materials and Semiconductor Technologies
Authors: Fuga Kumagai | Kazuhiro Gotoh | S. Miyamoto | Shinya Kato | Kentaro Kutsukake | Noritaka Usami | Yasuyoshi Kurokawa
Source: Research Square (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells Abstract Silicon quantum dot multilayer (Si-QDML) is a promising material for a light-absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley-Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS ( σ p / σ d ) was calculated as the ratio of photo conductivity ( σ p ) and dark conductivity ( σ d ) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage ( V OC ) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS. Materials science ; Quantum dot ; Silicon ; Dangling bond ; Optoelectronics ; Hydrogen ; Solar cell ; Plasma ; Band gap ; Quantum efficiency ; Chemical vapor deposition ; Carrier lifetime ; Conductivity ; Chemistry ; Physics Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; Nanowire Synthesis and Applications
Authors: Yanyan Liang | Matous Mrovec | Yury Lysogorskiy | Miquel Vega‐Paredes | Christina (Prof. Dr. rer. nat.) Scheu | Ralf Drautz
Source: Dokumentenrepositorium der RUB (Ruhr University Bochum) (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Atomic cluster expansion for Pt–Rh catalysts Insight into structural and thermodynamic properties of nanoparticles is crucial for designing optimal catalysts with enhanced activity and stability. In this work, we present a semi-automated workflow for parameterizing the atomic cluster expansion (ACE) from ab initio data. The main steps of the workflow are the generation of training data from accurate electronic structure calculations, an efficient fitting procedure supported by active learning and uncertainty indication, and a thorough validation. We apply the workflow to the simulation of binary Pt–Rh nanoparticles that are important for catalytic applications. We demonstrate that the Pt–Rh ACE is able to reproduce accurately a broad range of fundamental properties of the elemental metals as well as their compounds while retaining an outstanding computational efficiency. This enables a direct comparison of atomistic simulations to high-resolution experiments. Workflow ; Cluster expansion ; Cluster (spacecraft) ; Catalysis ; Ab initio ; Nanoparticle ; Range (aeronautics) ; Molecular dynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hakkı Kadayifçi | Burcu Işık | Funda Ekici
Source: Journal of Chemical Education (article, 2023-07-19)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching Some Features of Competitive Elimination and Substitution Reactions of Alcohols through a Guided Inquiry Laboratory Activity In this study, students’ understanding of some important features of competitive elimination and substitution reactions of alcohols was examined based on their participation in a guided inquiry activity of two organic laboratory experiments: the dehydration of cyclohexanol and the conversion of tert-butyl alcohol into tert-butyl chloride. Students performed the experiments comparatively and integrally during the activity. The participants in the study consisted of 29 undergraduate students enrolled in an Organic Chemistry Laboratory I course. At the end of the activity, a written quiz was administered to determine the students’ understanding level. In order to determine their views about the activity, a survey was administered, and 18 students were interviewed. The students’ decisions and justifications about the experimental conditions (acid, concentration, and temperature), the type of reactions, and the driving force in the reactions were determined by analyzing the written quiz. Most of the students made appropriate decisions and justifications. In follow-up interviews, the students reported that overall, they engaged in active learning during the guided inquiry activity, that their learning was permanent, and that they had a positive attitude. The results of the study support the idea that incorporating a conceptual comparison into inquiry activities contributes to student understanding. Cyclohexanol ; Chemistry ; Psychology ; Mathematics education ; Organic chemistry ; Catalysis Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Vikram Bhamidipati | David Říha | Eric Zhou | Michael K. Ballard | David Mollenhauer
Source: AIAA SCITECH 2023 Forum (article, 2023-01-19)
Field: Materials Science — Polymers and Plastics

Uncertainty Quantification of Fiber Volume Fraction of Fiber Bundles using Digital Chains View Video Presentation: https://doi.org/10.2514/6.2023-2425.vid Accurate simulation of deformation and damage in textile composites requires accurate characterization of the tow geometry and fiber volume fraction. Often it is computationally prohibitive to directly model the mesoscale in a macroscale finite element simulation. Instead, a set of effective properties for a repeating unit cell can determined that allow each unit cell to be modeled as a homogeneous material. The objective of this research study is to investigate the effect of model parameter uncertainty, such as the stabilization stiffness and contact penalty parameters, on the important quantities of interest (QoI), such as the final nodal displacements and fiber volume fractions at various locations in the tow geometry. Understanding the relationship between uncertainty for the input and output of these process simulations are important, since the quantities of interest are understood to greatly affect the accuracy of subsequent performance predictions for a textile composite. Results from a Gaussian Process model can help an analyst address tradeoff between accuracy and computational cost by determining a set of parameters that yield the most accurate results while also being computationally tractable. Volume fraction ; Fiber ; Stiffness ; Stiffness matrix ; Process (computing) ; Finite element method ; Computer science ; Volume (thermodynamics) ; Biological system ; Set (abstract data type) ; Materials science ; Algorithm ; Mathematical optimization ; Structural engineering ; Mathematics ; Composite material ; Engineering ; Physics Textile materials and evaluations ; Computer Graphics and Visualization Techniques ; Computational Geometry and Mesh Generation
Authors: Jun Wang | Rahul Rai
Source:  (article, 2023-08-20)
Field: Materials Science — Materials Chemistry

