jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ulderico Di Caprio | Jan Dègreve | Peter Hellinckx | Steffen Waldherr | M. Enis Leblebici
Source:  (preprint, 2023-03-14)
Field: Chemistry — Physical and Theoretical Chemistry

hybridGamma: a thermodynamically consistent framework for hybrid modelling of activity coefficients Predicting molecular interactions is a crucial step for chemical process modelling.It requires the full knowledge of the analyzed system, however, this is often impossible in complex real-world cases.Machine learning (ML) techniques overcome this bottleneck and enhance systems predictability using data.Hybrid modelling (HM) is an established technique combining first-principle information and ML techniques.This work introduces a mathematical framework to predict activity coefficients employing HM approach.The obtained models are physically consistent and can handle systems with unknown components or external sources of deviation.The framework is validated on experimental and in-silico cases employing different training approaches.In all the tested cases, the HM showed remarkable prediction capabilities with coefficients of determination R 2 above 0.98 for the predicted variables.This work proposes and develops a novel way to approach the HM of molecular interactions by embedding physical laws within the model structure.We encountered three main benefits in applying thermodynamically consistent HMs for activity coefficients: the reduction of tenable parameters, the increased prediction capabilities, and the physicalconsistent behavior of the model. Econometrics ; Computer science ; Statistical physics ; Environmental science ; Mathematics ; Physics thermodynamics and calorimetric analyses ; Chemical Thermodynamics and Molecular Structure ; Machine Learning in Materials Science
Authors: Lifeng Fan | Zhiyu Guo | Erbin Yue | Jianzhong He
Source: Metals (article, 2023-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effect of Secondary Cold Rolling Reduction Rate on Secondary Recrystallization Behavior of CGO Steel With the implementation of the “double carbon” policy in various countries in the world, the demand for grain-oriented electrical steel with low iron loss and high magnetic induction is increasing. Reducing the thickness of the steel sheets is an effective method to reduce the iron loss.The sheet thickness reduction means the increasing cold rolling reduction rate, especially the secondary cold rolling reduction rate, will directly affect the texture evolution of the secondary recrystallization process. In this paper, the secondary cold rolling reduction rate of commercial grain-oriented silicon steel was studied by means of X-Ray Diffraction, Electron Backscatter Diffraction. The results showed that Ultra-thin oriented silicon steel cannot be obtained by increasing the secondary cold rolling reduction rate alone; the optimum secondary cold rolling reduction rate was 59.2%. The grain size increased as the secondary cold rolling reduction rate increased and favorable texture content decreased, which was disadvantage to obtain a secondary recrystallization environment. Electrical steel ; Materials science ; Recrystallization (geology) ; Metallurgy ; Electron backscatter diffraction ; Grain size ; Annealing (glass) ; Silicon ; Dynamic recrystallization ; Microstructure ; Hot working Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Aleksandra Smejda-Krzewicka | Przemysław Rybiński | Witold Żukowski | Dariusz Bradło | Kinga Wencel | Gabriela Berkowicz-Płatek
Source: Materials (article, 2023-08-24)
Field: Materials Science — Polymers and Plastics

Cross-Linking Characteristics, Morphology, Dynamics, and Mechanical and Thermal Properties of Polychloroprene/Polybutadiene/Nano-Zinc (CR/BR/nZn) Compositions with Reduced Fire Hazard The properties of unconventional blends of crystallizable and thermo-cross-linkable polychloroprene (CR) with polybutadiene (BR) were investigated in this study. The compositions were prepared using the method of reactive processing and cross-linking in the presence of nano-sized zinc (nZn). The purpose of the research was to assess the efficacy of nano-zinc as a curing agent of polychloroprene and polybutadiene (CR/BR) composites and to obtain rubber goods characterized by increased flame resistance. The blends were filled with nano-silica (aerosil) and fillers of natural origin (chalcedonite or silitin). The cross-linking process was characterized by determining the kinetics curves, the equilibrium swelling, and the Mooney-Rivlin elasticity constants. The morphology of the vulcanizate surface was specified by scanning electron microscopy (SEM). The dynamic and mechanical properties, flammability, and toxicity of gaseous substances involved in thermal decomposition were determined. Mass changes and thermal effects were studied using simultaneous thermal analysis (STA). It was confirmed that nano-zinc is an efficient curing agent for the polychloroprene and polybutadiene compositions, with a satisfactory degree of cross-linking (α<sub>c</sub> = 0.10, CRI = 4.11 min<sup>-1</sup>), good mechanical strength (TS<sub>b</sub> = 5 MPa), satisfactory tear resistance (T<sub>s</sub> = 2.9 N/mm), and very high flame resistance (OI = 30%, HRR<sub>max</sub> = 283 kW/m<sup>2</sup>). Filled products could be used as non-combustible materials, confirming the low fire hazard (1/t<sub>flashover</sub> = 3.5-6.4 kW/m<sup>2</sup>∙s). The most effective filler of the tested composites was nano-sized silica. Materials science ; Polybutadiene ; Composite material ; Natural rubber ; Chloroprene ; Tear resistance ; Nanocomposite ; Curing (chemistry) ; Zinc ; Elastomer ; Nano- ; Thermal decomposition ; Neoprene ; Chemical engineering ; Copolymer ; Polymer ; Chemistry ; Organic chemistry ; Metallurgy Polymer Nanocomposites and Properties ; Flame retardant materials and properties ; Thermal and Kinetic Analysis
Authors: Mattia Atzeni | Mrinmaya Sachan | Andreas Loukas
Source: arXiv (Cornell University) (preprint, 2023-06-05)
Field: Materials Science — Materials Chemistry

Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most recent language-complete instantiation (LARC) has been postulated as an important step towards general AI. Yet, even state-of-the-art machine learning models struggle to achieve meaningful performance on these problems, falling behind non-learning based approaches. We argue that solving these tasks requires extreme generalization that can only be achieved by proper accounting for core knowledge priors. As a step towards this goal, we focus on geometry priors and introduce LatFormer, a model that incorporates lattice symmetry priors in attention masks. We show that, for any transformation of the hypercubic lattice, there exists a binary attention mask that implements that group action. Hence, our study motivates a modification to the standard attention mechanism, where attention weights are scaled using soft masks generated by a convolutional network. Experiments on synthetic geometric reasoning show that LatFormer requires 2 orders of magnitude fewer data than standard attention and transformers. Moreover, our results on ARC and LARC tasks that incorporate geometric priors provide preliminary evidence that these complex datasets do not lie out of the reach of deep learning models. Prior probability ; Computer science ; Artificial intelligence ; Lattice (music) ; Generalization ; Visual reasoning ; Binary number ; Transformation geometry ; Theoretical computer science ; Mathematics ; Bayesian probability ; Arithmetic Machine Learning in Materials Science ; Topic Modeling
Authors: Dawei Duan | Hongzhong Ma | Qifan Yang | Nan Li
Source: Frontiers in Energy Research (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

