jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shiyu Chen | Hazlee Azil Illias | Wong Jee Keen Raymond | Nurulafiqah Nadzirah Mansor
Source: Ain Shams Engineering Journal (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Denoising of partial discharges in switchgear insulation material using hybrid wavelet denoising-optimization-machine learning Partial discharge (PD) diagnosis is essential for assessing the insulation status of power equipment, but onsite interferences often contaminate PD signals with noise, impacting diagnostic accuracy. This work proposes an adaptive wavelet threshold denoising technique, where the PD signal is first decomposed into wavelet coefficients using discrete wavelet transform (DWT). Traditional threshold selection methods rely on experience and statistical factors, challenging optimal threshold determination. To address this issue, Particle Swarm Optimization (PSO), Energy Valley Optimization (EVO) and Subtraction Average Based Optimization (SABO) are applied to achieve the best adaptive threshold. The proposed method is evaluated against traditional sqtwolog-based threshold methods using root mean square error (RMSE) and the recognition accuracy of classifiers, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT) and K-Nearest Neighbours (KNN). The results show that the proposed technique can find the best threshold and increase the recognition accuracy by 19% compared to the traditional method, demonstrating its superior performance. Switchgear ; Noise reduction ; Partial discharge ; Wavelet ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Engineering ; Mechanical engineering ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image and Signal Denoising Methods
Authors: Abhinav S. Raman | Annabella Selloni
Source: Surface Science (article, 2024-08-31)
Field: Materials Science — Materials Chemistry

Long timescale molecular dynamics simulations of carboxylic acid-modified anatase TiO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e238"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> (101)-water interfaces using ab-initio deep neural network potentials Scalable Vector Graphics ; Anatase ; Molecular dynamics ; Chemistry ; Computer science ; Physics ; Materials science ; Mineralogy ; World Wide Web ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Atwakyire Moses | Peng Xie | Siyuan Wang | Ding Chen
Source: JOM (article, 2024-06-05)
Field: Materials Science — Biomaterials

Unraveling Magnesium Alloy Corrosion Patterns Through Unsupervised Machine Learning: Exploring Clustering Techniques for Enhanced Insight Cluster analysis ; Corrosion ; Computer science ; Unsupervised learning ; Machine learning ; Artificial intelligence ; Conceptual clustering ; Workflow ; Data mining ; Hierarchical clustering ; Interpretation (philosophy) ; CURE data clustering algorithm ; Correlation clustering ; Materials science ; Database Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Siyuan Zhang | Biao Zhao | Shun Zhu | Yanwei Liu
Source: Composite Structures (article, 2024-07-05)
Field: Materials Science — Biomaterials

Buckling behaviors prediction of biological staggered composites with finite element analysis and machine learning coupled method Buckling ; Materials science ; Finite element method ; Structural engineering ; Microstructure ; Artificial neural network ; Modulus ; Composite material ; Aspect ratio (aeronautics) ; Elastic modulus ; Compressive strength ; Computer science ; Engineering ; Artificial intelligence Calcium Carbonate Crystallization and Inhibition ; Material Selection and Properties ; Corrosion Behavior and Inhibition
Authors: John Broucek | Danial Khatamsaz | Cem Cakirhan | Sina Hossein Zadeh | Fan Min | Guillermo Vazquez | K.C. Atli | Xiaoning Qian | Raymundo Arróyave | İbrahim Karaman
Source: Acta Materialia (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Design of high-temperature NiTiCuHf shape memory alloys with minimum thermal hysteresis using Bayesian optimization Materials science ; Bayesian optimization ; Shape-memory alloy ; Hysteresis ; Thermal hysteresis ; Thermal ; Bayesian probability ; Thermodynamics ; Metallurgy ; Condensed matter physics ; Artificial intelligence ; Computer science ; Phase transition Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Topology Optimization in Engineering
Authors: Rahulkumar P. Jadav | Deobrat Singh | Rajeev Ahuja | Yogesh Sonvane
Source: The Journal of Physical Chemistry C (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Fluorine-Terminated MXene as an Anode Material for Dual-Ion(Ca<sup>2+</sup>/Mg<sup>2+</sup>) Batteries with Rapid Diffusion Mobility Discovering thin and high-ion transfer mobility electrode materials is necessary to boost the charge–discharge rate of rechargeable metal-ion batteries. The functionality of two-dimensional (2D) MXenes as anode materials is largely dependent on their surface terminal groups, and surface terminal techniques are frequently employed to enhance their charge–discharge performance. Here, we construct F-terminated Hf3C2, while we use the first-principles calculations to explore its potential as an anode material for rechargeable metal-ion batteries, such as Mg- and Ca-ion batteries. The metallic nature and significant structural stability of the Hf3C2F2 monolayer found by phonon and thermal properties assessed with ab-initio molecular dynamics (AIMD) and the machine learning force field (MLFF) and the low average open-circuit voltage (OCV) of 0.591 and 0.394 V and relatively low diffusion energies of Mg2+ and Ca2+ ions of 0.077 and 0.143 eV, respectively, can help improve the battery cycle. These multivalent metal cations have very low OCV values and very modest diffusion barriers, allowing the Hf3C2F2 monolayer to be a feasible anode material for rechargeable dual-ion batteries. Anode ; MXenes ; Materials science ; Battery (electricity) ; Ion ; Diffusion ; Chemical physics ; Open-circuit voltage ; Monolayer ; Electrode ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Voltage ; Physical chemistry ; Thermodynamics ; Electrical engineering MXene and MAX Phase Materials ; Advancements in Battery Materials ; Graphene research and applications
Authors: Qingyang Fan | G. Min | Li Liu | Yingbo Zhao | Xinhai Yu | Sining Yun
Source: Diamond and Related Materials (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Accelerate the design of new superhard carbon allotropes in Pca21 space group: High-throughput screening and machine learning strategies Throughput ; Computation ; Bulk modulus ; Computer science ; Support vector machine ; Shear modulus ; Extreme learning machine ; Artificial neural network ; Carbon fibers ; Materials science ; Computational science ; Artificial intelligence ; Machine learning ; Algorithm ; Composite material ; Composite number Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; X-ray Diffraction in Crystallography
Authors: Davide Tisi | Federico Grasselli | Lorenzo Gigli | Michele Ceriotti
Source: Physical Review Materials (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Thermal conductivity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Li</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>PS</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> solid electrolytes with <i>ab initio</i> accuracy The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable, due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this paper, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, ${\mathrm{r}}^{2}\text{SCAN}$, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state electrolyte, ${\mathrm{Li}}_{3}{\mathrm{PS}}_{4}$, in all its polymorphs ($\ensuremath{\alpha}, \ensuremath{\beta}$, and $\ensuremath{\gamma}$). By comparing MD estimates with lattice methods on the low-temperature, nondiffusive $\ensuremath{\gamma}\ensuremath{-}{\mathrm{Li}}_{3}{\mathrm{PS}}_{4}$, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for nondiffusive phases. Finally, for the ion-conducting $\ensuremath{\alpha}$ and $\ensuremath{\beta}$ phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases. Materials science ; Thermodynamics ; Thermal conduction ; Thermal conductivity ; Lattice (music) ; Ab initio ; Machine learning ; Algorithm ; Condensed matter physics ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Ming Sun | Caixia Fu | Haoming Su | Ruyue Xiao | Chaojie Shi | Zhiyun Lu | Xuemei Pu
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Enhancing chemistry-intuitive feature learning to improve prediction performance of optical properties Emitters have been widely applied in versatile fields, dependent on their optical properties. Thus, it is of great importance to explore a quick and accurate prediction method for optical properties. To this end, we have developed a state-of-the-art deep learning (DL) framework by enhancing chemistry-intuitive subgraph and edge learning and coupling this with prior domain knowledge for a classic message passing neural network (MPNN) which can better capture the structural features associated with the optical properties from a limited dataset. Benefiting from technical advantages, our model significantly outperforms eight competitive ML models used in five different optical datasets, achieving the highest accuracy to date in predicting four important optical properties (absorption wavelength, emission wavelength, photoluminescence quantum yield and full width at half-maximum), showcasing its robustness and generalization. More importantly, based on our predicted results, one new deep-blue light-emitting molecule PPI-2TPA was successfully synthesized and characterized, which exhibits close consistency with our predictions, clearly confirming the application potential of our model as a quick and reliable prediction tool for the optical properties of diverse emitters in practice. Feature (linguistics) ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Khurram Shahzad | Muhammad Asim Raza | Abrar Hussain | Shiv Dutt Purohit | Kyong-Cheol Ko | Sung Soo Han | Sang Hyun Park
Source: European Polymer Journal (article, 2024-10-20)
Field: Materials Science — Materials Chemistry

Integration of two-dimensional MXene into polymeric-based nanocomposite hydrogels for various biomedical applications: Recent advances and future prospects • MXene-polymer-based nanocomposite hydrogels (CHGs) are potential candidates to explore biomedical applications. • Properties and structure of CHGs are reviewed. • Biomedical applications of CHGs are discussed. • Machine learning-assisted design and applications of CHGs are also briefly overviewed. • Key challenges and future directions are addressed. MXenes (viz., transition metal carbides, carbonitrides, and nitrides) are emerging two-dimensional materials (2DMats) with remarkable properties, such as excellent biocompatibility, surface hydrophilicity, easy surface modification, electrical conductivity, and mechanical properties. Including these materials in a polymer matrix makes it possible to produce hydrogels with functional properties applicable to various biomedical areas. This review provides a brief overview of the structure and properties of MXenes, their integration into polymer matrices to formulate emerging MXene-polymer-based nanocomposite hydrogels (CHGs), their multifunctional biomedical applications, machine learning-assisted design and applications, the associated challenges, and future outlooks. Self-healing hydrogels ; Materials science ; Nanocomposite ; Nanotechnology ; Polymer chemistry MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Wenqiang Yang | Kareem Abdelfatah | Subrata Kumar Kundu | Biplab Rajbanshi | Gabriel Terejanu | Andreas Heyden
Source: ACS Catalysis (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Finally, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids. Propanoic acid ; Hydrodeoxygenation ; Density functional theory ; Catalysis ; Chemistry ; Transition state ; Adsorption ; Computational chemistry ; Selectivity ; Physical chemistry ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Shoieb Ahmed Chowdhury | M. F. N. Taufique | Jing Wang | Marissa Masden | Madison Wenzlick | Ram Devanathan | Alan Schemer-Kohrn | Keerti Kappagantula
Source: Integrating materials and manufacturing innovation (article, 2024-01-08)
Field: Materials Science — Metals and Alloys

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy Artificial intelligence ; Convolutional neural network ; Segmentation ; Grain boundary ; Materials science ; Computer science ; Pattern recognition (psychology) ; Pixel ; Deep learning ; Microstructure ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: K Chen | Amanda S. Barnard
Source: Journal of Physics Materials (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Advancing electron microscopy using deep learning Abstract Electron microscopy, a sub-field of microanalysis, is critical to many fields of research. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyze it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning (DL), a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different DL approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of DL is designed to guide electron microscopists to choose the right DL algorithm for their research and prepare for their digital future. Bottleneck ; Computer science ; Field (mathematics) ; Artificial intelligence ; Deep learning ; Data science ; Nanotechnology ; Machine learning ; Materials science ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Kihoon Bang | J. H. Kim | Doosun Hong | Dong-Hun Kim | Sang Soo Han
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Inverse design for materials discovery from the multidimensional electronic density of states To accelerate materials discovery, a deep learning method for inverse design of inorganic materials using multidimensional DOS properties was developed. Inverse ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Lei Wang | Jian Dong | Wenjie Zhang | Chong Zheng | Linhua Liu
Source: Optical Materials (article, 2024-06-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design for laser-compatible infrared camouflage metasurface enabled by physics-driven neural network and genetic algorithm Camouflage ; Infrared ; Artificial neural network ; Laser ; Inverse ; Inverse problem ; Optics ; Physics ; Computer science ; Artificial intelligence ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Thermal Radiation and Cooling Technologies
Authors: Yanbo Pei | Liting Li | Menghua Yu | En‐Bo Wei | Maosheng Zhao | Bugang Teng
Source: Journal of Magnesium and Alloys (article, 2024-08-24)
Field: Materials Science — Biomaterials

Comparison and evaluation of different constitutive models for predicting the hot deformation behavior of Mg-Gd-Y-Zr alloy The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models, machine learning models, and internal state variables (ISV) models based on physical mechanisms. Currently, there is a lack of comparison and evaluation regarding the suitability of different types of models. In this study, Mg-Gd-Y-Zr alloy is taken as the research object. The hot deformation behavior of the alloy was studied systematically. Subsequently, Arrhenius model with strain compensation, artificial neural network (ANN) model, and ISV model involving dynamic recrystallization (DRX), dislocation density and grain size evolution were established. ANN model demonstrates a higher level of accuracy in fitting the original stress-strain curves compared to both ISV model and modified Arrhenius model, but ANN model is not suitable for predicting the experimental results outside of the initial database. ISV model considers the impact of microstructure evolution history on stress, making it highly effective in reflecting the mechanical responses under complex loading condition. The established ISV model is embedded in the ABAQUS software, which shows good ability in calculating the mechanical response, dimension, and microstructure evolution information of the component during hot forming. Materials science ; Constitutive equation ; Deformation (meteorology) ; Alloy ; Metallurgy ; Composite material ; Structural engineering ; Finite element method ; Engineering Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Tianze Zheng | Ailun Wang | Xu Han | Yu Xia | Xingyuan Xu | Jiawei Zhan | Yu Liu | Yang Chen | Zhi Wang | Xiaojie Wu | Sheng Gong | Wen Yan
Source: Chemical Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery. Expansive ; Parametrization (atmospheric modeling) ; Molecular mechanics ; Space (punctuation) ; Force field (fiction) ; Chemical space ; Classical mechanics ; Statistical physics ; Physics ; Mechanics ; Molecular dynamics ; Computer science ; Chemistry ; Quantum mechanics ; Thermodynamics ; Drug discovery Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods