jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoxia Ma | Mojtaba Roshan | Keivan Kiani | Ali Nikkhoo
Source: Symmetry (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Dynamic Response of an Elastic Tube-like Nanostructure Embedded in a Vibrating Medium and under the Action of Moving Nano-Objects In recent years, researchers have looked at how tube-like nanostructures respond to moving loads and masses. However, no one has explored the scenario of a nanostructure embedded in a vibrating medium used for moving nano-objects. In this study, the governing equations of the problem are methodically derived using the nonlocal elasticity of Eringen as well as the Rayleigh and Reddy–Bickford beam theories. Analytical and numerical solutions are developed for capturing the nonlocal dynamic deflection of the nanostructure based on the moving nanoforce approach (excluding the inertia effect) and the moving nanomass approach (including the inertia effect), respectively. The results predicted by the established models are successfully verified with those of other researchers in some special cases. The results reveal that for low velocities of the moving nano-object in the absence of the medium excitation, the midspan deflection of the simply supported nanotube exhibits an almost symmetric time-history curve; however, by increasing the nano-object velocity or the medium excitation amplitude, such symmetry is violated, mainly due to the lateral inertia of the moving nano-object, as displayed by the corresponding three-dimensional plots. The study addresses the effects of the mass and velocity of the moving nano-object, amplitude, and frequency of the medium excitation, and the lateral and rotational stiffness of the nearby medium in contact with the nanostructure on the maximum dynamic deflection. The achieved results underscore the significance of considering both the inertial effect of the moving nano-object and the shear effect of stocky nanotubes embedded in vibrating media. This research can serve as a strong basis for conducting further investigations into the vibrational properties of more intricate tube-shaped nanosystems that are embedded in a vibrating medium, with the aim of delivering nano-objects. Deflection (physics) ; Nano- ; Nanostructure ; Excitation ; Vibration ; Inertia ; Rotary inertia ; Mechanics ; Classical mechanics ; Amplitude ; Physics ; Materials science ; Optics ; Nanotechnology ; Acoustics ; Composite material Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Xinyu Li | Qinfeng Shi | Alister J. Page
Source: Nano Letters (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning Despite today's commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials. Graphene ; Chemical vapor deposition ; Materials science ; Catalysis ; Nanotechnology ; Alloy ; Nanomaterials ; Crystallinity ; Transition metal ; Chemical engineering ; Metallurgy ; Chemistry ; Composite material Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Wei Li | Guoqiang Wang | Jing Ma
Source: National Science Review (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Deep learning for complex chemical systems Deep learning forms a bridge between the local features of molecular fragments/localized orbitals and the global properties of complex systems, enabling multi-scale simulations of complex chemical systems and reaction processes. Bridge (graph theory) ; Deep learning ; Scale (ratio) ; Atomic orbital ; Computer science ; Complex system ; Chemical physics ; Artificial intelligence ; Materials science ; Physics ; Biology ; Anatomy ; Nuclear physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ruilin Xiao | K. L. Liu | Ying Ruan | Liang Hu | B. Wei
Source: Journal of Applied Physics (article, 2023-11-21)
Field: Materials Science — Materials Chemistry

Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys The thermophysical properties of liquid metals and alloys are crucial to explore the intrinsic mechanisms of the solidification process, glass formation, and fluid dynamics. The deep learning approaches have emerged as powerful tools in numerous scientific fields and exhibit extraordinary accuracy in the estimation of physical properties and structural characteristics for various materials. In this Tutorial, focusing on the thermophysical properties of liquid multicomponent alloys, deep learning methods, including both supervised learning and active learning, are introduced. Combined with the verification from electrostatic and electromagnetic levitation experiments, the influences of training parameters and methods on the accuracy to obtain interatomic potential by deep learning are revealed on the basis of deep neural network algorithm. As a result, this prediction method of liquid state properties for multicomponent alloys exhibited the dual advantages of high accuracy derived from density functional theory and low computational cost associated with empirical potential. Deep learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Process (computing) ; Machine learning ; Materials science ; Statistical physics ; Physics Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics
Authors: Diptendu Roy | Shyama Charan Mandal | A. Das | Biswarup Pathak
Source: Chemistry - A European Journal (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Unravelling CO<sub>2</sub> Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations Establishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO<sub>2</sub> reduction reaction (CO<sub>2</sub> RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model-Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H<sub>2</sub> CO (MAE: 0.24 eV) and *H<sub>3</sub> CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state-of-the-art ML in catalysis towards better interpretability. Interpretability ; Scaling ; Adsorption ; Catalysis ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Mathematics ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Esther Heid | Charles J. McGill | Florence H. Vermeire | William H. Green
Source: ChemRxiv (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

Characterizing Uncertainty in Machine Learning for Chemistry Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on datasets of molecular properties, we show important trends in model performance associated with the level of noise in the dataset, size of the dataset, model architecture, molecule representation, ensemble size, and dataset splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance, and 4) evaluations of cross-validation models understate their performance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts. Robustness (evolution) ; Computer science ; Variance (accounting) ; Machine learning ; Uncertainty quantification ; Artificial intelligence ; Context (archaeology) ; Noise (video) ; Ensemble learning ; Property (philosophy) ; Reliability (semiconductor) ; Representation (politics) ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Jingting Wang | Mingying Lu | Yongxing Chen | Guolin Hao | Bin Liu | Pinghua Tang | Yu Lian | Wen Lei | Haining Ji
Source: Nanomaterials (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials Molybdenum disulfide (MoS<sub>2</sub>) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS<sub>2</sub> with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS<sub>2</sub> obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS<sub>2</sub> material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and <i>p</i>-value (<i>p</i>_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS<sub>2</sub> materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS<sub>2</sub> materials with a reduction in the time and cost of trial and error. Molybdenum disulfide ; Chemical vapor deposition ; Molybdenum ; Materials science ; Correlation coefficient ; Sulfur ; Mean squared error ; Regression analysis ; Nanotechnology ; Biological system ; Computer science ; Mathematics ; Machine learning ; Metallurgy ; Statistics 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: Tamir S. Hasan | Laurent Capolungo | M.A. Zikry
Source: Scientific Reports (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys Zirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint. Hydride ; Materials science ; Texture (cosmology) ; Fracture (geology) ; Zirconium ; Stress (linguistics) ; Zirconium alloy ; Metallurgy ; Composite material ; Artificial intelligence ; Computer science Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: E. O. Khazieva | N. M. Shchelkatchev | Azat O. Tipeev | R. E. Ryltsev
Source: Journal of Experimental and Theoretical Physics (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models In several recent years, a significant progress has been made in atomistic simulation of materials, involving the application of machine learning methods to constructing classical interatomic interaction potentials. These potentials are many-body functions with a large number of variable parameters whose values are optimized with the use of energies and forces calculated for various atomic configurations by ab initio methods. In the present paper a machine learning potential is developed on the basis of deep neural networks (DP) for Al–Cu alloys, and the accuracy and performance of this potential is compared with the embedded atom potential. The analysis of the results obtained implies that the DP provides a sufficiently high accuracy of calculation of the structural, thermodynamic, and transport properties of Al–Cu alloys in both solid and liquid states over the entire range of compositions and a wide temperature interval. The accuracy of the embedded atom model (EAM) in calculating the same properties is noticeably lower on the whole. It is demonstrated that the application of the potentials based on neural networks to the simulation on modern graphic processors allows one to reach a computational efficiency on the same order of magnitude as those of the embedded atom calculations, which at least four orders of magnitude higher than the computational efficiency of ab initio calculations. The most important result is that about the possibility of application of DP parameterized with the use of configurations corresponding to melts and perfect crystals to the simulation of structural defects in crystals and interphase surfaces. Transferability ; Atom (system on chip) ; Materials science ; Artificial intelligence ; Computer science ; Machine learning ; Atomic physics ; Physics ; Parallel computing Nuclear Materials and Properties ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science
Authors: Seunghee Han | Jaewan Lee | Sehui Han | Seyed Mohamad Moosavi | Jihan Kim | Chang-Young Park
Source: Journal of Chemical Information and Modeling (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Design of New Inorganic Crystals with the Desired Composition Using Deep Learning New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning. Autoencoder ; Crystal (programming language) ; Generative grammar ; Atom (system on chip) ; Generative model ; Materials science ; Crystal structure prediction ; Diffusion ; Computer science ; Composition (language) ; Range (aeronautics) ; Artificial intelligence ; Crystal structure ; Algorithm ; Deep learning ; Statistical physics ; Biological system ; Chemistry ; Crystallography ; Thermodynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Teerachote Pakornchote | Annop Ektarawong | Thiparat Chotibut
Source: Physical Review Research (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic-scale ab initio approaches are computationally intensive, especially for studying complex materials with a large number of atoms in a unit cell. We introduce a data-driven approach to efficiently predict the elastic properties of crystal structures using SE(3)-equivariant graph neural networks (GNNs). This approach yields important scalar elastic moduli with an accuracy comparable to that of recent data-driven studies. Importantly, our symmetry-aware GNN model also enables the prediction of the strain energy density (SED) and the associated elastic constants, the fundamental tensorial quantities that are significantly influenced by a material's crystallographic group. The model consistently distinguishes independent elements of SED tensors, in accordance with the symmetry of the crystal structures. Finally, our deep learning model possesses meaningful latent features, offering an interpretable prediction of the elastic properties. Equivariant map ; Elastic modulus ; Crystal structure prediction ; Ab initio ; Crystal structure ; Moduli ; Artificial neural network ; Scalar (mathematics) ; Statistical physics ; Symmetry (geometry) ; Materials science ; Computer science ; Graph ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Theoretical computer science ; Crystallography ; Geometry ; Thermodynamics ; Chemistry ; Pure mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Daniele Padula | Leonardo Barneschi | Andrea Peluso | Tommaso Cinaglia | Alessandro Landi
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Towards a fast machine-learning-assisted prediction of the mechanoelectric response in organic crystals A protocol combining molecular dynamics, density functional theory, machine learning, and kinetic Monte Carlo accurately predicts the dependence of charge mobility on mechanical deformations for organic semiconductors. Materials science ; Organic semiconductor ; Kinetic Monte Carlo ; Density functional theory ; Charge (physics) ; Monte Carlo method ; Molecular dynamics ; Chemical physics ; Statistical physics ; Biological system ; Computational chemistry ; Optoelectronics ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Force Microscopy Techniques and Applications
Authors: Muhammad Khurram Tufail | Syed Shoaib Ahmad Shah | Salahuddin Khan | Farooq Ahmad | Lucy W. Kiruri | Misbah Sehar Abbasi | Ali Ahmad
Source: Chemical Physics Letters (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Designing efficient materials for high-performance organic solar cells: Detailed chemical space exploration, machine learning and virtual screening Chemical space ; Hyperparameter ; Organic solar cell ; Computer science ; Virtual screening ; Space (punctuation) ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications