jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Natalia �vila Reyes
Source:  (article, 2024-01-01)
Field: Materials Science — Biomaterials

Presentación Los estudios de la escritura, definidos como el campo que indaga sistemáticamente la enseñanza, aprendizaje, adquisición, desarrollo y usos de la escritura a lo largo del ciclo vital, son un campo vibrante de investigación y práctica que se ha ido consolidando en el mundo entero durante las últimas décadas.La escritura es un objeto complejo de investigación, que puede ser abordado desde una multiplicidad de perspectivas en las diferentes dimensiones que lo constituyen.Esto habilita una diversidad de paradigmas, métodos y conceptualizaciones, que se plasman justamente en la riqueza de las investigaciones producidas en Latinoamérica. Psychology Phytochemistry and Bioactive Compounds ; Phytochemical Studies and Bioactivities
Authors: Shuhei Nogami | Tomoyuki Tamura | Masayuki Karasuyama
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Search for Stable Structures of Adsorbed Molecules on Material Surfaces by Bayesian Optimization Titanium dioxide (TiO 2 ) is one of the most technologically promising oxides with a broad range of catalytic and photocatalytic activities, and first-principles simulations of molecular adsorption onto TiO 2 surface have been extensively performed. Our group experimentally reported that the formation of an amorphous structure on the TiO 2 surface enhances its catalytic activity, and is investigating the origin of high activity for amorphous surface. From the theoretical point of view, searching for stable adsorption sites on an amorphous surface model based on first-principles calculations need much high computational cost due to many adsorption sites in a large supercell. In this study, to efficiently explore stable adsorption sites on an amorphous surface, we applied Bayesian optimization, a global search method for black-box optimization, and found the most stable adsorption site of a water molecule. First-principles calculations are performed with SIESTA code [1] with localized basis, using the generalized gradient approximation (GGA) for the exchange-correlation energy functional. The amorphous surface models of TiO2 are generated by quenching a (101) surface model of anatase-type TiO2 crystal containing 162 atoms with first-principles molecular dynamics simulation. Fig. 1 shows one of TiO2 surface models. The adsorption energy of a molecule adsorbate is defined as E ads =E tot (surf+mol) -(E tot surf +E tot mol ) E tot (surf+mol) is the total energy of the surface with a molecule adsorbate, E tot surf is the total energy of the clean surface, and E tot mol is the energy of a molecular in a vacuum. Bayesian optimization is adopted for searching the lowest energy site with fixing TiO 2 surface configurations. A Gaussian kernel and an Expected Improvement (EI) for the acquisition function are used. Absorption energies were calculated on mesh points in the six-dimension space of the atomic positions of the oxygen (x, y, z) and rotational angles. First, we performed Bayesian optimization in the six-dimension space of the position and the angle, but this did not work well due to the discontinuity of the potential surface. Thus, we performed Bayesian optimization in the structural-descriptor space. We adopted the smooth overlap of atomic positions (SOAP) and the radial distribution function (RDF) as structural descriptors. The lowest energy sites were much efficiently found in the descriptor space, and we achieved high search efficiency. Figure 1 Bayesian optimization ; Adsorption ; Bayesian probability ; Molecule ; Materials science ; Computer science ; Chemistry ; Mathematics ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science
Authors: Zhen Zhang | Zhichu Ren | Ju Li
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

(Invited) Human-AI-Robotics Collaboration for Catalysts Discovery in Electrochemical Reactions The exploration and discovery of materials for electrochemical reactions have traditionally been a tedious and time-intensive process. The complexity inherent in the design of materials with high-dimensional parameters and the exhaustive nature of data acquisition are the primary causes of this bottleneck [1]. To overcome this challenge, we introduce the Copilot for Real-world Experimental Scientist (CRESt). CRESt employs a large multimodal model (LMM) to guide a robotic system that is adept at active learning (driven by the Gaussian process-Bayesian optimization process), thereby streamlining the entire workflow—ranging from the selection of composition through to the high-throughput synthesis of materials, electrochemical screening, materials characterization, and device testing—for the optimization of electrocatalysts (see video demonstrations in [2-4]). Through this method, we have been able to rapidly screen thousands of catalysts with minimal human intervention. Our development of high entropy alloy catalysts, which comprise more than five elements, have demonstrated exceptional performance in applications such as direct formate fuel cells and alkaline water electrolyzers. This strategy holds the promise of broad implementation across a spectrum of electrochemical systems. Reference: [1] Ren, Z., Ren, Z., Zhang, Z. et al. Autonomous experiments using active learning and AI. Nat. Rev. Mater. 8 , 563–564 (2023). [2] https://www.youtube.com/watch?v=iRauT95ECmo&amp;t=82s [3] https://www.youtube.com/watch?v=POPPVtGueb0&amp;t=340s [4] https://www.youtube.com/watch?v=sibCICesrEY Workflow ; Bottleneck ; Computer science ; Bayesian optimization ; Artificial intelligence ; Nanotechnology ; Machine learning ; Materials science ; Embedded system ; Database Machine Learning in Materials Science
Authors: Yuheng Song | Meijiao Qu | Mengqi Li | Kai Liu | Hanrui Zhu | Weifeng He
Source: Journal of Materials Research and Technology (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

A dynamic response prediction of ultra-high strain rates of composite materials based on a surrogate model Based on the propagation of laser-induced tensile waves in materials and the principle of material ply cracking, nondestructive testing technology for measuring the interfacial bonding force of composite materials can be developed. The study of the ultra-high strain rate dynamic response model of composite materials under the action of laser shock waves is crucial to this technique. Therefore, this paper proposes a method for constructing a dynamic response prediction model for composites based on data and a physical model driven by comprehensively considering the strain rate effect of materials. The constructed model can accurately predict the peak velocity and duration of the initial pulse of the velocity curve under the condition of ultra-high strain rate. The relative error between the predicted and simulated peak values is within 3%, and the relative error between the predicted and simulated initial pulse durations is within 5%. In addition, laser impact experiments were conducted on specimens of various thicknesses to validate the accuracy of the predicted data at ultra-high strain rates. The results show that the relative errors between the predicted and experimental peak values are all within 2%, and the relative errors of the initial pulse duration are within 9%. Composite number ; Surrogate model ; Strain (injury) ; Materials science ; Composite material ; Computer science ; Machine learning ; Medicine High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Mechanical Behavior of Composites
Authors: 浩 舟窪 | Hiroshi Funakubo | Maxim Al bertovich Ziatdinov | Maxim Ziatdinov
Source: Institutional Repositories DataBase (IRDB) (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning Active learning (machine learning) ; Bayesian probability ; Bayesian inference ; Statistical learning ; Bayesian optimization Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Micro and Nano Robotics
Authors: Maryanne Long
Source:  (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Evidence of Success: Active Learning and Culturally Relevant Pedagogy Implemented Within General Chemistry at a Hispanic-Serving Institution Institution ; Computer science ; Academic institution ; Mathematics education ; Pedagogy ; Chemistry ; Psychology ; Sociology ; Library science ; Social science Various Chemistry Research Topics ; Innovative Teaching Methods ; Innovative Teaching and Learning Methods
Authors: Daniel Scheiber | Natalia Bedoya‐Martínez | Bernd Schuscha | Dominik Brandl | Lorenz Romaner | Jürgen Spitaler
Source: BHM Berg- und Hüttenmännische Monatshefte (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Accelerated Materials Design and Materials Data Management at MCL Workflow ; Bayesian optimization ; Computer science ; Context (archaeology) ; Process (computing) ; Architecture ; Inverse ; Systems engineering ; Engineering ; Artificial intelligence ; Database ; Mathematics ; Programming language Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Mineral Processing and Grinding
Authors: Zhenquan Shen | Xiaochen Zhou | Dong Bian | Ming Zhao | Yafei Li | Yufeng Zheng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Biomaterials

Study of Structural Optimization on Mg-1ca Interference Screws Interference (communication) ; Structural engineering ; Computer science ; Orthodontics ; Materials science ; Engineering ; Telecommunications ; Medicine Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Dhruv Gamdha | Ryan Fair | Adarsh Krishnamurthy | Enrique D. Gomez | Baskar Ganapathysubramanian
Source: arXiv (Cornell University) (preprint, 2024-11-05)
Field: Materials Science — Surfaces, Coatings and Films

GRATEV2.0: Computational Tools for Real-time Analysis of High-throughput High-resolution TEM (HRTEM) Images of Conjugated Polymers Automated analysis of high-resolution transmission electron microscopy (HRTEM) images is increasingly essential for advancing research in organic electronics, where precise characterization of nanoscale crystal structures is crucial for optimizing material properties. This paper introduces an open-source computational framework called GRATEV2.0 (GRaph-based Analysis of TEM), designed for real-time analysis of HRTEM data, with a focus on characterizing complex microstructures in conjugated polymers, illustrated using Poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)] (PCDTBT), a key material in organic photovoltaics. GRATEV2.0 employs fast, automated image processing algorithms, enabling rapid extraction of structural features like d-spacing, orientation, and crystal shape metrics. Gaussian process optimization rapidly identifies the user-defined parameters in the approach, reducing the need for manual parameter tuning and thus enhancing reproducibility and usability. Additionally, GRATEV2.0 is compatible with high-performance computing (HPC) environments, allowing for efficient, large-scale data processing at near real-time speeds. A unique feature of GRATEV2.0 is a Wasserstein distance-based stopping criterion, which optimizes data collection by determining when further sampling no longer adds statistically significant information. This capability optimizes the amount of time the TEM facility is used while ensuring data adequacy for in-depth analysis. Open-source and tested on a substantial PCDTBT dataset, this tool offers a powerful, robust, and accessible solution for high-throughput material characterization in organic electronics. High-resolution transmission electron microscopy ; Throughput ; Conjugated system ; Resolution (logic) ; Polymer ; High resolution ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence ; Geology ; Remote sensing ; Composite material ; Telecommunications ; Transmission electron microscopy Electron and X-Ray Spectroscopy Techniques ; Medical Imaging Techniques and Applications ; Machine Learning in Materials Science
Authors: Apurva Kalia | Vladimir Porokhin | Benjamin T. Blackburn | Robyn A. C. Alba | Caroline M. Ajo‐Franklin | Emily Mevers | Soha Hassoun
Source: ChemRxiv (preprint, 2024-11-18)
Field: Chemistry — Organic Chemistry