Design and Optimization of Quasi-Periodic Volumetric Microstructures by Aperiodic Tiling and Implicit Function Abstract Materials with intricate microstructures exhibit unique properties, including a large surface area and a highly distributed load path. This makes them very effective in engineering applications where weight reduction and structural strength are critical. In this paper, we propose two different frameworks to model quasi-periodic microstructures. The quasi-periodic structure can be formed following aperiodic tiling rules and isoparametric transformation, which infills single-type unit cells into the aperiodic hexahedral tiles. For the other method, the proposed implicit function-based representation provides compact, precise, and arbitrarily parameterized models of coherent microstructures, which contain more than one type of unit cell that follows the cubic periodic repeating manner. Compared to periodic structures that possess perfect regularity and non-periodic structures in a disordered random manner, a quasi-periodic structure shows a perfect long-range order with short-range variability, which overcomes the shortcomings of periodic structures with limited design space and non-periodic structures with unmanageable random style. This paper also demonstrates the practicability of the proposed quasi-periodic structures by utilizing a surrogate model-based optimization scheme to design lightweight but high-strength functional parts with the optimized quasi-periodic structures filled internally. Aperiodic graph ; Parameterized complexity ; Computer science ; Range (aeronautics) ; Representation (politics) ; Hexahedron ; Topology (electrical circuits) ; Mathematics ; Algorithm ; Finite element method ; Materials science ; Physics ; Combinatorics Quasicrystal Structures and Properties ; Cellular and Composite Structures ; Advanced Materials and Mechanics
Authors: Viktor Zaverkin | David Holzmüller | Henrik Christiansen | Federico Errica | Francesco Alesiani | Makoto Takamoto | Mathias Niepert | Johannes Kästner
Source: arXiv (Cornell University) (preprint, 2023-12-03)
Field: Materials Science — Materials Chemistry

Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate pools, aims to address this objective. Existing biased and unbiased MD-simulation methods, however, are prone to miss either rare events or extrapolative regions -- areas of the configurational space where unreliable predictions are made. This work demonstrates that MD, when biased by the MLIP's energy uncertainty, simultaneously captures extrapolative regions and rare events, which is crucial for developing uniformly accurate MLIPs. Furthermore, exploiting automatic differentiation, we enhance bias-forces-driven MD with the concept of bias stress. We employ calibrated gradient-based uncertainties to yield MLIPs with similar or, sometimes, better accuracy than ensemble-based methods at a lower computational cost. Finally, we apply uncertainty-biased MD to alanine dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD. Molecular dynamics ; Computer science ; Benchmark (surveying) ; A priori and a posteriori ; Space (punctuation) ; Statistical physics ; Set (abstract data type) ; Uncertainty quantification ; Interatomic potential ; Algorithm ; Machine learning ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Timo Schmalofski | Martin Kroll | Holger Dette | Rebecca Janisch
Source: Materialia (article, 2023-07-29)
Field: Materials Science — Materials Chemistry

Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom Many materials processes and properties depend on the anisotropy of the energy of grain boundaries, i.e. on the fact that this energy is a function of the five geometric degrees of freedom (DOF) of the interface. To access this parameter space in an efficient way and to discover energy cusps in unexplored regions, a method was recently established, which combines atomistic simulations with statistical methods (kroll et al., 2022). This sequential sampling technique is now extended in the spirit of an active learning algorithm by adding a criterion to decide when the sampling has advanced enough to stop. In this instance, two parameters to analyse the sampling results on the fly are introduced: the number of cusps, which correspond to the most interesting and important regions of the energy landscape, and the maximum change of energy between two sequential iterations. Monitoring these two quantities provides valuable insight into how the subspaces are energetically structured. The combination of both parameters provides the necessary information to evaluate the sampling of the 2D subspaces of grain boundary plane inclinations of even non-periodic, low angle grain boundaries. With a reasonable number of data points in the initial design, only a few appropriately chosen sequential iterations already improve the accuracy of the sampling substantially and unknown cusps can be found within a few additional sequential steps. Linear subspace ; Degrees of freedom (physics and chemistry) ; Sampling (signal processing) ; Energy (signal processing) ; Boundary (topology) ; Plane (geometry) ; Sequential sampling ; Space (punctuation) ; Algorithm ; Computer science ; Statistical physics ; Mathematics ; Mathematical analysis ; Geometry ; Statistics ; Physics Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; Metallurgy and Material Forming
Authors: Hui Tan | Wei Heng Chng | Christian Chonardo | Magdeline Tao Tao Ng | Fun Man Fung
Source: Journal of Chemical Education (article, 2023-02-14)
Field: Chemistry — Physical and Theoretical Chemistry

Retraction of “How Chemists Achieve Active Learning Online During the COVID-19 Pandemic: Using the Community of Inquiry (CoI) Framework to Support Remote Teaching” ADVERTISEMENT RETURN TO ISSUEPREVRetractionNEXTORIGINAL ARTICLEThis notice is a retraction.Retraction of "How Chemists Achieve Active Learning Online During the COVID-19 Pandemic: Using the Community of Inquiry (CoI) Framework to Support Remote Teaching"Hui Ru TanHui Ru TanMore by Hui Ru Tan, Wei Heng ChngWei Heng ChngMore by Wei Heng Chng, Christian ChonardoChristian ChonardoMore by Christian Chonardo, Magdeline Tao Tao NgMagdeline Tao Tao NgMore by Magdeline Tao Tao Ng, and Fun Man Fung*Fun Man FungMore by Fun Man FungCite this: J. Chem. Educ. 2023, 100, 3, 1410Publication Date (Web):February 14, 2023Publication History Published online14 February 2023Published inissue 14 March 2023https://pubs.acs.org/doi/10.1021/acs.jchemed.3c00088https://doi.org/10.1021/acs.jchemed.3c00088retractionACS PublicationsCopyright © 2023 American Chemical Society and Division of Chemical Education, Inc. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1885Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (811 KB) Get e-Alertsclose Get e-Alerts Coronavirus disease 2019 (COVID-19) ; Pandemic ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; 2019-20 coronavirus outbreak ; Mathematics education ; Computer science ; Virology ; Medicine ; Psychology ; Infectious disease (medical specialty) ; Pathology Various Chemistry Research Topics ; Innovative Teaching Methods ; Biomedical and Engineering Education
Authors: Balasaheb J. Nagare | Sajeev Chacko | D. G. Kanhere
Source: arXiv (Cornell University) (preprint, 2023-09-16)
Field: Materials Science — Materials Chemistry

Machine-Learned Potential Energy Surfaces for Free Sodium Clusters with Density Functional Accuracy: Applications to Melting Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large sample of over 100,000 data points computed for clusters in the size range of N = 40 - 200, using the density-functional method as implemented in the VASP package. Two models have been developed, model M1 using data for N=55 only, and model M2 using additional data from larger clusters. The models are intended for computing thermodynamic properties using molecular dynamics. Hence, particular attention has been paid to improve the fitting of the forces. Interestingly, it turns out that the best fit can be obtained by carefully selecting a smaller number of data points viz. 1,900 and 1,300 configurations, respectively, for the two models M1 and M2. Although it was possible to obtain a good fit using the data of Na55 only, additional data points from larger clusters were needed to get better accuracies in energies and forces for larger sizes. Surprisingly, the model M1 could be significantly improved by adding about 50 data points per cluster from the larger sizes. Both models have been deployed to compute the heat capacities of Na55 and Na147 and to obtain about 40 isomers for larger clusters of sizes N = 147, 200, 201, and 252. There is an excellent agreement between the computed and experimentally measured melting temperatures. The geometries of these isomers when further optimized by DFT, the mean absolute error in the energies between DFT results and those of our models is about 7 meV/atom or less. The errors in the interatomic bond lengths are estimated to be below 2% in almost all the cases. Gaussian ; Cluster (spacecraft) ; Range (aeronautics) ; Data point ; Energy (signal processing) ; Statistical physics ; Density functional theory ; Algorithm ; Computer science ; Materials science ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Yi Liu | Ke Sun | Han Hu | Zirong He | Zhuojun Xiao | Xiaoqian Jin | Zheng Chen
Source: Research Square (preprint, 2023-09-22)
Field: Materials Science — Materials Chemistry

One-pot green solid-state synthesis of Cu2O/microcrystalline cellulose composite with high anti-pathogenic activity Microcrystalline cellulose ; Composite number ; Cellulose ; Microcrystalline ; Solid-state ; Chemical engineering ; Materials science ; Chemistry ; Composite material ; Organic chemistry ; Crystallography ; Physical chemistry ; Engineering Copper-based nanomaterials and applications ; Nanoparticles: synthesis and applications ; Advanced Photocatalysis Techniques