jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Piyushkumar Panchal | Bansi Shingala | Sanketsinh Thakor | Prince Jain | Chandan R. Vaja | Anand Joshi | Karan Shah | V. A. Rana | Jaivik Pathak
Source: Journal of Macromolecular Science Part B (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

XGBoost Regression Analysis of Dielectric Properties of Epoxy Resin with Inorganic Hybrid Nanofillers Our research described in this paper investigated the influence of varying weight percentages of a hybrid nano filler composed of titanium dioxide (TiO2) and silicon dioxide (SiO2) in an epoxy polymer composite. The dielectric properties were analyzed across a broadband frequency range. Additionally, X-ray diffraction (XRD) was employed to examine the structural changes and crystalline phases within the composite materials. The Influence of hybrid inorganic nano-fillers in the epoxy composite on the dielectric properties are discussed thoroughly in this report. To improve material design and prediction modeling, our research utilized machine learning algorithms, such as the XGBoost regressor. The aim was to assess the effectiveness of regression techniques in evaluating the material properties. By employing performance metrics, like R2 score and RMSE, the tests could achieve accuracies ranging from 30% to 60%. Simulation results demonstrated that machine learning-based methods can significantly expedite the forecasting of the dielectric properties, i.e. dielectric constant and dielectric loss at intermediate frequencies, thereby saving both time and energy. Epoxy ; Dielectric ; Materials science ; Composite material ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices ; Dielectric materials and actuators
Authors: Zhenyu Wang | Xiaoyang Wang | Xiaoshan Luo | Pengyue Gao | Ying Sun | Jian Lv | Handong Wang | Yanchao Wang | Yanming Ma
Source: Physical review. B./Physical review. B (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Concurrent learning scheme for crystal structure prediction Crystal structure prediction (CSP) and machine learning potential (MLP) are two fundamental methods for modern computational material discovery. While the former aims at efficient sampling of the potential energy surface (PES) for discovering new materials, the latter focuses on reproducing the PES to accelerate various atomic simulation tasks. In this work, we combine the two methods within a concurrent learning framework in an effort to generate efficient MLP models for accelerating CSP. The proposed scheme explores the PES through the swarm-intelligence calypso method, labels the most representative structures with quantum mechanical calculations, and learns the PES through a deep potential (DP) model. The process proceeds in an iterative, computationally efficient, and automated manner, leading to the collection of a most compact reference training set from which the resulting DP model is proven particularly suitable for accelerating calypso structure prediction. The scheme has been systematically benchmarked on binary magnesium-aluminium (Mg-Al) alloys and ternary lithium-lanthanum-hydrogen (Li-La-H) superhydrides, demonstrating its efficiency and reliability in DP model construction and calypso structure prediction. The proposed scheme represents a promising routine to perform the structure prediction of large or multicomponent systems. Scheme (mathematics) ; Computer science ; Materials science ; Statistical physics ; Computational physics ; Physics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Jayanta Bhusan Deb | Jihua Gou | Haonan Song | Chiranjit Maiti
Source: Journal of Composites Science (article, 2024-03-05)
Field: Materials Science — Ceramics and Composites

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study. Composite material ; Materials science ; Ceramic matrix composite ; Ceramic ; Matrix (chemical analysis) ; Ablation ; Engineering ; Aerospace engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Y. S. Cheng | I-Ting Sung | Chieh‐Ming Hsieh | Li‐Chiang Lin
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2024-08-29)
Field: Chemistry — Inorganic Chemistry

Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks Metal-organic framework ; Adsorption ; Modular design ; Sigma ; Biological system ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics ; Machine Learning in Materials Science
Authors: Felix Glaser | Simon De Kreijger | Katerina Achilleos | Lakshmi Narayan Satheesh | Alexia Ripak | Noémie Chantry | Céline Bourgois | Sophie Quiquempoix | Joffrey Scriven | Julien Rubens | Milan Vander Wee‐Léonard | Martin Daenen | Martin Gillard | Benjamin Elias | Ludovic Troian‐Gautier
Source: ChemPhotoChem (article, 2024-08-21)
Field: Chemistry — Organic Chemistry

