jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongqiu Zhu | Lei Peng | Can Zhou | Yusi Dai | Tianyu Peng
Source: Measurement Science and Technology (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

An attention-based long short-term memory prediction model for working conditions of copper electrolytic plates Abstract Copper is an important source of non-ferrous metals, with electrolytic refining being one of the main methods to produce fine copper. In the electrolytic process, plate states seriously affect the output and quality of the copper. Therefore, timely and accurate prediction of the working states of the plates is of great significance to the copper electrolytic refining process. Aiming at the issues associated with traditional plate state detection algorithms of large lag, poor anti-interference ability and low accuracy, a plate state prediction model based on a long short-term memory (LSTM) neural network with an attention mechanism is here proposed in this paper. The average gray values of the plates in infrared imagery are used to characterize the plates’ working states. To address the problems of large fluctuation and the large amount of time series data required in such a study, a double-layer LSTM neural network structure is used to improve the efficiency and accuracy of model training. Meanwhile, in view of the periodicity of the time series data and the possible correlation between adjacent data, a unique attention mechanism is proposed to enable the model to learn this correlation between the adjacent data so as to improve the accuracy of the model prediction. The experimental results show that the accuracy of the proposed model for plate state prediction reaches 95.11%. Compared with commonly used prediction algorithms, the plate state prediction model proposed in this paper demonstrates stronger prediction ability. Computer science ; Artificial neural network ; Refining (metallurgy) ; Copper ; Lag ; Artificial intelligence ; Data mining ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Tarik Sadat
Source: Applied Mechanics (article, 2023-06-09)
Field: Materials Science — Polymers and Plastics

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites Flax fiber/shape memory epoxy hygromorph composites are a promising area of research in the field of biocomposites. This paper focuses on the tensile modulus of these composites and investigates how it is affected by factors such as fiber orientation (0° and 90°), temperature (20 °C, 40 °C, 60 °C, 80 °C, and 100 °C), and humidity (50% and fully immersed) conditions. Machine learning algorithms were utilized to predict the tensile modulus based on non-linearly dependent initial variables. Both decision tree (DT) and random forest (RF) algorithms were employed to analyze the data, and the results showed high coefficient of determination R2 values of 0.94 and 0.95, respectively. These findings demonstrate the effectiveness of machine learning in analyzing large datasets of mechanical properties in biocomposites. Moreover, the study revealed that the orientation of the flax fibers had the greatest impact on the tensile modulus value (with feature importance of 0.598 and 0.605 for the DT and RF models, respectively), indicating that it is a crucial factor to consider when designing these materials. Composite material ; Ultimate tensile strength ; Materials science ; Epoxy ; Modulus ; Fiber ; Young's modulus ; Humidity ; Random forest ; Response surface methodology ; Machine learning ; Computer science Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Electrospun Nanofibers in Biomedical Applications
Authors: Xiaowei Zhang | Chong Wang | Xiaoyu Liu | Yueyao Fan | Ting Cao | Di Xiao
Source: arXiv (Cornell University) (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Polarization-driven band topology evolution in twisted MoTe$_2$ and WSe$_2$ Motivated by recent experimental observations of opposite Chern numbers in $R$-type twisted MoTe$_2$ and WSe$_2$ homobilayers, we perform large-scale density-functional-theory (DFT) calculations with machine learning force fields to investigate moiré band topology from large to small twist angles in both materials. We find that the Chern numbers of the moiré frontier bands change sign as a function of twist angle, and this change is driven by the competition between moiré ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moiré-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau-level physics in the absence of a magnetic field. Topology (electrical circuits) ; Ferroelectricity ; Physics ; Polarization (electrochemistry) ; Twist ; Length scale ; Condensed matter physics ; Geometry ; Quantum mechanics ; Mathematics 2D Materials and Applications ; Topological Materials and Phenomena ; Machine Learning in Materials Science
Authors: Hanjing Tang | Siqi Wang | Shixin Lai | Yaowen Chen | Wei Yang | Gang Xiao | Xiaolei Zhang
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Quasi-Steady-State CEST Prediction Based on TCN-LSTM An important topic in chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is that certain CEST effects (such as amide proton transfer effects) require sufficiently long saturation time to reach steady state, which becomes a constraint to the clinical application. To address this issue, we develop a deep learning-based model to predict quasi-steady-state (QUASS) CEST from CEST images with short saturation times obtained from actual experiments. We outline this study as follows: (1) A Bloch-McConnell equation is designed to obtain simulated CEST Z-spectra data as the training dataset, in which all possible parameters of the equation were optimized to automatically acquire large amount of training data reflecting metabolite combinations. (2) A tumor-bearing rat model was established on a 7T horizontal diameter small animal MRI scanner, allowing ground-truth generation. (3) By combining the advantages of temporal convolutional network (TCN) and long short-term memory (LSTM) in temporal modelling, a TCN-LSTM model is developed to predict QUASS CEST data. (4) To evaluate the performance of TCN-LSTM, the multilayer perceptron (MLP), recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), BiLSTM and TCN are included in comparison experiment. In terms of mean absolute error, mutual information (MI), structural similarity (SSIM) and feature similarity (FSIM), the results show that TCN-LSTM provides better prediction results than its counterparts. Recurrent neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Convolutional neural network ; Deep learning ; Artificial neural network ; Algorithm Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Simon Batzner
Source: Nature Computational Science (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Biasing energy surfaces towards the unknown Biasing ; Molecular dynamics ; Energy (signal processing) ; Potential energy ; Statistical physics ; Surface (topology) ; Materials science ; Computer science ; Nanotechnology ; Biological system ; Voltage ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Classical mechanics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuelin Zhang | Rui Zheng
Source: CORROSION (article, 2023-07-18)
Field: Materials Science — Materials Chemistry

Advanced Algorithms to Predict Time-Dependent Atmospheric Corrosion Wastage of Low-Alloy and High-Strength Steels Based on Chemical Compositions The mathematical relationship between corrosion degree and time is referred to as a corrosion model. Existing corrosion models can only be used to predict the corrosion wastage of a certain material based on its available historical corrosion data, but the corrosion wastage of newer steel grades cannot be obtained if the data are not available. To solve this problem, two advanced algorithms, i.e., generalized regression neural network (GRNN) and optimizing grey model (OGM (1, N)), are introduced, based on which corrosion models can be obtained for steel classes even in the absence of historical corrosion data, as long as the chemical compositions of the material are known. First, the theoretical basis and operational procedures of GRNN and OGM (1, N) are introduced. Grey relational analysis of corrosion wastage influencing factors is subsequently conducted. Last, the time-dependent atmospheric corrosion wastages of Q345 and Q460 steels, two typical structural steel grades but their corrosion models have not been well established, are predicted based on their chemical compositions by these two advanced algorithms. The results show that the main chemical compositions that influence the atmospheric corrosion wastage of steels are C and S. Both GRNN and OGM (1, N) can accurately predict the corrosion wastage of the steels, and the predicted results can be fitted by quadratic function or power function, where the goodness of fit is greater than 0.95, which indicates a high fitting accuracy. Corrosion ; Alloy ; Materials science ; Metallurgy ; Algorithm ; Computer science Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses
Authors: Tanooj Shah | Kamron Fazel | Jie Lian | Liping Huang | Yunfeng Shi | Ravishankar Sundararaman
Source: The Journal of Chemical Physics (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

First-principles molten salt phase diagrams through thermodynamic integration Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. We show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials. Thermodynamic integration ; Phase diagram ; Statistical physics ; Molecular dynamics ; Dispersion (optics) ; Boundary (topology) ; Interatomic potential ; Density functional theory ; Phase boundary ; Phase (matter) ; Thermodynamics ; Molten salt ; Materials science ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Changxin Wang | Mei Yang | Shuo Cao | Xiao‐Xu Wang | Hao Fu | Yang Bai | Turab Lookman | Ping Qian | Yanjing Su
Source: Physical Review Materials (article, 2023-08-02)
Field: Materials Science — Materials Chemistry

Regulation mechanisms of electron-delocalized single transition metal-doped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Mo</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">C</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> MXene hydrogen evolution reaction catalysts Two-dimensional (2D) Mo-based MXenes $({\mathrm{Mo}}_{n+1}{C}_{n}{T}_{x})$ are recognized to have significant potential as hydrogen evolution reaction (HER) activity electrocatalysts. However, appropriate descriptors are absent to predict the H-adsorption Gibbs energy $(\mathrm{\ensuremath{\Delta}}{G}_{\mathrm{H}})$ due to the unique delocalized electronic properties of the Mo atom. In this paper, we used first-principles calculations and machine learning to study the HER activity of ${\mathrm{Mo}}_{2}\mathrm{C}{\mathrm{O}}_{2}$ with single transition metal-doped (${\mathrm{Mo}}_{2}\mathrm{C}{\mathrm{O}}_{2}$-STM), and elucidate the mechanisms by which single transition metals (STMs) regulate the hydrogen evolution reaction. Our results revealed that $\mathrm{\ensuremath{\Delta}}{G}_{\mathrm{H}}$ has a ``W'' shape as a function of the doped atom changing in one period. The electronic structure analysis indicates that the electronic delocalized Mo has a longer range affecting not only the nearest atoms, but the second-nearest neighbor (STM-Mo) bonding effect controls the periodic distribution of $\mathrm{\ensuremath{\Delta}}{G}_{\mathrm{H}}$. Using machine-learning method, we quantized the STM regulation mechanism using five key structural and electronic descriptors, and predicted the $\mathrm{\ensuremath{\Delta}}{G}_{\mathrm{H}}$ of ${\mathrm{Mo}}_{2}\mathrm{C}{\mathrm{O}}_{2}$-STM, which were also extended to ${\mathrm{W}}_{2}\mathrm{C}{\mathrm{O}}_{2}$-STM successfully. Our findings highlight the importance of considering second-nearest-neighbor bonding effects in similar delocalized materials systems research. Delocalized electron ; Crystallography ; Atom (system on chip) ; Electronic structure ; Materials science ; Physics ; Doping ; Machine learning ; Condensed matter physics ; Chemistry ; Computer science ; Quantum mechanics MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: Xiang Feng | Ruilin Dong | Yuanjian Li | Xiaopeng Liu | Chao Lin | Tianshuai Wang | Zhi Wei Seh | Qianfan Zhang
Source: Energy storage materials (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