Fault diagnosis of free-conducting particles within GIL based on vibration signals Accurate quantitative diagnosis of free-conducting particle faults plays an important role in improving the reliability of the gas insulated line (GIL) system. However, the existing fault diagnosis methods cannot accurately identify the free-conducting particle faults with different quantities and sizes. Motivated by this, this paper proposes a novel fault diagnosis method based on vibration signals, which integrates variational mode decomposition (VMD), self-adapting whale optimization algorithm-multiscale permutation entropy (SAWOA-MPE), and deep forest (DF). First, the raw vibration signals of free-conducting particle faults are decomposed via VMD, and the decomposed signals are reconstructed based on the correlation degree. Afterwards, SAWOA is employed to optimize the critical parameters of MPE, and the optimized MPE is further utilized to extract the fault features of the reconstructed signals. Finally, the extracted feature vectors are trained and tested to construct a valid DF classification model that identifies the free-conducting particle faults. The experimental results indicate that the identification accuracy of the proposed method can reach 99.5%. Moreover, comparative tests based on various feature vector extraction methods and classification models further validate the superiority of the proposed method. Vibration ; Fault (geology) ; Pattern recognition (psychology) ; Computer science ; Feature vector ; Feature extraction ; Hilbert–Huang transform ; Algorithm ; Artificial intelligence ; Acoustics ; Physics ; Computer vision High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Machine Fault Diagnosis Techniques
Authors: Pei Huang | Tong Shen | Zhenxing Liu | Renjun Dian | Wanlun Xu | Dan Wang
Source: Thermal Science (article, 2023-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal characteristics analysis of medium frequency transformer under multiple working conditions Medium frequency transformer plays an important role in high power AC-DC transformation in power grid, due to its high power density and high loss density, it brings difficulty to heat dissipation. Thermal reliability is crucial for transformer design, different from the previous work, the thermal characteristics of MFT under different working conditions are concerned and analyzed in this paper. The temperature field model of medium frequency transformer is established, and the temperature field characteristics of medium frequency transformer at rated load, no load, overload, fluctuating overload and short circuit conditions are simulated and analyzed, the results show that with the increase of load coefficient, the winding temperature increases obviously, the allowable load coefficient of medium frequency transformer can be determined by calculating the transformer temperature at different load coefficients, which lays a foundation for thermal reliability and safe operation of medium frequency transformer. Transformer ; Reliability (semiconductor) ; Materials science ; Thermal ; Electrical engineering ; Dissipation ; Computer science ; Reliability engineering ; Power (physics) ; Engineering ; Thermodynamics ; Physics ; Voltage Magnetic Properties and Applications ; High-Voltage Power Transmission Systems ; Power Transformer Diagnostics and Insulation
Authors: Xiu Zhou | Tian Tian | Peng Wu | Yan Luo | Jin Bai | Ninghui He | Xiuguang Li
Source: Frontiers in Energy Research (article, 2023-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analytical modeling and calculation of core grounding current in converter transformer In recent years, the calculation of the core grounding current of the converter transformer is difficult, low precision, and time-consuming, especially when the core is grounded by multiple points. This makes it difficult to analyze the core grounding current. To improve the accuracy of the calculation and reduce the complexity of the calculation, this paper carries out analytical calculation, simulation analysis, and experimental research on the grounding current of the converter transformer core. Firstly, a calculation method of core grounding current considering the winding connection of the converter transformer is proposed. In the implementation process, the converter transformer circuit model is established and the influence of saturation characteristics is considered, and the core grounding current is calculated and analyzed. Secondly, the grounding current of the converter transformer core is calculated by the finite element method (FEM). Finally, the proposed scheme is verified by experiments. The results show that the analytical method is close to the FEM and the experimental method, which indicates that the analytical method proposed can meet the accuracy requirements of engineering in this paper, and provides a new scheme for fast and accurate calculation of the grounding current of the converter transformer core. Ground ; Transformer ; Finite element method ; Current transformer ; Electronic engineering ; Electromagnetic coil ; Magnetic core ; Electrical engineering ; Engineering ; Computer science ; Voltage ; Structural engineering Magnetic Properties and Applications ; High-Voltage Power Transmission Systems ; Power Transformer Diagnostics and Insulation
Authors: Udemgba Chinonso Stanley | Amarachi Anyawu Solace | Université de Poitiers | Excel Obumneme Amaefule | Odoemelam Patience Ogechi | Odo Godfrey Ifeanyi | Okam Chukwu Emmanuel | Nnaemeka Nwachuckwu Chukwudi | Sandra Ijeoma Izuchukwu | Iheanacho Eberechi Clement | Ogbonna Chiemezuo Chinedu | Sunday Okechukwu | Moses Chukwuebuka Okwudifele
Source: Path of Science (article, 2023-01-31)
Field: Chemistry — Analytical Chemistry

Prediction of the Phytochemical Properties of Luffa Cylindrica Seed Oil Using Artificial Neural Network The research used an artificial neural network (ANN) to examine optimum extraction conditions and phytochemical contents of Luffa cylindrica seed oil. The oil yield was predicted using an artificial neural network. The performance of the ANN and response surface methodology models was compared. The optimum extraction yielded 7.567% oil yield, 185.676 mg/l phenol, and 45.087 mg/l terpineol at 75.57 °C extraction temperature, 5.77 h extraction time, and 10.68 g/mol n-hexane concentration, respectively. These data show that the oil output is poor but has a significant phenol and terpenoid content that may be employed in pharmaceutical sectors. The FT-IR analysis of Luffa cylindrica seed oil revealed a high level of unsaturated hydrocarbons and esters, making the oil appropriate for using in the paint industry and creating cosmetics. Phytochemical ; Extraction (chemistry) ; Phenol ; Hexane ; Yield (engineering) ; Terpenoid ; Chemistry ; Artificial neural network ; Chromatography ; Mathematics ; Botany ; Organic chemistry ; Biology ; Machine learning ; Materials science ; Computer science ; Stereochemistry Spectroscopy and Chemometric Analyses
Authors: Bowen Deng
Source:  (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

Catalysis distillation neural network for the few-shot open catalyst challenge &lt;p&gt;The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a “large-scale language model” in catalysis persist, and the recent success of ChatGPT's(Chat Generative Pre-trained Transformer) few-shot methods surpassing BERT (Bidirectional Encoder Representation from Transformers) underscores the importance of addressing limited data, expensive computations, time constraints and structure-activity relationship in research. Hence, the development of few-shot techniques for catalysis is critical and essential, regardless of present and future requirements. This paper introduces the Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning technology for predicting catalytic reactions on catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN).Our results demonstrate that CDGNN effectively learns embeddings from catalytic structures, enabling the capture of structure-adsorption relationships. This accomplishment has resulted in the utmost advanced and efficient determination of the reaction pathway for hydrogen peroxide, surpassing the current graph neural network approach by 16.1%. Consequently, CDGNN presents a promising approach for few-shot learning in catalysis. &lt;/p&gt; Artificial neural network ; Catalysis ; Computer science ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: James Stevenson | Garvit Agarwal | Leif D. Jacobson
Source: ChemRxiv (preprint, 2023-12-06)
Field: Materials Science — Materials Chemistry

Machine learning force field ranking of candidate solid electrolyte interphase structures in Li-ion batteries The Solid-Electrolyte Interphase (SEI) formed in lithium-ion batteries is a vital but poorly-understood class of materials, combining organic and inorganic components. An SEI allows a battery to function by protecting electrode materials from unwanted side reactions. We use a combination of classical sampling and a novel machine learning model to produce the first set of SEI candidate structures ranked by predicted energy, to be used in future machine learning applications and compared to experimental results. We hope that this work will be the start of a more quantitative understanding of lithium-ion battery interphases and an impetus to development of machine learning models for battery materials. Battery (electricity) ; Electrolyte ; Lithium (medication) ; Interphase ; Ranking (information retrieval) ; Lithium-ion battery ; Computer science ; Materials science ; Electrode ; Artificial intelligence ; Process engineering ; Nanotechnology ; Engineering ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Daniel J. Hutton | Kari E. Cordes | Carine Michel | Florian Göltl
Source: ChemRxiv (preprint, 2023-07-27)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction of activation energies for chemical reactions on metal surfaces In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which in many cases limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C, O, and H containing molecules on transition metal surfaces. We rely on generalized Bronsted-Evans-Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a Mean Absolute Error for activation energies within our test set of 0.14 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future. Catalysis ; Activation energy ; Chemistry ; Energy (signal processing) ; Chemical reaction ; Set (abstract data type) ; Computer science ; Computational chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Fu‐Qiang Gong | Yun‐Pei Liu | Ye Wang | E Weinan | Zhong‐Qun Tian | Jun Cheng
Source: ChemRxiv (preprint, 2023-11-28)
Field: Materials Science — Materials Chemistry

Anomalous entropic effect on catalysis via surface pre-melting of nanoclusters revealed by machine-learning molecular dynamics Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exits a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes. Nanoclusters ; Catalysis ; Nanomaterial-based catalyst ; Molecular dynamics ; Vacancy defect ; Melting point ; Materials science ; Chemical physics ; Atom (system on chip) ; Nanotechnology ; Nanoparticle ; Chemical engineering ; Chemistry ; Computational chemistry ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; nanoparticles nucleation surface interactions
Authors: Keletso Monareng | Rapela R. Maphanga | Petros S. Ntoahae
Source: MATEC Web of Conferences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning models for predicting density of sodium-ion battery materials With the unprecedented amounts of material data generated from high-throughput density functional theory, machine learning provides the ability to accelerate the discovery and design of new materials. In this work, machine learning regression techniques are applied to a large amount of data from Materials Project Database, to develop machine learning models capable of accurately predicting the densities of sodium-ion battery cathode materials. Different machine learning regression models are successfully developed and validated. Feature vectors derived from the properties of materials’ chemical compounds are evaluated. Extra trees regressor model is found to be the best model in predicting the density with an accuracy of 0.95 and 0.09 g/cm 3 coefficient of determination and mean square error, respectively. Battery (electricity) ; Machine learning ; Regression ; Computer science ; Mean squared error ; Feature (linguistics) ; Artificial intelligence ; Regression analysis ; Mathematics ; Statistics ; Power (physics) ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography