jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huaigang Su | Qin Zhao | Cheng Jiang | Yanan Wang | Yongfang Niu | Xuelian Li | Wenjing Lou | Yanxing Qi | Xiaobo Wang
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Preparation of highly dispersed SnO/TiO<sub>2</sub> catalysts and their performances in catalyzing polyol ester A series of stannous oxide supported on rutile titanium dioxide (SnO/TiO<sub>2</sub>) were prepared by a conventional incipient wetness impregnation method, and their performance as catalysts for fatty acid esterification reactions was investigated. The effects of Sn precursors (SnCl<sub>2</sub>·2H<sub>2</sub>O or SnC<sub>2</sub>O<sub>4</sub>), loading amounts (5-15%), and treating ambiences (air and N<sub>2</sub>) were explored. The optimized 10% SnO/TiO<sub>2</sub>-Cl with SnCl<sub>2</sub>·2H<sub>2</sub>O as the Sn precursor and thermal treatment in N<sub>2</sub> showed the best esterification performance. Specifically, 10% SnO/TiO<sub>2</sub>-Cl catalyzed the esterification process of trimethylolpropane and <i>n</i>-octanoic acid with a conversion of 99.6% over 5 h at 160 °C, and 10% SnO/TiO<sub>2</sub>-Cl was efficient for six catalytic cycles. Based on the results of X-ray diffraction (XRD), Raman spectra, high-resolution transmission electron microscopy (HRTEM), infrared spectra of pyridine adsorption (Py-IR), and ammonia temperature programmed desorption (NH<sub>3</sub>-TPD), the improved catalytic performance is supposed to be attributable to the high dispersion of the Sn species on 10% SnO/TiO<sub>2</sub>-Cl as the moderate Lewis acid sites. Polyol ; Catalysis ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry ; Polyurethane ; Engineering Chemical Synthesis and Reactions ; Oxidative Organic Chemistry Reactions ; Biodiesel Production and Applications
Authors: Ruolin He | Shaohua Gu | Jiazheng Xu | Xuejian Li | Hao Chen | Zhengying Shao | Fanhao Wang | Jiqi Shao | Wen‐Bing Yin | Long Qian | Zhong Wei | Zhiyuan Li
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-09-01)
Field: Chemistry — Spectroscopy

SIDERITE: Unveiling Hidden Siderophore Diversity in the Chemical Space Through Digital Exploration Abstract Siderophores, a highly diverse family of secondary metabolites, play a crucial role in facilitating the acquisition of the essential iron. However, the current discovery of siderophore relies largely on manual approaches. In this work, we introduced SIDERTE, a digitized siderophore information database containing 872 siderophore records with 649 unique structures. Leveraging this digitalized dataset, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules. Applying this method to 4,314 natural product molecules from TargetMol’s Natural Product Library for high throughput screening, we experimentally confirmed that 40 out of the 48 molecules predicted as siderophore candidates possessed iron-binding abilities. Expanding our approach to the COCONUT natural product database, we predicted a staggering 3,199 siderophore candidates, showcasing remarkable structure diversity that are largely unexplored. Our study provides a valuable resource for accelerating the discovery of novel iron-binding molecules and advancing our understanding towards siderophores. Siderophore ; Chemical space ; Natural product ; Cluster analysis ; Computational biology ; Combinatorial chemistry ; Biology ; Chemistry ; Computer science ; Artificial intelligence ; Drug discovery ; Bioinformatics ; Stereochemistry ; Biochemistry ; Gene Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Plant biochemistry and biosynthesis
Authors: Yifan Zhang | Bing Luo | Mingli Fu | Lei Jia | Chi Chen | Gang Zhou | Chuang Wang
Source: Molecules (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

Space Charge Characteristics at the Interface of Laminated Epoxy Resin In the design and manufacturing of epoxy resin insulation components, complex structures can be achieved through multiple pours, thereby forming the structure of interface of laminated epoxy resin. This type of interface structure is often considered a weak link in performance which can easily accumulate charges and cause electric field distortion. However, research on the interlayer interface of epoxy resin has received little attention. In this study, epoxy samples with and without interlayer interfaces were prepared, and the space charge accumulation characteristics and trap characteristics of the samples were analyzed via pulsed electro-acoustic (PEA) and thermally stimulated depolarization current (TSDC) methods. The experimental results indicate that the Maxwell-Wagner interface polarization model cannot fully explain the charge accumulation at the interface. Due to the influence of the secondary curing, the functional groups in the post-curing epoxy resin can move and react with the partially reacted functional groups in the prefabricated epoxy resin layer, resulting in a weak cross-linking network at the interface. With the increase in temperature, the molecular chain segments in the weak cross-linked region of the interface become more active and introduce deep traps at the interface, thereby exacerbating the accumulation of interface charges. In addition, due to the influence of interface polarization and weak cross-linking, the ability of the interface charges to cause field strength distortions decreases with the increase in applied field strength. This research study can provide a theoretical reference for the interfacial space charge transport characteristics of epoxy-cured cross-linked layers and provide ideas for regulating interfacial cross-linking to suppress interfacial charge accumulation. Epoxy ; Space charge ; Materials science ; Composite material ; Curing (chemistry) ; Polarization (electrochemistry) ; Electric field ; Interface (matter) ; Chemistry ; Contact angle ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Sensor and Energy Harvesting Materials
Authors: Ping Huang | Wenyao Yu | Chunhao Lu | Xinghua He | Yiyi Zhang | Yansong Liu | Jiaheng Zhou | Yuwang Liang
Source: Polymers (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

Quantitative Evaluation of Thermal Ageing State of Cross-Linked Polyethylene Insulation Based on Polarization and Depolarization Current The widespread use of cross-linked polyethylene (XLPE) as insulation in cables may be attributed to its outstanding mechanical and dielectric properties. In order to quantitatively evaluate the insulation status of XLPE after thermal ageing, an accelerated thermal ageing experimental platform is established. Polarization and depolarization current (PDC) as well as elongation at break of XLPE insulation under different ageing durations are measured. XLPE insulation state is determined by the elongation at break retention rate (ER%). Based on the extended Debye model, the paper proposed the stable relaxation charge quantity and the dissipation factor at 0.1 Hz to evaluate the insulation state of XLPE. The results show that the ER% of XLPE insulation decreases with the growth of ageing degree. The polarization and depolarization current of XLPE insulation will increase obviously with thermal ageing. Conductivity and trap level density will also increase. The number of branches of the extended Debye model increases, and new polarization types appear. Stable relaxation charge quantity and dissipation factor at 0.1 Hz proposed in this paper have a good fitting relationship with ER% of XLPE insulation, which can evaluate the thermal ageing state of XLPE insulation effectively. Materials science ; Composite material ; Polyethylene ; Dielectric ; Depolarization ; Polarization (electrochemistry) ; Dissipation factor ; Ageing ; Forensic engineering ; Optoelectronics ; Chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Jorge Torre | Suset Barroso‐Solares | Miguel Ángel Rodríguez‐Pérez | Javier Pinto
Source: Polymer (article, 2023-12-15)
Field: Materials Science — Polymers and Plastics

On the use of neural networks for the structural characterization of polymeric porous materials Characterization (materials science) ; Materials science ; Porosity ; Polymer science ; Porous medium ; Composite material ; Nanotechnology Polymer Foaming and Composites ; Injection Molding Process and Properties ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Kyoka Hara | Takuto Kojima | Kentaro Kutsukake | Hiroaki Kudo | Noritaka Usami
Source: APL Machine Learning (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

A machine learning-based prediction of crystal orientations for multicrystalline materials We established a rapid, low-cost, and accurate technique to measure crystallographic orientations in multicrystalline materials by optical images and machine learning. A long short-term memory neural network was trained with pairs of light reflection patterns and the correct orientations of each grain, successfully predicting orientation with an error median of 8.61°. The model was improved by diverse data taken from various incident light angles and by data augmentation. When trained on different incident angles, the model was capable of estimating different orientations. This is related to the geometrical configuration of the incident light angles and surface facets of the crystal. The failure in certain orientations is thought to be complemented by supplementary data taken from different incident angles. Combining data from multiple incident angles, we acquired an error median of 4.35°. Data augmentation was successfully performed, reducing error by an additional 35%. This technique can provide the crystallographic orientations of a 15 × 15 cm2 sized wafer in less than 8 min, while baseline techniques such as electron backscatter diffraction and Laue scanner may take more than 10 h. The rapid and accurate measurement can accelerate data collection for full-sized ingots, helping us gain a comprehensive understanding of crystal growth. We believe that our technique will contribute to controlling crystalline structure for the fabrication of high-performance materials. Materials science ; Wafer ; Orientation (vector space) ; Diffraction ; Optics ; Reflection (computer programming) ; Electron backscatter diffraction ; Crystal (programming language) ; Plane (geometry) ; Ray ; Artificial intelligence ; Computer science ; Optoelectronics ; Geometry ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Cultural Heritage Materials Analysis
Authors: Anton Ashuiev | A. Nobile | D. Trummer | Daniel Klose | С. А. Гуда | Оlga V. Safonova | Christophe Copéret | Alexander A. Guda | Gunnar Jeschke
Source: Angewandte Chemie International Edition (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Active Sites in Cr(III)‐Based Ethylene Polymerization Catalysts from Machine‐Learning‐Supported XAS and EPR Spectroscopy** The ethylene polymerization Phillips catalyst has been employed for decades and is central to the polymer industry. While Cr(III) alkyl species are proposed to be the propagating sites, there is so far no direct experimental evidence for such proposal. In this work, by coupling Surface organometallic chemistry, EPR spectroscopy, and machine learning-supported XAS studies, we have studied the electronic structure of well-defined silica-supported Cr(III) alkyls and identified the presence of several surface species in high and low-spin states, associated with different coordination environments. Notably, low-spin Cr(III) sites are shown to participate in ethylene polymerization, indicating that similar Cr(III) alkyl species could be involved in the related Phillips catalyst. X-ray absorption spectroscopy ; Polymerization ; Ethylene ; Electron paramagnetic resonance ; Alkyl ; Catalysis ; Polymer ; Organometallic chemistry ; Chemistry ; Spectroscopy ; Polymer chemistry ; Absorption spectroscopy ; Physical chemistry ; Materials science ; Photochemistry ; Organic chemistry ; Nuclear magnetic resonance ; Physics Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Catalysis and Oxidation Reactions
Authors: Satoki Ishiai | Katsuhiro Endo | Kenji Yasuoka
Source: The Journal of Chemical Physics (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Graph neural networks classify molecular geometry and design novel order parameters of crystal and liquid Molecular dynamics simulation produces three-dimensional data on molecular structures. The classification of molecular structure is an important task. Conventionally, various order parameters are used to classify different structures of liquid and crystal. Recently, machine learning (ML) methods have been proposed based on order parameters to find optimal choices or use them as input features of neural networks. Conventional ML methods still require manual operation, such as calculating the conventional order parameters and manipulating data to impose rotational/translational invariance. Conversely, deep learning models that satisfy invariance are useful because they can automatically learn and classify three-dimensional structural features. However, in addition to the difficulty of making the learned features explainable, deep learning models require information on large structures for highly accurate classification, making it difficult to use the obtained parameters for structural analysis. In this work, we apply two types of graph neural network models, the graph convolutional network (GCN) and the tensor embedded atom network (TeaNet), to classify the structures of Lennard-Jones (LJ) systems and water systems. Both models satisfy invariance, while GCN uses only length information between nodes. TeaNet uses length and orientation information between nodes and edges, allowing it to recognize molecular geometry efficiently. TeaNet achieved a highly accurate classification with an extremely small molecular structure, i.e., when the number of input molecules is 17 for the LJ system and 9 for the water system, the accuracy is 98.9% and 99.8%, respectively. This is an advantage of our method over conventional order parameters and ML methods such as GCN, which require a large molecular structure or the information of wider area neighbors. Furthermore, we verified that TeaNet could build novel order parameters without manual operation. Because TeaNet can recognize extremely small local structures with high accuracy, all structures can be mapped to a low-dimensional parameter space that can explain structural features. TeaNet offers an alternative to conventional order parameters because of its novelty. Computer science ; Artificial neural network ; Convolutional neural network ; Artificial intelligence ; Graph ; Algorithm ; Orientation (vector space) ; Cartesian coordinate system ; Deep learning ; Pattern recognition (psychology) ; Theoretical computer science ; Geometry ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Yongyi Wang | Jiahui Liu | Peng‐Hu Du | Zhili Sun | Qiang Sun
Source: ACS Applied Energy Materials (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

Screening Topological Quantum Cathode Materials for K-Ion Batteries by Graph Neural Network and First-Principles Calculations Among the key parts of metal-ion batteries, cathode materials significantly affect the energy density and cycling stability. However, due to the large size of K+, not much progress has been made on cathode materials for K-ion batteries (KIBs). In this study, using the Atomistic Line Graph Neural Network and first-principles calculations, for the first time we screen cathode materials for KIBs from 7385 topological quantum materials with high electronic conductivity and reversible capacity. The experimentally synthesized K2MnS2 is discovered to have a reversible capacity of 203.8 mAh/g, an energy density of 564.5 Wh/kg, a small volume change of 6.4%, and multiple channels for K+ transport with fast dynamics. Furthermore, K2MnS2 shows high electrochemical interface stability with the reported solid electrolytes of K4V2O7, and K3NbP2O9. These findings suggest that topological quantum materials expand the design space of battery cathodes. Cathode ; Materials science ; Battery (electricity) ; Ion ; Quantum ; Topology (electrical circuits) ; Electrochemistry ; Electrolyte ; Chemical physics ; Nanotechnology ; Chemistry ; Physical chemistry ; Physics ; Electrical engineering ; Electrode ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Graphene research and applications
Authors: Dawei Zhang | Songyun Ma | Bernd Markert
Source: PAMM (article, 2023-03-01)
Field: Materials Science — Metals and Alloys

Machine learning based prediction of the corrosion behaviour of biodegradable magnesium alloys with PEO coatings Abstract The present work focuses on the application of deep learning methods for predicting the in vitro pitting corrosion behaviour of coated magnesium alloys with different microstructures. The datasets are generated by phase field simulation of pitting corrosion. Performance analysis shows that physics‐informed convolutional neural network (CNN) is superior to regular CNN in predicting corrosion curve. A good agreement between deep learning predictions and the ground truths indicates excellent predictive performance of the current encoder‐decoder architecture regardless of the variation of microstructures. Furthermore, the computational time is reduced by 4 to 6 orders of magnitude compared to the phase field simulation. The computational results demonstrate that machine learning based approach is promising alternative to traditional modelling methods for predicting pitting corrosion of coated magnesium alloys. Corrosion ; Convolutional neural network ; Materials science ; Magnesium ; Pitting corrosion ; Microstructure ; Deep learning ; Artificial intelligence ; Metallurgy ; Computer science ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties
Authors: Alexander Goscinski | Victor Paul Principe | Guillaume Fraux | Sergei Kliavinek | Benjamin A. Helfrecht | Philip Loche | Michele Ceriotti | Rose K. Cersonsky
Source: Open Research Europe (article, 2023-05-16)
Field: Materials Science — Materials Chemistry

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science <ns7:p>Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domain-specific functionalities. We present the python library scikit-matter that targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows.</ns7:p> Workflow ; Computer science ; Suite ; Usability ; Python (programming language) ; Software engineering ; Interoperability ; Domain (mathematical analysis) ; Implementation ; Data science ; Database ; Programming language ; World Wide Web ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Priyanka Sinha | D. Roshini | Varad Daoo | B. Moses Abraham | Jayant K. Singh
Source: Transactions of Indian National Academy of Engineering (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Molecular Simulation for Material Design and Discovery Workflow ; Transformative learning ; Computer science ; Field (mathematics) ; Artificial intelligence ; Data science ; Set (abstract data type) ; Machine learning ; Management science ; Engineering ; Mathematics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalysis and Oxidation Reactions
Authors: Evan M. Askenazi | Emanuel A. Lazar | Ilya Grinberg
Source: Journal of Chemical Information and Modeling (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular, for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion, and perovskite formation energy, band gap, and lattice parameter as examples, we demonstrate that (1) construction of a convex hull in feature space that encloses accurately predicted systems can be used to identify regions in feature space for which ML predictions are highly reliable; (2) analysis of the systems enclosed by the convex hull can be used to extract physical understanding; and (3) materials that satisfy all well-known chemical and physical principles that make a material physically reasonable are likely to be similar and show strong relationships between the properties of interest and the standard features used in ML. We also show that similar to the composition-structure-property relationships, inclusion in the ML training data set of materials from classes with different chemical properties will not be beneficial for the accuracy of ML prediction and that reliable results likely will be obtained by ML model for narrow classes of similar materials even in the case where the ML model will show large errors on the data set consisting of several classes of materials. Convex hull ; Computer science ; Reliability (semiconductor) ; Chemical space ; Lattice (music) ; Algorithm ; Regular polygon ; Chemistry ; Mathematics ; Thermodynamics ; Physics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Fan Wang
Source: Materials Today Communications (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Neural network model for hysteretic characteristic of shape memory alloy Shape-memory alloy ; Hysteresis ; Artificial neural network ; Materials science ; Alloy ; Function (biology) ; Simple (philosophy) ; Network model ; Biological system ; Computer science ; Artificial intelligence ; Condensed matter physics ; Physics ; Metallurgy Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Piezoelectric Actuators and Control