The role of structural, pharmacokinetic and energy properties in the high-throughput prediction of redox potentials for organic molecules with experimental calibration Reduction/Oxidation (redox) potential of small organic compounds is a key property that drives innumerable chemical and biological electron transfer reactions. However, experimental measurement of redox potential is time-consuming and expensive, yielding few and small experimental measured datasets. Computational methods have previously been applied to create redox predictors applicable only to a specific dataset. In this work, we investigate the effectiveness of various descriptors, including structural and functional properties, molecular energies, and drug-like properties, to predict redox potential. We use Gaussian Process Regression (GPR) as a model, as it is suitable for fitting small datasets and has shown promise in predicting redox potential. We train and test our redox predictor on three organic molecule datasets. We demonstrate that a GPR-based redox predictor using a combination of molecular descriptors, DFT energies, and pharmacokinetic properties works well across the datasets. Finally, we test the trained model against an experimental dataset of quinones to show that the model makes predictions well correlated with experimental data. Calibration ; Redox ; Throughput ; Organic molecules ; Molecule ; Pharmacokinetics ; Computational chemistry ; Energy (signal processing) ; Chemistry ; Biological system ; Materials science ; Computer science ; Pharmacology ; Mathematics ; Organic chemistry ; Medicine ; Statistics ; Biology Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Chasheng He | Guihua Luo | Hongliang Duan | Yuanyuan Xie | Guijun Zhang | An Su | Weike Su
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Optimizing Phosphine Ligands for Ruthenium Catalysts in Asymmetric Hydrogenation of Β-Keto Esters: The Role of Water in Activity and Selectivity Phosphine ; Ruthenium ; Selectivity ; Catalysis ; Chemistry ; Asymmetric hydrogenation ; Combinatorial chemistry ; Organic chemistry ; Enantioselective synthesis Asymmetric Hydrogenation and Catalysis
Authors: Yonghui An | Yitian Wang
Source: Journal of Chemical Education (article, 2024-11-09)
Field: Chemistry — Physical and Theoretical Chemistry

A Biochemistry Teaching Experiment That Demonstrates the Digestion of Carbohydrates, Proteins, and Lipids in the Digestive Tract High school chemistry and biology courses often face challenges in integrating interdisciplinary and complex topics like human digestion, leading to unsatisfactory learning outcomes and diminished student interest. This study introduces a new teaching experiment that simulates the digestive process through hands-on chemical methods, allowing students to visualize biochemical changes and gain a deeper understanding of essential concepts. Structured around the CORE learning cycle (Chemical Observations, Representations, and Experimentation), the experiment guides students through enzyme functions, nutrient breakdown, and the testing of digestive products. This approach is designed to enhance comprehension, encourage inquiry, prompt students to formulate new hypotheses, design experiments, and foster scientific reasoning. By engaging students in active learning, the experiment stimulates curiosity and motivates exploration beyond the standard curriculum. Digestion (alchemy) ; Digestive tract ; Biochemistry ; Chemistry ; Lipid digestion ; Food science ; Chromatography ; Enzyme ; Medicine ; Lipase ; Internal medicine Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Roshini Ramachandran | J. Rachel Prado | Joe L. March
Source: CourseSource (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Kinesthetic and Collaborative Activity to Improve Conceptual Understanding of Chemical Equilibrium While teaching in large lecture halls, it is challenging even for experienced teachers to hold their students&rsquo; attention for the entire class period. Owing to the large enrollment in STEM introductory courses, it is especially crucial to incorporate active-learning strategies that enhance student engagement. We have developed a kinesthetic student activity to demonstrate the dynamic nature of chemical equilibrium in a large classroom setting. The activity consists of two parts; the first employs a demonstration utilizing student volunteers participating in a reversible chemical reaction while the second part involves students working in small groups using objects to track the movement of &ldquo;molecules&rdquo; for different reversible reactions. The primary learning objectives of the activity are to (i) perform the movement of molecules during chemical equilibrium, (ii) explain the dynamic nature of equilibrium, and (iii) connect the concepts of rate constants and equilibrium constant. This activity has been implemented in a large classroom of 100+ students who were life science majors, with student feedback stating that the activity enhanced their understanding of chemical equilibrium. Pre-activity and post-activity quiz performance were an indicator that learning gains were observed. This report provides a comprehensive set of resources for chemistry faculty to use while implementing this activity in their classroom. <em>Primary&nbsp;Image:</em>&nbsp;This activity has the potential to aid in addressing common misconceptions about the dynamic nature of chemical equilibrium. It also serves as a useful tool to create connections between chemical equilibrium and reaction kinetics. Kinesthetic learning ; Computer science ; Psychology ; Mathematics education Various Chemistry Research Topics ; Innovative Teaching and Learning Methods ; Advanced Text Analysis Techniques
Authors: Vincent Chiravalle
Source: The Journal of Chemical Physics (article, 2024-10-23)
Field: Materials Science — Materials Chemistry

Analysis of differential scanning calorimetry data for aged plutonium Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ɛ and ɛ to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ɛ phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ɛ to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time. Differential scanning calorimetry ; Activation energy ; Melting point ; Phase transition ; Thermodynamics ; Materials science ; Annealing (glass) ; Latent heat ; Alloy ; Chemistry ; Physics ; Metallurgy ; Physical chemistry ; Composite material Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear materials and radiation effects
Authors: Hiroyuki Toda | Kyosuke Hirayama | Shogo Yamaguchi | Hiro Fujihara | Ryota Higa | Kazuyuki Shimizu | Akihisa Takeuchi | Masayuki Uesugi
Source: Journal of the Japan Institute of Metals and Materials (article, 2024-02-24)
Field: Materials Science — Metals and Alloys

Dominant Factors Controlling the Initiation of Hydrogen Embrittlement in Al-Zn-Mg Alloy The surrogate-based microstructural optimization was applied to describe the relationship between local crystallographic microstructure and intergranular hydrogen embrittlement in an Al-Zn-Mg alloy. The support vector machine with an infill sampling criterion was used to realise high-accuracy optimisation with a limited size of data set. The methodology integrates thoroughgoing microstructural quantification, a couple of coarsening processes and the surrogate modelling. An objective function was defined together with 66 design parameters, which quantitatively express the size, shape, orientation and damage during specimen machining for surface grain boundaries and grains. The number of design parameters was then reduced from 66 to 3 during the two-step coarsening process. It has been clarified that intergranular crack initiation is described using the simple size of grains and grain boundaries together with grain boundary orientation with respect to the loading direction. It can be inferred that those design parameters are of crucial importance for crack initiation through the elevation in stress normal to grain boundaries. Correlation between the selected design parameters and crack initiation was rather weak compared to the past application of a similar technique to particle damage. The reason for this was discussed. The present approach offers a cost-efficient solution for the prevention of hydrogen embrittlement through 3D design of crystallographic microstructure, which cannot be obtained with the conventional approaches for developing materials. Hydrogen embrittlement ; Alloy ; Materials science ; Embrittlement ; Metallurgy ; Hydrogen ; Chemistry ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition
Authors: Mohammad Mahdi Sabouri Ghannad
Source:  (preprint, 2024-02-22)
Field: Materials Science — Materials Chemistry

Simulation and Response Prediction of Shape Memory Alloy (SMA)-based Steel Beam-column Connections with Steel Angles &lt;p&gt;The main goal of this research is to develop equations which can be used for predicting the cyclic response of steel beam-column connections with superelastic Shape Memory Alloy (SMA) bolts and steel angles. Finite-element (FE) models are developed to simulate the cyclic behavior of SMA connections. The results are then validated against seven specimens from a previous experimental study. The validated models are used to perform sensitivity and response surface studies. Beam web slenderness ratio, SMA bolt diameter, and angle thickness are identified as the most significant factors affecting the cyclic response of SMA connections. Angle yielding is also found to be the dominant limit state in all connections, confirming their robust self-centering response. It is recommended to use beams of lower depth to minimize residual deformations and avoid early bolt fracture in these connections. The developed surrogate models can be used to accurately predict the SMA connections’ cyclic response.&lt;/p&gt; SMA* ; Shape-memory alloy ; Structural engineering ; Materials science ; Finite element method ; Beam (structure) ; Pseudoelasticity ; Residual ; Fracture (geology) ; Column (typography) ; Bar (unit) ; Connection (principal bundle) ; Composite material ; Computer science ; Engineering ; Martensite ; Physics Shape Memory Alloy Transformations ; Structural Load-Bearing Analysis ; Structural Behavior of Reinforced Concrete