A Compendium of Methodically Determined Ground‐ and Excited‐State Properties of Homoleptic Ruthenium(II) and Osmium(II) Photosensitizers Abstract The one‐pot synthesis of a total of 32 ruthenium(II) and osmium(II) photosensitizers bearing substituted 2,2’‐bipyridines, 1,10‐phenanthrolines, and diaza ligands is reported. Whereas most of these photosensitizers were already reported in the literature, the present study offers extensive datasets of ground‐ and excited‐state properties highly desirable for future development in e. g., machine learning, artificial intelligence, and photoredox catalysis. All photosensitizers absorbed light intensely in the visible part of the spectrum, with the Os(II) photosensitizers absorbing further into the red part. Excited‐state lifetimes and photoluminescence quantum yields were generally larger for Ru(II) photosensitizers than for Os(II) analogs, which agrees with the energy gap law. The excited‐state redox potentials were determined for all investigated photosensitizers covering a range of −0.21 to −1.35 V vs . SCE for excited‐state oxidation and 0.14 to 1.48 V vs . SCE for excited‐state reduction. A procedure for counterion exchange to generate the corresponding PF 6 − , Cl − , BF 4 − , NO 3 − , OTf − , ClO 4 − , and BAr F− is reported for six photosensitizers. The synthetic ease, detailed report of fundamental photophysical properties, and a broad range of excited‐state redox potentials open opportunities for systematic investigations in several applications and further streamline developments in photoredox catalysis. Excited state ; Ruthenium ; Osmium ; Homoleptic ; Chemistry ; Photochemistry ; Photoredox catalysis ; Redox ; Ground state ; Catalysis ; Inorganic chemistry ; Organic chemistry ; Atomic physics ; Physics ; Metal Radical Photochemical Reactions ; Metal complexes synthesis and properties ; Polyoxometalates: Synthesis and Applications
Authors: Nishtha Chaurawal | Sheikh Shahnawaz Quadir | Garima Joshi | Md. Abul Barkat | Abdulkareem A. Alanezi | Kaisar Raza
Source: International Journal of Biological Macromolecules (article, 2024-08-27)
Field: Materials Science — Biomaterials

Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central composite design (CCD) and ML algorithms were used to screen the effects of concentrations of both the polymers (polyethyleneimine and fucoidan) on the outcome responses, i.e., particle size and entrapment efficiency with defined constraints. The prediction from different ML models (bootstrap forest, K-nearest neighbors, artificial neural network, generalized regression-lasso and support vector machines) were compared with ANN-DoE model. The ANN-DoE model showed better accuracy and predictability and outperformed all the other models. This depicted that the concept of using ANN and DoE combination approach provided the best, uncomplicated and cost-effective way to optimized the nanoformulations. The optimized formulation generated from the ANN-DoE combined model was further evaluated for characterization and anticancer activity. The optimized SFB-SANPs were prepared using the polyelectrolyte complexation method with Polyethyleneimine (PEI) as a cationic polymer and fucoidan (FCD) as an anionic. The SFB-SANPs were nanometric in size (280.4 ± 0.089 nm) and slightly anionic in nature (zeta potential = -6.03 ± 0.92 mV) with an encapsulation efficiency of 95.56 ± 0.30 %. The drug release from SFB-SANPs was controlled and sustained in the cancer microenvironment (pH 5.0). The SFB-SANPs were compatible with red blood cells (RBCs), and the % hemolysis was found to be <5.0 %. The anticancer activity of the SFB-SANPs exhibited an IC<sub>50</sub> at 2.017 ± 0.516 μM against MDMB-231 cells, showing a significantly high inhibitory effect on breast cancer cell lines. Therefore, the nanocarriers developed using various ML tools inherit a huge promise in anticancer drug delivery. Fucoidan ; Sorafenib ; Artificial neural network ; Nanoparticle ; Zeta potential ; Central composite design ; Design of experiments ; Computer science ; Nanotechnology ; Chemistry ; Materials science ; Artificial intelligence ; Machine learning ; Response surface methodology ; Mathematics ; Polysaccharide ; Biochemistry ; Biology ; Cancer research Nanoparticle-Based Drug Delivery ; Seaweed-derived Bioactive Compounds ; Nanoplatforms for cancer theranostics
Authors: Zhiyong Zou | Meng-Hua Li | Qianlong Wang | Qing‐Song Wu | Jiangbo Zhen | Dongyu Yuan | Shutao Yin | Man Zhou | Qiang Cui | Lijia Xu
Source: Journal of Food Composition and Analysis (article, 2024-06-14)
Field: Chemistry — Analytical Chemistry

A non-destructive detection method of protein and TVB-N content changes in refrigerated and frozen-thawed salmon fillets using fluorescence hyperspectral technology Hyperspectral imaging ; Kjeldahl method ; Preprocessor ; Data pre-processing ; Computer science ; Artificial intelligence ; Hyperparameter ; Pattern recognition (psychology) ; Chemistry ; Nitrogen Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Daniel Scheiber | Vsevolod I. Razumovskiy | Oleg E. Peil | Lorenz Romaner
Source: Advanced Engineering Materials (article, 2024-06-22)
Field: Materials Science — Materials Chemistry

High‐Throughput First‐Principles Calculations and Machine Learning of Grain Boundary Segregation in Metals The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the material performance. However, the available computational data are scattered and inconsistent due to the use of different simulation parameters and methods. A high‐throughput study is presented on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first‐principles methodology. The data are evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, and W with the majority of the elements from the periodic table treated as segregating elements. Trends among the solute elements are analyzed and explained in terms of phenomenological models and the computed data are compared to the available literature data. The computed first‐principles data are used for a machine learning investigation, showing the capabilities for extrapolation from first‐principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties. Materials science ; Throughput ; Grain boundary ; Metallurgy ; Boundary (topology) ; Mechanical engineering ; Nanotechnology ; Engineering physics ; Microstructure ; Computer science ; Engineering ; Mathematical analysis Microstructure and mechanical properties ; Advanced materials and composites ; Advanced Materials Characterization Techniques
Authors: Pai‐Chun Wei | Cheng‐Rong Hsing | Chun‐Chuen Yang | Yung‐Hsiang Tung | Hsin‐Jay Wu | Wan‐Ting Yen | Yen‐Chung Lai | Jey‐Jau Lee | Chin‐Wei Wang | Hung‐Cheng Wu | H. D. Yang | Venkatesh Singaravelu | Xiaohe Miao | Andrea Giugni | Jia‐Kai Hu | Jui‐Han Fu | Vincent Tung | Jian He | Ching‐Ming Wei | Jr-Hau He
Source: Nano Energy (article, 2024-01-22)
Field: Materials Science — Materials Chemistry

Liquid-like thermal conductivity in solid materials: Dynamic behavior of silver ions in argyrodites Materials science ; Ionic liquid ; Thermal conductivity ; Chemical physics ; Thermoelectric materials ; Ionic conductivity ; Ion ; Molecular dynamics ; Fast ion conductor ; Thermoelectric effect ; Thermodynamics ; Physical chemistry ; Computational chemistry ; Composite material ; Chemistry ; Electrode ; Electrolyte ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Jyothish Joy | Anthony J. Schaefer | Matthew S. Teynor | Daniel H. Ess
Source: Journal of the American Chemical Society (article, 2024-01-19)
Field: Chemistry — Inorganic Chemistry

Dynamical Origin of Rebound versus Dissociation Selectivity during Fe-Oxo-Mediated C–H Functionalization Reactions The mechanism of catalytic C-H functionalization of alkanes by Fe-oxo complexes is often suggested to involve a hydrogen atom transfer (HAT) step with the formation of a radical-pair intermediate followed by diverging pathways for radical rebound, dissociation, or desaturation. Recently, we showed that in some Fe-oxo reactions, the radical pair is a nonstatistical-type intermediate and dynamic effects control rebound versus dissociation pathway selectivity. However, the effect of the solvent cage on the stability and lifetime of the radical-pair intermediate has never been analyzed. Moreover, because of the extreme complexity of motion that occurs during dynamics trajectories, the underlying physical origin of pathway selectivity has not yet been determined. For the reaction between [(TQA_Cl)Fe<sup>IV</sup>O]<sup>+</sup> and cyclohexane, here, we report explicit solvent trajectories and machine learning analysis on transition-state sampled features (e.g., vibrational, velocity, and geometric) that identified the transferring hydrogen atom kinetic energy as the most important factor controlling rebound versus nonrebound dynamics trajectories, which provides an explanation for our previously proposed dynamic matching effect in fast rebound trajectories that bypass the radical-pair intermediate. Manual control of the reaction trajectories confirmed the importance of this feature and provides a mechanism to enhance or diminish selectivity for the rebound pathway. This led to a general catalyst design principle and proof-of-principle catalyst design that showcases how to control rebound versus dissociation reaction pathway selectivity. Chemistry ; Dissociation (chemistry) ; Selectivity ; Catalysis ; Transition state ; Reaction coordinate ; Hydrogen atom ; Potential energy surface ; Photochemistry ; Computational chemistry ; Physical chemistry ; Molecule ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; CO2 Reduction Techniques and Catalysts ; Radical Photochemical Reactions
Authors: Ruge Cao | Jingxin Li | Han Ding | Tingting Zhao | Ziqi Guo | Yaying Li | X. Sun | Fang Wang | Qiu Ju
Source: Trends in Food Science & Technology (review, 2024-12-20)
Field: Chemistry — Analytical Chemistry

Synergistic approaches of AI and NMR in enhancing food component analysis: A comprehensive review Component (thermodynamics) ; Chemistry ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Metabolomics and Mass Spectrometry Studies
Authors: Chao Jiang | Chris A. Marianetti | Marat Khafizov | David H. Hurley
Source: npj Computational Materials (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Machine learning potential assisted exploration of complex defect potential energy surfaces Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work. Cluster (spacecraft) ; Density functional theory ; Ground state ; Photovoltaics ; Materials science ; Ranging ; Nanotechnology ; Computer science ; Chemical physics ; Physics ; Chemistry ; Computational chemistry ; Engineering ; Atomic physics ; Photovoltaic system Nuclear Materials and Properties ; Machine Learning in Materials Science ; Radioactive element chemistry and processing
Authors: G. M. Fazley Elahee | Xiang Cheng | Lihan Rong | Mingwei Xu | Lucio Souza | Rigoberto C. Advíncula | João M. Maia
Source: Polymer Engineering and Science (article, 2024-03-27)
Field: Materials Science — Polymers and Plastics

On the cogent formulation of an elastomeric silicone ink material for direct ink write (<scp>DIW</scp>) <scp>3D</scp> printing Abstract Adhesives and sealants show fine rheology with good physical and mechanical properties as viscous pastes, a possible starting point for developing direct ink writing (DIW) 3D printing ink. However, many commercial adhesives and sealants take days or weeks to cure fully. DIW 3D‐printed parts made directly from these sealants are not designed for a scalable manufacturing process and high‐volume production. Moreover, most of these adhesives and sealants have volume shrinkage during cure. A systematic understanding of formulation methods and design principles for an elastomeric silicone DIW ink can overcome these issues. This study presents the cogent formulation development of a 3D printable thermoset elastomer silicone that gels and cures isotropically in minutes, reducing cycle time for rapid ink development with no shrinkage during cure. More specifically, we outline the principles of raw material selection of a formulation to achieve excellent rheology, printability, synchronized working, and gel time fitting requirements closer to scalable manufacturing. The reaction kinetics and their corresponding 3D‐printed structural properties are also described. Interest in future work is toward a rational DIW 3D printing ink material development protocol and use of machine learning (ML). Highlights Formulation method flexibility and design principle of DIW ink. Raw material selection principle to achieve optimal rheology for DIW printing. Ink gel kinetics for large‐scale DIW manufacturing. Hydrosilylation conversion over time at different ambient temperatures. Structural properties of DIW 3D printed parts. Materials science ; Inkwell ; Silicone ; Rheology ; Elastomer ; Adhesive ; 3D printing ; Composite material ; Impression ; Shrinkage ; Raw material ; Thermosetting polymer ; Nanotechnology ; Computer science ; Chemistry Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Peng Han | Wen Wang | Jingyu Deng | Ke Qiao | Kai Zhou | Jia Lin | Yuye Zhang | Fengming Qiang | Kuaishe Wang
Source: Journal of Materials Processing Technology (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Achieving excellent superplasticity and predicting the elongations in ultrafine-grained Ti-4.5Al-3V-2Mo-2Fe titanium alloy prepared by friction stir processing Superplasticity ; Friction stir processing ; Materials science ; Alloy ; Titanium alloy ; Metallurgy ; Titanium ; Composite material Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Advanced Welding Techniques Analysis