jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Raashiq Ishraaq | Tanmay Sarkar Akash | Siddhartha Das
Source: Macromolecules (article, 2024-05-22)
Field: Materials Science — Surfaces, Coatings and Films

Combined Machine Learning and Molecular Dynamics Reveal Two States of Hydration of a Single Functional Group of Cationic Polymeric Brushes The state of hydration of a macromolecular system regulates a plethora of different properties of such a system. In this article, we develop a novel machine learning (ML) approach, based on the unsupervised clustering algorithm, for probing the hydration behavior of the {N(CH3)3}+ functional group of the PMETAC [poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] polyelectrolyte (PE) brush system. The PE brushes and brush-supported water molecules and counterions (chloride ions) are first described using all-atom molecular dynamics (MD) simulations. The simulation data is subsequently used in our ML framework to identify that (1) the {N(CH3)3}+ functional groups of the PMETAC brushes have two distinct hydration states, with one state (state 1) being characterized by less structured water molecules and the other state (state 2) being characterized by more structured water molecules and (2) an enhancement in the brush grafting density leads to the progressive dissappearance of state 2. An increase in the grafting density increases the number of chloride counterions in a given volume around the {N(CH3)3}+ functional group and increases the number of shared water molecules between {N(CH3)3}+ and Cl–. The chloride counterions are associated with a hydration layer with much less structured water molecules. Therefore, with an increase in the grafting density, an increase in the percentage of shared water molecules leads to the prevalence of the hydration state [of the {N(CH3)3}+ moiety] with less structured water molecules. Finally, we explain how the present findings are commensurate with two key previous related results, namely, a significantly large chloride ion mobility inside the PMETAC brush layer and the {N(CH3)3}+–Cl– average distance remaining independent of the PMETAC brush grafting density. We anticipate that the combined ML–MD-simulation approach proposed in this study can be adapted to probe other soft matter systems to reveal new insights into the underlying mechanisms of emergent phenomenon. Cationic polymerization ; Group (periodic table) ; Chemistry ; Polymer chemistry ; Molecular dynamics ; Functional group ; Polymer ; Polymer science ; Computational chemistry ; Organic chemistry Polymer Surface Interaction Studies ; Lipid Membrane Structure and Behavior ; Microfluidic and Capillary Electrophoresis Applications
Authors: Tao Wang | Xiaolong Lai | Yadong Wei | Hong Guo | Hao Jin
Source: Science China Materials (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Enhancing interpretability in the exploration of high-energy conversion efficiency in CsSnBr3−xIx configurations using crystal graph convolutional neural networks and adversarial example methods Interpretability ; Artificial intelligence ; Classifier (UML) ; Computer science ; Machine learning ; Convolutional neural network ; Feature engineering ; Adversarial system ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Yibo Jin | Jun Wang | Ruihao Yuan | Hongchao Li | Wei Tong | Chao Li | Jinshan Li
Source: Journal of Alloys and Compounds (article, 2024-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-aided design of LaCe(Fe,Mn,Si)13H-type magnetocaloric materials for room-temperature applications La(Fe,Si)13 magnetocaloric materials have attracted extensive attention in recent years due to their excellent magnetocaloric properties and low price. In this study, we established and compared several machine models and used a support vector machine for predicting the influences of different element contents on the Curie temperature. Based on the results predicted by the machine learning model, a series of fully hydrogenated materials (La1.1-xCexFe13-y-zMnySizHF, y≈0.3–0.5x, z=1.1, 1.3, 1.5) with Curie temperatures between 290 and 310 K were designed. After material screening, a material with an element composition of La0.66Ce0.44Fe11.4Mn0.1Si1.5HF was proven to have good room temperature magnetocaloric properties, with a Curie temperature of 301 K, magnetic entropy change of 11.3 J/kgK under a 0–2 T magnetic field, and a relative cooling power of 119.3 J/kg, which performs well in the existing La(Fe,Si)13-type materials that can be applied near room temperature. This work not only deepens researchers' understanding of machine learning-aided design of materials with specific application environment restrictions but also greatly accelerates the design of near-room temperature La(Fe,Si)13 magnetocaloric materials. Magnetic refrigeration ; Curie temperature ; Materials science ; Curie ; Condensed matter physics ; Thermodynamics ; Machine learning ; Magnetic field ; Mechanical engineering ; Ferromagnetism ; Computer science ; Magnetization ; Physics ; Engineering Magnetic and transport properties of perovskites and related materials ; Thermal Expansion and Ionic Conductivity ; Heusler alloys: electronic and magnetic properties
Authors: Dixant Bikal Sapkota | Puskar Neupane | Mecon Joshi | Shahabuddin Khan
Source: IET Power Electronics (article, 2024-05-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials Abstract This paper outlines the methodology for predicting power loss in magnetic materials. It starts by introducing the concept of core loss and the complexity of modelling it. Steinmetz's equation is presented to calculate power loss based on frequency and magnetic flux density, but its limitations are highlighted. As an alternative, a neural network‐based method is introduced. The proposed methodology adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density, frequency, temperature, and wave classification. Fast Fourier transform was implemented to reduce the data points of the sampled flux density waveform while preserving its characteristics. Analyzing in the frequency domain enabled streamlining the training of the model. The input features were arranged as required, and the network architecture was designed with appropriate layers and optimal activation functions. Through extensive training using the datasets, the model assimilated intricate relationships between input variables and known power loss. Evaluation and validation metrics were subsequently employed to gauge the performance of the trained network. This innovative methodology aims to significantly augment the precision of power loss predictions, providing valuable insights into the nuanced behaviour of magnetic materials. Frequency domain ; Computer science ; Artificial neural network ; Power (physics) ; Fourier transform ; Magnetic flux ; Waveform ; Power loss ; Time domain ; Electronic engineering ; Machine learning ; Engineering ; Magnetic field ; Mathematics ; Physics ; Telecommunications Magnetic Properties and Applications ; Magnetic properties of thin films ; Power Transformer Diagnostics and Insulation
Authors: Quan Luo | Hua Hao | Hanxing Liu
Source: Royal Society Open Science (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO <sub>3</sub> -type perovskite with doping modification The perovskite crystal structure represents a semiconductor material poised for widespread application, underpinned by attributes encompassing heightened efficiency, cost-effectiveness and remarkable flexibility. Notably, strontium titanate (SrTiO<sub>3</sub>)-type perovskite, a prototypical ferroelectric dielectric material, has emerged as a pre-eminent matrix material for enhancing the energy storage capacity of perovskite. Typically, the strategy involves augmenting its dielectric constant through doping to enhance energy storage density. However, SrTiO<sub>3</sub> doping data are plagued by significant dispersion, and the small sample size poses a formidable research hurdle, hindering the investigation of dielectric property and energy storage density enhancements. This study endeavours to address this challenge, our foundation lies in the compilation of 200 experimental records related to SrTiO<sub>3</sub>-type perovskite doping, constituting a small dataset. Subsequently, an interactive framework harnesses deep neural network models and a one-dimensional convolutional neural network model to predict and scrutinize the dataset. Distinctively, the mole percentage of doping elements exclusively serves as input features, yielding significantly enhanced accuracy in dielectric performance prediction. Lastly, rigorous comparisons with traditional machine learning models, specifically gradient boosting regression, validate the superiority and reliability of deep learning models. This research advances a novel, effective methodology and offers a valuable reference for designing and optimizing perovskite energy storage materials. Perovskite (structure) ; Doping ; Dielectric ; Materials science ; Sample (material) ; Deep learning ; Artificial intelligence ; Computer science ; Condensed matter physics ; Mineralogy ; Physics ; Optoelectronics ; Geology ; Crystallography ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Mohammed Beldjilali | Drai Ikram | Khaled Mekhissi | Inas Taha | Issam Ismail | Abdelkader Nebatti Ech‐Chergui | Lahcen Belarbi | Smaïn Bousalem
Source: Surface Review and Letters (article, 2024-08-11)
Field: Chemistry — Organic Chemistry

GREEN SYNTHESIS OF CaO–PdNps FROM LAWSONIA INERMIS LEAF EXTRACT: CATALYTIC REDUCTION OF METHYLENE BLUE WITH CENTRAL COMPOSITE DESIGN-ENHANCED NEURAL NETWORK PREDICTION In this work, small-sized CaO–PdNps nanocomposites were synthesized using Lawsonia inermis leaf extract as a reducing and capping agent. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were used to analyze the produced nanocomposites. Based on these investigations, the average particle size was found to be [Formula: see text][Formula: see text]nm, with a predominantly spherical morphology. The resulting nanocomposite was then tested for catalytic activity in methylene blue reduction. The mass of the catalyst and dye, the concentration of sodium borohydride ([NaBH4]), and the reaction time were all optimized using a central composite design (CCD). A significant correlation between predicted and experimental degradation percentages was shown by the CCD model ([Formula: see text]), which also proved statistically significant (p-[Formula: see text]). Furthermore, a deep learning neural network (DLNN) was used to improve the prediction accuracy over and beyond CCD. There was a good connection between the two models as a result of using the CCD output as DLNN validation data. Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and [Formula: see text] were used to assess the performance of the DLNN. The results ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) validated that all four metrics were successful in forecasting the percentage of degradation. Methylene blue ; Composite number ; Catalysis ; Reduction (mathematics) ; Lawsonia inermis ; Chemistry ; Central composite design ; Nuclear chemistry ; Methylene ; Materials science ; Medicine ; Traditional medicine ; Organic chemistry ; Mathematics ; Chromatography ; Composite material ; Response surface methodology ; Photocatalysis Multicomponent Synthesis of Heterocycles ; Inorganic and Organometallic Chemistry ; Ionic liquids properties and applications
Authors: Yongliang Ou | Yuji Ikeda | Lena Scholz | Sergiy V. Divinski | Felix Fritzen | Blazej Grabowski
Source: Physical Review Materials (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Li</mml:mi><mml:mn>6</mml:mn></mml:msub><mml:msub><mml:mi>PS</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mi>Cl</mml:mi></mml:mrow></mml:math> using machine-learning interatomic potentials Harnessing the potential of solid-state electrolytes is crucial for advancing all-solid-state lithium-ion batteries, yet the role of grain boundaries (GBs) in ionic conductivity remains poorly understood. The authors present an active learning approach to develop machine-learning interatomic potentials with high accuracy, enabling large-scale, long-term simulations of complex GBs in polycrystalline Li${}_{6}$PS${}_{5}$Cl. Intercage diffusion of Li ions near GBs, triggered by the ``cage-opening effect'' of GBs, is notably observed. These findings offer key insights into optimizing ionic transport in solid electrolytes, paving the way for improved performance in next-generation battery technologies. Diffusion ; Materials science ; Thermodynamics ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Jian Li | Jin Zhang
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Thermal transport properties of defective graphene/graphyne van der Waals heterostructures elucidated <i>via</i> molecular dynamics and machine learning Two-dimensional (2D) all-carbon van der Waals (vdW) heterostructures consisting of graphene and graphyne component layers are reported to have enormous application prospects. Understanding the thermal transport properties of such graphene/graphyne (G/GY) heterostructures is critical to control their performance and stability in prospective applications. In this study, using molecular dynamics simulations and a machine learning (ML) method, we investigate the thermal conductivity of pristine G/GY heterostructures and their defective counterparts. Our simulation results show a significant reduction in the thermal conductivity of G/GY heterostructures due to the presence of vacancies, which become more aggressive as the defect concentration increases. Besides the concentration, the distribution of defects is another important factor affecting the thermal conductivity of defective G/GY heterostructures. Moreover, the defect effect on the thermal conductivity of G/GY heterostructures is majorly determined by the defect characteristics of their graphene layer. Such an impact is found to originate from the changes in both phonon scattering and heat flux. Based on the ML method together with a transfer learning strategy, we also develop a convolutional neural network that can be used to quickly and effectively predict the thermal conductivities of massive possible structures of defective G/GY heterostructures. Graphyne ; Graphene ; van der Waals force ; Molecular dynamics ; Materials science ; Chemical physics ; Heterojunction ; Nanotechnology ; Thermal ; Chemistry ; Computational chemistry ; Physics ; Molecule ; Thermodynamics ; Optoelectronics ; Organic chemistry Graphene research and applications ; Thermal properties of materials ; Advancements in Battery Materials
Authors: Fangshun Wu | Heng Zou | Qiaoxin Zhang | Tongtong Zhang | Jingui Yu
Source: Computational Materials Science (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Combining machine learning and molecular dynamics to predict strength-toughness and energy dissipation mechanisms of hybrid double-crosslinked CNT networks Dissipation ; Toughness ; Molecular dynamics ; Materials science ; Energy (signal processing) ; Composite material ; Nanotechnology ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics Carbon Nanotubes in Composites ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Gan Li | Qunbo Fan | Guoju Li | Lin Yang | Haichao Gong | Meiqin Li | Shun Xu | Xingwang Cheng
Source: Materials Science and Engineering A (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Unveiling the quantitative relationship between microstructural features and quasi-static tensile properties in dual-phase titanium alloys based on data-driven neural networks Ultimate tensile strength ; Materials science ; Dual (grammatical number) ; Titanium ; Artificial neural network ; Phase (matter) ; Titanium alloy ; Composite material ; Metallurgy ; Computer science ; Artificial intelligence ; Alloy ; Chemistry Titanium Alloys Microstructure and Properties ; Welding Techniques and Residual Stresses ; Additive Manufacturing Materials and Processes
Authors: Liangcai Su | Chen Zhang | Yewei Yu | Xiuyu Zhang | Chun‐Yi Su | Miaolei Zhou
Source: Nonlinear Dynamics (article, 2024-09-16)
Field: Materials Science — Materials Chemistry

Neural network-based nonlinear model predictive control with anti-dead-zone function for magnetic shape memory alloy actuator Actuator ; Nonlinear system ; Shape-memory alloy ; Control theory (sociology) ; Nonlinear model ; Artificial neural network ; Dead zone ; Model predictive control ; Function (biology) ; Control (management) ; Computer science ; Physics ; Artificial intelligence ; Geology Shape Memory Alloy Transformations ; Iterative Learning Control Systems ; Piezoelectric Actuators and Control
Authors: Chen Chen | Enze Xu | Defu Yang | Chenggang Yan | Tao Wei | Hanning Chen | Yong Wei | Minghan Chen
Source: Neural Computing and Applications (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets Abstract This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C 3 N 4 ) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications; however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C 3 N 4 . Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. Graphitic carbon nitride ; Computer science ; Artificial neural network ; Graph ; Carbon nitride ; Band gap ; Doping ; Computational Science and Engineering ; Artificial intelligence ; Machine learning ; Materials science ; Theoretical computer science ; Optoelectronics ; Chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Hongxia Li | Lang Wu | Chenxi Xia | Shu-Chi Huang | Min Ni | Chunlin Huang | Ming Xu | Zhaohui Ruan
Source: International Communications in Heat and Mass Transfer (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics Materials science ; Artificial neural network ; Thermal ; Molecular dynamics ; Graphitic carbon nitride ; Dynamics (music) ; Nitride ; Computer science ; Thermodynamics ; Nanotechnology ; Physics ; Artificial intelligence ; Layer (electronics) Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Ulises Casado | Facundo I. Altuna | Luis A. Miccio
Source: Sustainability (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R2: 0.76) and Morgan fingerprints (MAPE: 24%, R2: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science. Principal component analysis ; Artificial neural network ; Computer science ; Mean absolute percentage error ; Dimensionality reduction ; Artificial intelligence ; Random forest ; Machine learning ; Data mining ; Pattern recognition (psychology) Machine Learning in Materials Science ; Building materials and conservation ; Advanced Chemical Sensor Technologies
Authors: Nassima Aleb | Nedal Y. Abu-Thabit
Source: Neural Computing and Applications (article, 2024-12-09)
Field: Materials Science — Polymers and Plastics

TransTg: a new transformer model for predicting glass transition temperature of polymers from monomers’ molecular structures Glass transition ; Computational Science and Engineering ; Polymer ; Monomer ; Transformer ; Materials science ; Chemical physics ; Thermodynamics ; Polymer science ; Statistical physics ; Composite material ; Computer science ; Chemistry ; Physics ; Machine learning Polymer crystallization and properties ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis