jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunqi Xing | Jiahao Liu | Jingang Su | Jun‐Wei Zha | Guochang Li | Ze Guo | Xiaozhen Zhao | Mengjia Feng
Source: High Voltage (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Effect of nucleating agents on the electrical properties of cross‐linked polyethylene under tensile stress Abstract During the operation of high‐voltage cables, external stress and residual stress can affect the aggregated structure of insulating materials and lead to significant deterioration in their electrical performance. To investigate the evolution characteristics of the electrical properties of cross‐linked polyethylene (XLPE) under mechanical stress, this paper explains the relationship between the aggregated structure of XLPE and its electrical properties and proposes a method for improving insulation performance under mechanical stress. The results show that metallocene polyethylene used as a nucleating agent can promote crystallisation through heterogeneous nucleation and increase Young's modulus by non‐uniform nucleation, increasing crystallinity and reducing interplanar spacing, resulting in more complete crystal forms and reduced damage to the aggregated structure during the tensile process. After nucleating agent modification, the XLPE crystallisation becomes more uniform, and interfacial adhesion forces increase. The weakened interface damage process between the amorphous and crystalline regions under tensile stress effectively inhibits the process of molecular chain polarisation turning and reduces trap density. The modified XLPE crystal structure shows a tendency towards densification and enhanced molecular chain interactions, which can reduce the damage to the aggregated structure under tensile stress, while the reduced free volume inside the material and the shortened average free path of carriers can weaken the damage of high‐energy electrons to molecular chains, thereby inhibiting the process of electrical tree degradation. The results show that nucleating agents have great potential for maintaining the stable operation of XLPE cables under mechanical stress. Materials science ; Composite material ; Nucleation ; Ultimate tensile strength ; Polyethylene ; Crystallinity ; Crystallization ; Stress (linguistics) ; Crystal (programming language) ; Crystallization of polymers ; Chemical engineering ; Chemistry High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Electrical Fault Detection and Protection
Authors: Alessandra Toniato | Jan P. Unsleber | Alain C. Vaucher | Thomas Weymuth | Daniel Probst | Teodoro Laino | Markus Reiher
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning Data-driven synthesis planning has seen remarkable successes in recent years by virtue of modern approaches of artificial intelligence that efficiently exploit vast databases with experimental data on chemical reactions. However, this success story is intimately connected to the availability of existing experimental data. It may well occur in retrosynthetic and synthesis design tasks that predictions in individual steps of a reaction cascade are affected by large uncertainties. In such cases, it will, in general, not be easily possible to provide missing data from autonomously conducted experiments on demand. However, first-principles calculations can, in principle, provide missing data to enhance the confidence of an individual prediction or for model retraining. Here, we demonstrate the feasibility of such an ansatz and examine resource requirements for conducting autonomous first-principles calculations on demand. Retrosynthetic analysis ; Reliability (semiconductor) ; Computer science ; Quantum chemical ; Quantum ; Reliability engineering ; Artificial intelligence ; Machine learning ; Engineering ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Thiago F. Santos | Caroliny M. Santos | Marcos Aquino | Sanjay Mavinkere Rangappa | Suchart Siengchin | José Heriberto Oliveira do Nascimento | Ivan Medeiros
Source: Heliyon (article, 2023-03-30)
Field: Materials Science — Polymers and Plastics

Effects of UV sensitivity and accelerated photo-aging on stab resistance of ρ-aramid fabrics impregnated with shear thickening fluids (STFs) The use of Kevlar in the field of ballistic and stabbing protection has been studied by researchers in polymeric composites for this purpose. This study presents complementary knowledge on energy absorption and dissipation in ρ-aramid fabric impregnated with shear thickening fluids (STFs), especially aiming to obtain better protection against impacts that are deeply associated with STFs, as well as color change, accelerated aging (QUV), and penetration depth (drop tower test). In addition, Scanning Electron Microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) was performed. The research shows that there was a good distribution of STF particles on the ρ-Aramid fabric surface, promoting increased friction between the interfilament and the yarns, further increasing performance and, consequently, improving the energy absorption and dissipation mechanism and, also, the penetration effectiveness in relation to non-impregnated ρ-Aramid fabric. Regarding the protection efficiency against UV exposure (250-400 nm region), there was a significantly decreased compared to those non-impregnated Kevlar® woven with STFs. The FTIR analysis showed that the conditions of aging, after exposure to UV, did not produce new functional groups, that is, there was no chemical modification. Finally, Kevlar fabric impregnated with STFs improved penetration depth performance with the blades independent of the blade type with up to 81% increase in resistance. This result was improved due to interactions between the nanoparticles present in STFs, yarns, and even high-performance woven impregnated with shear-thickening fluids. Materials science ; Composite material ; Aramid ; Penetration (warfare) ; Kevlar ; Dilatant ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Epoxy ; Chemical engineering Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Textile materials and evaluations
Authors: Nicholas Casetti | Javier Emilio Alfonso Ramos | Connor W. Coley | Thijs Stuyver
Source: Chemistry - A European Journal (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules; in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field. Workflow ; Molecular machine ; Computer science ; Quantum ; Throughput ; Field (mathematics) ; Quantum chemical ; High-throughput screening ; Nanotechnology ; Quantum machine learning ; In silico ; Systems engineering ; Artificial intelligence ; Quantum computer ; Molecule ; Chemistry ; Materials science ; Physics ; Engineering ; Telecommunications ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: J. Maniraj | A. Felix Sahayaraj | C. Ram Kumar | D. Ashok Kumar | Jenish Iyyadurai | Indran Suyambulingam | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: E3S Web of Conferences (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review Polymer composites are employed in a variety of applications due to their distinctive characteristics. Nevertheless, designing and optimizing these materials can be a lengthy and resourceintensive process for low cost and sustainable materials. Machine learning has the potential to simplify this process by offering predictions of the characteristics of novel composite materials based on their microstructures. This review outlines machine learning techniques and highlights the potential of machine learning to improve the design and optimization of polymer composites. This review also examines the difficulties and restrictions of utilizing machine learning in this context and offers insights into potential future research paths in this field. Context (archaeology) ; Process (computing) ; Composite number ; Variety (cybernetics) ; Computer science ; Machine design ; Polymer ; Field (mathematics) ; Materials science ; Mechanical engineering ; Machine learning ; Artificial intelligence ; Process engineering ; Manufacturing engineering ; Composite material ; Engineering ; Mathematics Machine Learning in Materials Science ; Polymer Nanocomposites and Properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Jie Liu | Tao Zhang | Shuyu Sun
Source: Geoscience Frontiers (article, 2023-10-29)
Field: Materials Science — Materials Chemistry

Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as the chemical interaction between injected gas and mineral, and the formation of hydrate. Emerging advances in DL technology enable solving molecular dynamics (MD) with quantum accuracy. The conventional quantum chemical method is computational expensive, whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters. With the help of the DL force field, precision at the quantum chemistry level can be achieved in MD. Moreover, the DL force field promotes the computational speed compared with first-principles calculations. In this review, the basic knowledge of the molecular force field and deep neural network (DNN) is first introduced. Then, three representative open-source packages relevant to the DL force field are introduced. As the most common components in the development of oil and gas reservoirs, water and methane are studied from the aspects of computational efficiency and chemical reaction respectively, providing the foundation of oil and gas researches. However, in the oil and gas problems, the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD. Regarding the computational efficiency, it needs improvement via GPU and parallel accelerations to compete with classical MD. Even with such difficulties, the booming of this technique in the area of petroleum engineering can be predictable. Microscale chemistry ; Molecular dynamics ; Computer science ; Force field (fiction) ; Biochemical engineering ; Field (mathematics) ; Fossil fuel ; Process (computing) ; Quantum ; Hydrate ; Nanotechnology ; Algorithm ; Computational science ; Process engineering ; Chemistry ; Computational chemistry ; Artificial intelligence ; Materials science ; Physics ; Mathematics ; Engineering Machine Learning in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Kryštof Březina | Hubert Beck | Ondřej Maršálek
Source: Journal of Chemical Theory and Computation (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems Although machine learning potentials have recently had a substantial impact on molecular simulations, the construction of a robust training set can still become a limiting factor, especially due to the requirement of a reference ab initio simulation that covers all the relevant geometries of the system. Recognizing that this can be prohibitive for certain systems, we develop the method of transition tube sampling that mitigates the computational cost of training set and model generation. In this approach, we generate classical or quantum thermal geometries around a transition path describing a conformational change or a chemical reaction using only a sparse set of local normal mode expansions along this path and select from these geometries by an active learning protocol. This yields a training set with geometries that characterize the whole transition without the need for a costly reference trajectory. The performance of the method is evaluated on different molecular systems with the complexity of the potential energy landscape increasing from a single minimum to a double proton-transfer reaction with high barriers. Our results show that the method leads to training sets that give rise to models applicable in classical and path integral simulations alike that are on par with those based directly on ab initio calculations while providing the computational speedup we have come to expect from machine learning potentials. Computer science ; Path (computing) ; Set (abstract data type) ; Artificial neural network ; Ab initio ; Speedup ; Molecular dynamics ; Quantum ; Trajectory ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Pingluo Zhao | Yangwei Wang | Bingyue Jiang | Hongmei Zhang | Xingwang Cheng | Qunbo Fan
Source: Materials Science and Engineering A (article, 2023-06-03)
Field: Materials Science — Materials Chemistry

Neural network modeling of titanium alloy composition-microstructure-property relationships based on multimodal data Microstructure ; Workflow ; Artificial neural network ; Titanium alloy ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Alloy ; Metallurgy ; Database Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alexey S. Galushko | Valentine P. Ananikov
Source: ACS Catalysis (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

4D Catalysis Concept Enabled by Multilevel Data Collection and Machine Learning Analysis ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXT4D Catalysis Concept Enabled by Multilevel Data Collection and Machine Learning AnalysisAlexey S. GalushkoAlexey S. GalushkoZelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, RussiaMore by Alexey S. Galushkohttps://orcid.org/0000-0002-4372-213X and Valentine P. Ananikov*Valentine P. AnanikovZelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia*Email: [email protected]. Website: https://AnanikovLab.ruMore by Valentine P. Ananikovhttps://orcid.org/0000-0002-6447-557XCite this: ACS Catal. 2024, 14, 1, 161–175Publication Date (Web):December 12, 2023Publication History Received17 August 2023Accepted13 November 2023Revised7 November 2023Published online12 December 2023Published inissue 5 January 2024https://pubs.acs.org/doi/10.1021/acscatal.3c03889https://doi.org/10.1021/acscatal.3c03889article-commentaryACS PublicationsCopyright © 2023 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views2007Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (3 MB) Get e-AlertscloseSUBJECTS:Catalysis,Catalysts,Metal nanoparticles,Nanoparticles,Platinum Get e-Alerts Catalysis ; Data collection ; Computer science ; Chemistry ; Organic chemistry ; Sociology Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Fengru Zheng | Jiayi Lu | Zhiwen Zhu | Hao Jiang | Yuyi Yan | Yu He | Shaoxuan Yuan | Qiang Sun
Source: ACS Nano (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules. Generalization ; Molecule ; Supramolecular chemistry ; Artificial neural network ; Computer science ; Nanostructure ; Materials science ; Scanning tunneling microscope ; Molecular graph ; Self-assembly ; Molecular dynamics ; Graph ; Training set ; Nanotechnology ; Biological system ; Artificial intelligence ; Computational chemistry ; Chemistry ; Theoretical computer science ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Joy Datta | Dibakar Datta | Vidushi Sharma
Source: Journal of Materials Science (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Transferable and robust machine learning model for predicting stability of Si anodes for multivalent cation batteries Hyperparameter optimization ; Materials science ; Hyperparameter ; Artificial intelligence ; Machine learning ; Support vector machine ; Computer science ; Stability (learning theory) ; Bayesian optimization ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhizhong Si | Daming Zhou | Jianlei Yang | Xiaoyang Lin
Source: Applied Physics A (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

Review: 2D material property characterizations by machine-learning-assisted microscopies Characterization (materials science) ; Computer science ; Artificial intelligence ; Property (philosophy) ; Automation ; Field (mathematics) ; Nanotechnology ; Scale (ratio) ; Data science ; Machine learning ; Engineering ; Materials science ; Mechanical engineering ; Epistemology ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Nanopore and Nanochannel Transport Studies
Authors: Moritz Thürlemann | Sereina Riniker
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems Hybrid machine-learning force fields combine the strengths of machine learning potentials and classical force fields enabling accurate descriptions of molecular condensed-phase systems. Force field (fiction) ; Phase (matter) ; Computer science ; Field (mathematics) ; Artificial intelligence ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Filip Dinic | Ihor Neporozhnii | Oleksandr Voznyy
Source: Computational Materials Science (article, 2023-11-04)
Field: Materials Science — Materials Chemistry

Machine learning models for the discovery of direct band gap materials for light emission and photovoltaics Band gap ; Photovoltaics ; Direct and indirect band gaps ; Computer science ; Wide-bandgap semiconductor ; Materials science ; Photovoltaic system ; Computational physics ; Optoelectronics ; Physics ; Engineering ; Electrical engineering Machine Learning in Materials Science ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Volodymyr Gvozdetskyi | Balaranjan Selvaratnam | Anton O. Oliynyk | Arthur Mar
Source: Chemistry of Materials (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Revealing Hidden Patterns through Chemical Intuition and Interpretable Machine Learning: A Case Study of Binary Rare-Earth Intermetallics <i>RX</i> Machine learning algorithms have been applied successfully in many areas of materials chemistry but often suffer from an inability to extract chemical insight. To demonstrate that an approach combining machine learning and chemical intuition can be effective in generating interpretable models, the structures of binary equiatomic rare-earth intermetallics RX, whose relationships have long defied understanding, were investigated as a case study. A structure map was developed based on only two parameters, which are derived from simple elemental descriptors (atomic number, period and group numbers, radii, and electronegativity) of the R and X components. This map reveals the previously hidden patterns of structural regularities of RX intermetallics. It explains the preference for CsCl-, TlI-, or FeB-type structures among these compounds, predicts the structures of missing members, rationalizes the occurrence of metastable phases and polymorphic transformations, and offers strategies for structure stabilization through the addition of a third component. Electronegativity ; Intermetallic ; Binary number ; Metastability ; Intuition ; Rare earth ; Artificial intelligence ; Atomic radius ; Materials science ; Machine learning ; Computer science ; Chemistry ; Mathematics ; Metallurgy ; Arithmetic ; Organic chemistry ; Cognitive science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials