jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pedram Mirchi | Christophe Adessi | Samy Mérabia | Ali Rajabpour
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN<sub>2</sub> explored by a deep-learning interatomic potential Penta-NiN<sub>2</sub>, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from <i>ab initio</i> molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The DLP's accuracy is verified, showing strong agreement with AIMD results. The dependence of thermal conductivity on size, temperature, and tensile strain, reveals important insights into the material's thermal properties. Additionally, the mechanical response of penta-NiN<sub>2</sub> under uniaxial loading is examined, yielding a Young's modulus of approximately 368 GPa. The influence of vacancy defects on mechanical properties is analyzed, demonstrating a significant reduction in modulus, fracture stress, and ultimate strength. This study also investigates the influence of strain on phonon dispersion relations and phonon group velocity in penta-NiN<sub>2</sub>, shedding light on how alterations in the atomic lattice affect the phonon dynamics and, consequently, impact the thermal conductivity. This investigation showcases the ability of deep learning-based interatomic potentials in studying the properties of 2D penta-NiN<sub>2</sub>. Interatomic potential ; Thermal conductivity ; Lattice (music) ; Materials science ; Condensed matter physics ; Layer (electronics) ; Thermal ; Thermodynamics ; Chemistry ; Nanotechnology ; Physics ; Computational chemistry ; Molecular dynamics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Changhang Huang | Kechun Bai | Yanyan Zhu | David Andelman | Xingkun Man
Source: Advanced Functional Materials (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Design and Fabrication of Nano‐Particles with Customized Properties using Self‐Assembly of Block‐Copolymers Abstract Functional nanoparticles (NPs) have gained significant attention as promising applications in various fields, including sensor, smart coating, drug delivery, and more. Here, a novel mechanism assisted by machine‐learning workflow is proposed to accurately predict phase diagram of NPs, which elegantly achieves tunability of shapes and internal structures of NPs using self‐assembly of block‐copolymers (BCP). Unlike most of previous studies, onion‐like and mesoporous NPs in neutral environment and hamburger‐like NPs in selective environment are obtained. Such novel phenomena are obtained only by tailoring the topology of a miktoarm star BCP chain architecture without the need for any further treatment. Moreover, it is demonstrated that the BCP chain architecture can be used as a new strategy for tuning the lamellar asymmetry of NPs. It is shown that the asymmetry between A and B lamellae in striped ellipsoidal and onion‐like particles increases as the volume fraction of the A‐block increases, beyond the level reached by linear BCPs. In addition, an extended region of onion‐like structure is found in the phase diagram of A‐selective environment, as well as the emergence of an inverse onion‐like structure in the B‐selective one. The findings provide a valuable insight into the design and fabrication of nanoscale materials with customized properties, opening up new possibilities for advanced applications in sensing, materials science, and beyond. Materials science ; Copolymer ; Fabrication ; Self-assembly ; Nanotechnology ; Nano- ; Block (permutation group theory) ; Composite material ; Polymer ; Geometry Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Nanomaterials for catalytic reactions
Authors: Andrew J. P. White | Marco Gibaldi | Jake Burner | Rebeca Mayo | Tom K. Woo
Source: ChemRxiv (preprint, 2024-10-10)
Field: Materials Science — Materials Chemistry

Alarming structural error rates in MOF databases used in data driven workflows identified via a novel metal oxidation state-based method Metal-organic frameworks (MOFs) are a diverse class of porous materials composed of inorganic nodes joined by organic linkers, currently under investigation for a wide range of applications including gas storage and separation where they have been commercialized. Given the labor-intensive nature of synthesizing and testing individual MOFs, high-throughput computational screening and machine learning (ML) methods are increasingly viewed as essential for facilitating MOF development. However, the structural fidelity of the “computation-ready” MOF databases used in such studies remains largely unquantified. We introduce MOSAEC, an algorithm that detects chemically invalid structures on the basis of metal oxidation states. MOSAEC was manually validated against ~16k MOF structures from the popular CoRE database, and was found to flag erroneous structures with 95% accuracy. Systematic examination of 14 leading experimental and hypothetical MOF databases containing >1.9 million MOFs reveals concerning structural error rates, exceeding 40% in most cases. Workflow ; Database ; Computer science ; Oxidation state ; State (computer science) ; Data mining ; Metal ; Materials science ; Algorithm ; Metallurgy Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Neural Networks and Applications
Authors: Mohd Zaki | Jayadeva | N. M. Anoop Krishnan
Source: Frontiers in Materials (article, 2024-09-18)
Field: Materials Science — Ceramics and Composites

Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glasses Artificial intelligence (AI) and machine learning (ML) have enabled property-targeted design of glasses. Several machine learning models and open-source tools in the literature allow researchers to predict the optical, physical, mechanical, and electrical properties of glasses as a function of their chemical compositions. However, these properties also depend on testing conditions. In this paper, we train machine learning models by considering composition and wavelength, temperature, and frequency to predict the refractive index, density, and the two electrical properties, i.e., dielectric constant and loss tangent of glasses, respectively. The predictions of trained models are explained using SHAP analysis, revealing that testing conditions, such as wavelength and temperature, interact majorly with network formers while predicting refractive index and density. In the case of electrical properties, network formers and frequency have the highest interactions, followed by network modifiers and intermediates, and hence govern predictions of dielectric constant and loss tangent. Overall, AI/ML models that can predict the properties of glasses as a function of their composition and testing conditions, coupled with SHAP plots, provide a practical tool to develop a range of glasses for application under varying conditions. Dielectric ; Dissipation factor ; Refractive index ; Tangent ; Materials science ; Constant (computer programming) ; Wavelength ; Function (biology) ; Machine learning ; Mineralogy ; Optics ; Computer science ; Mathematics ; Chemistry ; Physics ; Optoelectronics ; Geometry Glass properties and applications ; Pigment Synthesis and Properties ; Cultural Heritage Materials Analysis
Authors: Jiali Chen | Zixuan Wang | Jiang‐Jiang Ma | Zhongyin Cao | Kexun Li | Junfeng Zhang
Source: Carbon (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Thermal and electronic properties of borophene in two-dimensional lateral graphene-borophene heterostructures empowered by machine-learning approach Borophene ; Graphene ; Materials science ; Heterojunction ; Thermal conductivity ; Chemical physics ; Nanotechnology ; Thermal ; Lattice (music) ; Density functional theory ; Condensed matter physics ; Optoelectronics ; Chemistry ; Computational chemistry ; Physics ; Composite material ; Thermodynamics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Jianwei Dai | Juyi Yang | Xiangang Zhang | Lu Zhang | Biwei Sun | Xuan Li | Jing Bai | Feng Xue | Chenglin Chu
Source: Corrosion Science (article, 2024-07-24)
Field: Materials Science — Biomaterials

Synergistic effects of BSA adsorption and shear stress on corrosion behaviors of WE43 alloy under simulated physiological flow field Corrosion ; Materials science ; Alloy ; Adsorption ; Shear stress ; Metallurgy ; Composite material ; Chemistry ; Physical chemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Ahmed Nuri Kurşunlu | Yaser Açıkbaş | Ceren Yilmaz | Mustafa Özmen | İnci Çapan | Rıfat Çapan | Kemal Büyükkabasakal | Ahmet Şenocak
Source: ACS Applied Materials & Interfaces (article, 2024-06-05)
Field: Chemistry — Spectroscopy

Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named <b>P[5]-1</b> and <b>P[5]-2</b>, were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories. The thin films of the synthesized macrocycles were coated by using the spin coating technique on a suitable substrate under optimum conditions. All compounds and the prepared thin film surfaces were characterized by NMR, Fourier transform infrared (FT-IR), elemental analysis, atomic force microscopy (AFM), scanning electron microscopy (SEM), and contact angle measurements. All vapor sensing measurements were performed via the surface plasmon resonance (SPR) optical technique, and the responses of the <b>P[5]-1</b> and <b>P[5]-2</b> thin-film sensors were calculated with Δ<i>I</i>/<i>I</i><sub>o</sub> × 100. The responses of the <b>P[5]-1</b> and <b>P[5]-2</b> thin-film sensors to dichloromethane vapor were determined to be 7.17 and 4.11, respectively, while the responses to chloroform vapor were calculated to be 5.24 and 2.8, respectively. As a result, these thin-film sensors showed a higher response to dichloromethane and chloroform vapors than to other harmful vapors. The SPR kinetic data for vapors validated that a nonlinear autoregressive neural network was performed with exogenous input for the best molecular modeling by using normalized reflected light intensity values. It can be clearly seen from the correlation coefficient values that the nonlinear autoregressive with exogenous input artificial neural network (NARX-ANN) model for dichloromethane converged more successfully to the experimental data compared to other gases. The correlation coefficient values of the dichloromethane modeling results were approximately 0.99 and 0.98 for <b>P[5]-1</b> and <b>P[5]-2</b> thin-film sensors, respectively. Thin film ; Materials science ; Dichloromethane ; Chloroform ; Spin coating ; Fourier transform infrared spectroscopy ; Chemical engineering ; Surface plasmon resonance ; Analytical Chemistry (journal) ; Nanoparticle ; Nanotechnology ; Organic chemistry ; Chemistry Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials
Authors: Songhao Wang | Zhenghua Qian | Yan Shang
Source: Case Studies in Thermal Engineering (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Size-dependent vibration analysis of porous 3D-FG microshells in complex thermal environments using a neural network enhanced finite element model This work aims to investigate the vibration behavior of porous three-directional functionally graded (3D-FG) microshells in thermal environments, where both mechanical and thermal material properties vary along three spatial directions following a power-law distribution, based on the modified couple stress theory (MCST). An isoparametric thermal element is employed for uncoupled heat conduction analysis, followed by dynamic analysis using a penalty solid element with three translation degrees of freedom (DOFs) and three rotation DOFs per node. However, due to the strong nonlinearity of the temperature field in 3D-FG microshells, obtaining exact temperature values at integration points in dynamic analysis requires a pretty dense mesh of the solid element. To overcome this issue, a back propagation (BP) neural network is utilized to predict the temperature field for various gradient indexes, enhancing the efficiency and precision of temperature distribution calculation. Numerical results demonstrate the effectiveness of the neural network enhanced finite element model. Furthermore, the effects of the material length scale parameter (MLSP), temperature difference, and gradient indexes on the natural frequencies are investigated. It is shown that higher structural stiffness weakens the impact of temperature difference on the frequency. Finite element method ; Vibration ; Porosity ; Artificial neural network ; Thermal ; Materials science ; Mechanics ; Porous medium ; Acoustics ; Biological system ; Computer science ; Structural engineering ; Physics ; Composite material ; Thermodynamics ; Artificial intelligence ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Jiménez-Rodríguez Jacobo | Oscar Olea-Mejía | Ana Laura Martínez‐Hernández | Velasco-Santos Carlos
Source: FlatChem (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Optimization of the optical response of 2D MoS2 materials obtained through liquid-phase exfoliation using a comprehensive multi-objective approach Materials science ; Exfoliation joint ; Raman spectroscopy ; Molybdenum disulfide ; Absorbance ; Band gap ; Scanning electron microscope ; Graphene ; Spectroscopy ; Optoelectronics ; Analytical Chemistry (journal) ; Nanotechnology ; Optics ; Composite material 2D Materials and Applications ; Perovskite Materials and Applications ; Graphene research and applications
Authors: Tripti Joshi | Hansa Sehgal | Sonakshi Puri | Karnika | Tanmaya Mahapatra | Mukul Joshi | P. R. Deepa | Pankaj Kumar Sharma
Source: Journal of Agriculture and Food Research (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

ML-based technologies in sustainable agro-food production and beyond: Tapping the (semi) arid landscape for bioactives-based product development The current era of rapid climate change necessitates greater emphasis on wild, often underutilized yet sturdy, edible plants that are capable of growing in harsh arid lands. When compared to more popular crops like rice, these are often of traditional significance and more region-specific; but needing less chemical fertilizers, pesticides and irrigation water, they can not only provide food and nutrition in a sustainable manner but also medicinally valuable compounds (nutraceuticals) to target various communicable and non-communicable diseases. These bioactive metabolites could also serve as markers for in-process quality control of herbal formulations and as metabolic biomarkers. Of late, a few of the common food crops across the world have benefited from the use of technological interventions, employing various Internet of Things (IoT) devices and sensors to collect data on the farm and conduct agro-food specific analytics. Machine Learning (ML) and deep learning (DL) have found application in numerous facets of agriculture, particularly in tasks such as yield prediction, disease detection, weed detection, crop recognition, and assessing crop quality at pre-harvest, harvest, and post-harvest stages. ML technology also has shown potential to be effectively employed at various stages of bioactives discovery, encompassing target identification, compound screening, lead discovery, as well as pre-clinical and clinical development phases. However, the usage of these modern technologies has been less explored in the desert plants of the world. The current article reviews a few available examples and highlights the potential of employing ML and DL technologies in edible plants of the world, with a focus on sustainable desert flora, for achievement of multidisciplinary objectives, that is, agro-food production, food safety and bioactives discovery. Biotechnology ; Nutraceutical ; Agriculture ; Agricultural engineering ; Business ; Agroforestry ; Biology ; Engineering ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Ameera Tag | Shady S. Refaat | Sayed Mohammad Kameli | Mohammad AlShaikh Saleh
Source:  (review, 2024-01-08)
Field: Materials Science — Materials Chemistry

Machine Learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review The power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. The detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. Therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis. Partial discharge ; Transformer ; Computer science ; Machine learning ; Artificial intelligence ; Electrical engineering ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Junwei Che | Wenjie Huang | Guoliang Ren | Jiajun Linghu | Xue-Zhi Wang
Source: Ceramics International (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Dual-channel phonon transport leads to low thermal conductivity in pyrochlore La2Hf2O7 Phonon ; Anharmonicity ; Thermal conductivity ; Condensed matter physics ; Materials science ; Pyrochlore ; Boltzmann equation ; Thermal conduction ; Thermal ; Physics ; Thermodynamics ; Phase (matter) ; Quantum mechanics Nuclear materials and radiation effects ; Thermal properties of materials ; High-pressure geophysics and materials
Authors: Sang‐Hyun Kim | Joo‐Hyoung Lee
Source: Materials Today Advances (article, 2024-06-26)
Field: Chemistry — Inorganic Chemistry

Data-driven discovery of novel metal organic frameworks with superior ammonia adsorption capacity Ammonia (NH 3 ) has been a subject of great interest due to its important roles in diverse technological applications. However, high toxicity and corrosiveness of NH 3 has made it an important task to develop an efficient carrier to safely capture NH 3 with high capacity. Here, we employ a machine learning (ML) model to discover high-performance metal organic frameworks (MOFs) that will work as efficient NH 3 carriers. By constructing databases at two distinct conditions, adsorption and desorption, through Grand Canonical Monte Carlo (GCMC) simulations to train ML models, we identify eight novel MOFs as potentially efficient NH 3 carriers through screening the large-scale MOF databases with the trained models and GCMC verification. The identified MOFs exhibit the average NH 3 working capacity exceeding 1100 mg/g, and subsequent molecular dynamics simulations demonstrate mechanical stability of the predicted MOFs. Moreover, analyses of the diffusion mechanism within the proposed MOFs underscore the strong dependence of NH₃ gas diffusivity on the structural details of the materials. Metal-organic framework ; Adsorption ; Ammonia ; Environmental science ; Environmental chemistry ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Weikang Dong | Yi‐Chi Wang | Chen Yang | Chunhao Sun | Hesong Li | Ze Hua | Ziqi Wu | Xiaoxue Chang | Lixia Bao | Shuangquan Qu | Xintao Zuo | Wen Yang | Jing Lü | Ying Fu | Jiafang Li | Lixin Dong | Ruiwen Shao
Source: Nano Letters (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Deep Learning Enhanced in Situ Atomic Imaging of Ion Migration at Crystalline–Amorphous Interfaces Improving the performance of energy storage, neuromorphic computing, and more applications requires an in-depth understanding of ion transport at interfaces, which are often hindered by facile atomic reconfiguration at working conditions and limited characterization capability. Here, we construct an in situ double-tilt electric manipulator inside an aberration-corrected scanning transmission electron microscope. Coupled with deep learning-based image enhancement, atomic images are enhanced 3-fold compared to traditional methods to observe the potassium ion migration and microstructure evolution at the crystalline-amorphous interface in antimony selenide. Potassium ions form stable anisotropic insertion sites outside the (Sb<sub>4</sub>Se<sub>6</sub>) chain, with a few potassium ions present within the moieties. Combined experiments and density functional theory calculations reveal a reaction pathway of forming a novel metastable state during potassium ion insertion, followed by recovery and unexpected chirality changes at the interface upon potassium ion extraction. Our unique methodology paves the way for facilitating the improvement and rational design of nanostructured materials. In situ ; Amorphous solid ; Ion ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Crystallography ; Engineering Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis ; Nuclear Physics and Applications