A systematic study on the metallophilicity of ordered five-atomic-layer MXenes using high-throughput automated workflow and machine learning MXenes ; Materials science ; Anode ; Throughput ; Layer (electronics) ; Workflow ; Computer science ; Metal ; Nanotechnology ; Artificial intelligence ; Chemistry ; Metallurgy ; Electrode ; Physical chemistry ; Wireless MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Jiawei Jiang | Li-Chun Xu | Fenglian Li | Jian-Li Shao
Source: Metals (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Machine Learning Potential Model Based on Ensemble Bispectrum Feature Selection and Its Applicability Analysis With the continuous improvement of machine learning methods, building the interatomic machine learning potential (MLP) based on the datasets from quantum mechanics calculations has become an effective technical approach to improving the accuracy of classical molecular dynamics simulation. The Spectral Neighbor Analysis Potential (SNAP) is one of the most commonly used machine learning potentials. It uses the bispectrum to encode the local environment of each atom in the lattice. The hyperparameter jmax controls the mapping complexity and precision between the local environment and the bispectrum descriptor. As the hyperparameter jmax increases, the description will become more accurate, but the number of parameters in the bispectrum descriptor will increase dramatically, increasing the computational complexity. In order to reduce the computational complexity without losing the computational accuracy, this paper proposes a two-level ensemble feature selection method (EFS) for a bispectrum descriptor, combining the perturbation method and the feature selector ensemble strategy. Based on the proposed method, the feature subset is selected from the original dataset of the bispectrum descriptor for building the dimension-reduced MLP. As a method application and validation, the data of Fe, Ni, Cu, Li, Mo, Si, and Ge metal elements are used to train the linear regression model based on SNAP for predicting these metals’ atomic energies and forces them to evaluate the performance of the feature subsets. The experimental results show that, compared to the features of SNAP and qSNAP, the training complexity improvement of our EFS method on the qSNAP feature is more effective than SNAP. Compared with the existing methods, when the feature subset size is 0.7 times that of the original features, the proposed EFS method based on the SSWRP ensemble strategy can achieve the best performance in terms of stability, achieving an average stability of 0.94 across all datasets. The training complexity of the linear regression model is reduced by about half, and the prediction complexity is reduced by about 30%. Bispectrum ; Hyperparameter ; Computer science ; Artificial intelligence ; Feature selection ; Feature (linguistics) ; Computational complexity theory ; Machine learning ; Ensemble learning ; Dimensionality reduction ; Pattern recognition (psychology) ; Algorithm Machine Learning in Materials Science ; Machine Learning and ELM ; Fault Detection and Control Systems
Authors: Xinpeng Zhao | Ruiqiang Qin | Jie Zhang | Zugang Liu | Dong Wang | Haiyou Huang | Lei Gao | Yanjing Su | Lijie Qiao
Source: Computational Materials Science (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Machine learning method to predict the interlayer sliding energy barrier of polarized MoS2 layers Stacking ; Density functional theory ; Energy (signal processing) ; Materials science ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Condensed matter physics ; Computational chemistry ; Physics ; Chemistry ; Nuclear magnetic resonance Graphene research and applications ; 2D Materials and Applications ; Ga2O3 and related materials
Authors: Xiangyue Liu | Weiqi Wang | Jesús Pérez‐Ríos
Source: The Journal of Chemical Physics (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer In this work, we present a full-dimensional potential energy surface for AlF-AlF. We apply a general machine learning approach for full-dimensional potential energy surfaces, employing an active learning scheme trained on ab initio points, whose size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As a result, it is possible to provide the full-dimensional AlF-AlF potential energy surface, requiring ≲0.01% of the configurations to be calculated ab initio. Furthermore, we analyze the general properties of the AlF-AlF system, finding critical differences with other reported results on CaF or bi-alkali dimers. Potential energy surface ; Ab initio ; Potential energy ; Molecular dynamics ; Energy (signal processing) ; Work (physics) ; Range (aeronautics) ; Surface (topology) ; Dimer ; Statistical physics ; Computer science ; Ab initio quantum chemistry methods ; Computational chemistry ; Physics ; Chemistry ; Materials science ; Quantum mechanics ; Molecule ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Ali Riza Durmaz | Sai Teja Potu | Daniel Romich | Johannes J. Möller | Ralf Nützel
Source: Frontiers in Materials (article, 2023-08-10)
Field: Materials Science — Metals and Alloys

Microstructure quality control of steels using deep learning In quality control, microstructures are investigated rigorously to ensure structural integrity, exclude the presence of critical volume defects, and validate the formation of the target microstructure. For quenched, hierarchically-structured steels, the morphology of the bainitic and martensitic microstructures are of major concern to guarantee the reliability of the material under service conditions. Therefore, industries conduct small sample-size inspections of materials cross-sections through metallographers to validate the needle morphology of such microstructures. We demonstrate round-robin test results revealing that this visual grading is afflicted by pronounced subjectivity despite the thorough training of personnel. Instead, we propose a deep learning image classification approach that distinguishes steels based on their microstructure type and classifies their needle length alluding to the ISO 643 grain size assessment standard. This classification approach facilitates the reliable, objective, and automated classification of hierarchically structured steels. Specifically, an accuracy of 96% and roughly 91% is attained for the distinction of martensite/bainite subtypes and needle length, respectively. This is achieved on an image dataset that contains significant variance and labeling noise as it is acquired over more than 10 years from multiple plants, alloys, etchant applications, and light optical microscopes by many metallographers (raters). Interpretability analysis gives insights into the decision-making of these models and allows for estimating their generalization capability. Microstructure ; Interpretability ; Materials science ; Optical microscope ; Artificial intelligence ; Martensite ; Bainite ; Reliability (semiconductor) ; Computer science ; Metallurgy ; Composite material ; Scanning electron microscope Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses