jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suyang An | Kun Li | Liang Zhu | Haisong Liang | Ruijin Ma | Ruobing Liao | L.E. Murr
Source: Frontiers in Materials (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

A framework for computer-aided high performance titanium alloy design based on machine learning Titanium alloy exhibits exceptional performance and a wide range of applications, with the high performance serving as the foundation for the development. However, traditional material design methods encounter numerous calculations and experimental trial-and-error processes, leading to increased costs and decreased efficiency in material design. The data-driven model presents an intriguing alternative to traditional material design methods by offering a novel approach to expedite the materials design process. In this study, a framework for computer-aided design high performance titanium alloys based on machine learning is proposed, which constructs an intelligent search space encompassing various combinations of 18 elements to facilitate alloy design. Firstly, a proprietary dataset was constructed for titanium alloy materials using feature design and a combination of unsupervised and supervised feature engineering methods. Secondly, six machine learning algorithms were employed to establish regression models, and the hyperparameters of each algorithm were optimized to improve model performance. Thirdly, the model was screened using five regression algorithm evaluation methods. The results demonstrated that the selected optimized model achieved an R 2 value of 0.95 on the verification set and 0.93 on the test set, yielding satisfactory outcomes. Finally, a comprehensive model framework along with an intelligent search methodology for designing high-strength titanium alloys has been established. It is believed that this method is also applicable to other properties of titanium alloys and the optimization of other materials. Computer science ; Hyperparameter ; Titanium alloy ; Machine learning ; Artificial intelligence ; Titanium ; Feature (linguistics) ; Set (abstract data type) ; Engineering design process ; Materials science ; Alloy ; Mechanical engineering ; Engineering Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Haoyu Dong | Zhiqiang Li | Baole Sun | Yanguang Zhou | Linhua Liu | Jia‐Yue Yang
Source: Materials Today Communications (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Thermal transport in disordered wurtzite ScAlN alloys using machine learning interatomic potentials Materials science ; Wurtzite crystal structure ; Thermal ; Interatomic potential ; Condensed matter physics ; Molecular dynamics ; Thermodynamics ; Metallurgy ; Computational chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Mostafa Elaskalany | Kamran Behdinan
Source: Advanced Engineering Materials (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Stochastic Multiscale Modeling of Electrical Conductivity of Carbon Nanotube Polymer Nanocomposites: An Interpretable Machine Learning Approach This study introduces an interpretable machine learning (ML) framework for efficiently predicting the electrical conductivity of carbon nanotube (CNT)/polymer nanocomposites. A stochastic multiscale numerical model based on representative volume element (RVE) is employed to generate a representative dataset. This dataset is used to train three ML models, including random forest, XGBoost, and artificial neural networks (ANN). The dataset includes six input features: CNT length, aspect ratio, intrinsic CNT conductivity, number of CNT conduction channels, energy barrier height, and volume fraction, with the electrical conductivity of the nanocomposites as the output feature. The findings highlight the exceptional accuracy of the ANN model in predicting electrical conductivity at significantly lower computational costs. Furthermore, the use of Shapley additive explanations (SHAP) enhances the interpretability of these ML models, identifying the volume fraction, energy barrier height, and intrinsic CNT conductivity as the most influential factors affecting conductivity. This approach sets the stage for rapid and efficient modeling of CNT/polymer nanocomposites facilitating the design of materials with tailored electrical properties for diverse applications. Carbon nanotube ; Materials science ; Nanocomposite ; Polymer ; Nanotechnology ; Polymer nanocomposite ; Conductivity ; Multiscale modeling ; Electrical resistivity and conductivity ; Conductive polymer ; Composite material ; Engineering ; Physics Carbon Nanotubes in Composites ; High voltage insulation and dielectric phenomena ; Low-power high-performance VLSI design
Authors: Mohamed Saib | Alain Moussa | Matteo Beggiato | Benjamin Groven | Henry Medina | P. Morin | Janusz Bogdanowicz | Gouri Sankar Kar | Anne-Laure Charley
Source:  (article, 2024-04-10)
Field: Materials Science — Surfaces, Coatings and Films

Advanced characterization of 2D materials using SEM image processing and machine learning 2D materials hold significant potential for enhancing semiconductor device performance. However, their integration necessitates the establishment of a robust metrology that is both accurate and fast, enabling a comprehensive understanding and precise control of their growth processes. Scanning electron microscopy (SEM) ticks all the boxes to be a promising technique for 2D materials characterization due to its sensitivity to monolayers (ML) and its high measurement throughput. Nevertheless, automating its measurement analysis is essential to avoid slow data processing and inaccuracies in results. In this work, I propose a set of image-processing algorithms to extract various 2D material properties from SEM images and reveal hidden aspects of their growth processes. Firstly, I implemented and benchmarked two competing segmentation algorithms to process raw SEM images and localize surface regions corresponding to the substrate, 1ML, and 2ML. The first algorithm employs a statistical approach, named global thresholding, while the second is based on supervised machine Learning. These two algorithms were evaluated on a batch of wafers where tungsten disulfide (WS<sub>2</sub>) material was grown, reaching a maximum of 2ML. The machine learning algorithm demonstrated exceptional performance, achieving segmentation success rates that surpassed 98%, outperforming the global thresholding technique, which has a success rate of 86.5%. Subsequently, other algorithms were developed to extract quality indicators of 2D material layers from the segmented images, such as the coverage rate, and the count of basic crystals and islands. Reviewing all extracted properties enabled us to assess the process uniformity on wafers across different facets. On the other hand, the cross-analysis of these features unveiled some fundamental properties of the studied growth process. We determined the 1ML coverage at which crystals transition from a quasi-isolated growth to the coalescence phase, as well as when the formation of a uniform 1ML begins. Finally, we deduced the important relationship between the growth rates of 1ML and 2ML. Thresholding ; Computer science ; Artificial intelligence ; Wafer ; Algorithm ; Throughput ; Image processing ; Machine learning ; Image segmentation ; Characterization (materials science) ; Machine vision ; Segmentation ; Materials science ; Image (mathematics) ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Keisuke Kameda | Takaaki Ariga | Kazuma Ito | Manabu Ihara | Sergei Manzhos
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation Machine learning of the screening factor in the SoftBV approximation as a function of chemical composition was used to improve the accuracy of structure estimation with SoftBV to help rapid prescreening of ceramic materials. Valence (chemistry) ; Computer science ; Artificial intelligence ; Bond ; Materials science ; Machine learning ; Chemical physics ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Business ; Biology ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Amir Teimouri | Guoqiang Li
Source: Journal of Polymer Science (article, 2024-12-25)
Field: Materials Science — Polymers and Plastics

Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders ABSTRACT The discovery of high‐performance shape memory polymers (SMPs) with enhanced glass transition temperatures (Tg) is of paramount importance in fields such as geothermal energy, oil and gas, aerospace, and other high‐temperature applications, where materials are required to exhibit shape memory effect at extremely high‐temperature conditions. Here, we employ a novel machine learning framework that integrates transfer learning with variational autoencoders (VAE) to efficiently explore the chemical design space of SMPs and identify new candidates with high Tg values. We systematically investigate the effect of different latent space dimensions on the VAE model performance. Several machine learning models are then trained to predict Tg. We find that the SVM model demonstrates the highest predictive accuracy, with R 2 values exceeding 0.87 and a mean absolute percentage error as low as 6.43% on the test set. Through systematic molar ratio adjustments and VAE‐based fingerprinting, we discover novel SMP candidates with Tg values between 190°C and 200°C, suitable for high‐temperature applications. These findings underscore the effectiveness of combining VAEs and SVM for SMP discovery, offering a scalable and efficient method for identifying polymers with tailored thermal properties. Thermosetting polymer ; Glass transition ; Shape-memory polymer ; Polymer ; Materials science ; Transition (genetics) ; Polymer science ; Polymer chemistry ; Composite material ; Artificial intelligence ; Computer science ; Chemistry Polymer composites and self-healing ; Machine Learning in Materials Science ; Photopolymerization techniques and applications
Authors: Kyle R. Stoltz | Mario F. Borunda
Source: The Journal of Physical Chemistry A (article, 2024-01-23)
Field: Materials Science — Polymers and Plastics

Benchmarking DFT and Supervised Machine Learning: An Organic Semiconducting Polymer Investigation Using a training set consisting of twenty-two well-known semiconducting organic polymers, we studied the ability of a simple linear regression supervised machine learning algorithm to accurately predict the bandgap (BG) and ionization potential (IP) of new polymers. We show that using the PBE or PW91 exchange-correlation functionals and this simple linear regression, calculated BGs and IPs can be obtained with average percent errors of less than 3 and 4%, respectively. We then apply this method to predict the BG and IP of a group of new polymers composed of monomers used in the training set and their derivatives in AABB and ABAB orientations. Benchmarking ; Polymer ; Monomer ; Set (abstract data type) ; Linear regression ; Regression ; Materials science ; Simple (philosophy) ; Computer science ; Artificial intelligence ; Machine learning ; Linear correlation ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Xi Zhang | Haitao Chen | Kangpu Li | Bo Wen | Jiang Ma | Qiang Wu
Source: ACS Applied Nano Materials (article, 2024-06-07)
Field: Materials Science — Materials Chemistry

Machine-Learning Accelerated DFT to Predict Activation Energies on Amorphous Nanocatalysts for the Chlorine Evolution Reaction In order to improve the production efficiency of chlorine (Cl2) and reduce production costs, electrocatalysts with high activity are essential. The conventional catalysts such as dimensionally stable anode (DSA) have the disadvantages of high cost and poor selectivity. Amorphous alloy is a promising candidate for a low-cost and efficient chlorine evolution reaction (CER) electrocatalyst. In this letter, we predicted an efficient amorphous NiFeP catalyst by using machine-learning (ML) accelerated density functional theory (DFT). There is a problem of insufficient adsorption sites in traditional DFT calculations. To deal with this insufficiency, we developed a distance contribution descriptor for ML feature engineering and calculated the Gibbs free energies (ΔGCl) of 50400 Cl* binding sites on the surface of Ni40Fe40P20 by ML-accelerated DFT. At the same time, the analysis of catalytic reaction pathways, bonding configurations, and surface adsorption capacity shows that the CER is more inclined to the Volmer–Heyrovsky pathway, and the Ni and Fe atoms contribute to the adsorption of Cl–, while the P atom contributes to the desorption of Cl–. These research methods provide an idea for predicting the activation energies of amorphous nanocatalysts. Nanomaterial-based catalyst ; Chlorine ; Amorphous solid ; Materials science ; Activation energy ; Chemical engineering ; Chemistry ; Nanotechnology ; Physical chemistry ; Metallurgy ; Engineering ; Crystallography ; Nanoparticle Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Yingyu Gao | Yunfeng Hu | Xinpeng Zhao | Yang Liu | Haiyou Huang | Yanjing Su
Source: Preprints.org (preprint, 2024-08-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys In recent years, due to the detrimental impact of traditional gas-liquid refrigerants on the environment, the pursuit of solid-state refrigeration technology based on elastocaloric effect, as a promising sustainable alternative, has been promoted. However, the traditional trial-and-error method is inefficient and difficult to meet the material application requirements. In this work, machine learning (ML) was employed to accelerate the development of NiTi-based shape memory alloy (SMA) with excellent elastocaloric effect. By means of active learning, we identified 9 novel NiTi-based SMAs in four iterations, the phase transformation-induced entropy change (ΔS) of which are as high as over 90 J/kg·K-1. Moreover, the model exhibits good interpretability, allowing us to understand the relevance between the target and features from the aspect of mechanism. Nickel titanium ; Shape-memory alloy ; Materials science ; Metallurgy ; Computer science ; Mechanical engineering ; Engineering Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Topology Optimization in Engineering
Authors: Zihao Yang | Xixin Wu | Xindang He | Xiaofei Guan
Source: Engineering Applications of Artificial Intelligence (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

A multiscale analysis-assisted two-stage reduced-order deep learning approach for effective thermal conductivity of arbitrary contrast heterogeneous materials Computer science ; Contrast (vision) ; Thermal conductivity ; Stage (stratigraphy) ; Artificial intelligence ; Order (exchange) ; Materials science ; Composite material Thermal properties of materials ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: Frank Cerasoli | Davide Donadio
Source: The Journal of Chemical Physics (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale. Atomic units ; Phase transition ; Statistical physics ; Simulated annealing ; Materials science ; Occupancy ; Chemical physics ; Algorithm ; Computer science ; Chemistry ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Ke Ye | Song Wang | Yan Huang | Min Hu | Donglai Zhou | Yi Luo | Sheng Ye | Guozhen Zhang | Jun Jiang
Source: The Journal of Physical Chemistry Letters (article, 2024-02-12)
Field: Chemistry — Electrochemistry

Machine Learning Prediction of Molecular Binding Profiles on Metal-Porphyrin via Spectroscopic Descriptors The study of molecular adsorption is crucial for understanding various chemical processes. Spectroscopy offers a convenient and non-invasive way of probing structures of adsorbed states and can be used for real-time observation of molecular binding profiles, including both structural and energetic information. However, deciphering atomic structures from spectral information using the first-principles approach is computationally expensive and time-consuming because of the sophistication of recording spectra, chemical structures, and their relationship. Here, we demonstrate the feasibility of a data-driven machine learning approach for predicting binding energy and structural information directly from vibrational spectra of the adsorbate by using CO adsorption on iron porphyrin as an example. Our trained machine learning model is not only interpretable but also readily transferred to similar metal-nitrogen-carbon systems with comparable accuracy. This work shows the potential of using structure-encoded spectroscopic descriptors in machine learning models for the study of adsorbed states of molecules on transition metal complexes. Porphyrin ; Adsorption ; Molecule ; Binding energy ; Chemistry ; Spectroscopy ; Transition metal ; Artificial intelligence ; Machine learning ; Biological system ; Computer science ; Chemical physics ; Physical chemistry ; Physics ; Atomic physics ; Photochemistry ; Organic chemistry Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications
Authors: Hajime Shimakawa | Akiko Kumada | Masahiro Sato
Source: Journal of Chemical Information and Modeling (article, 2024-04-20)
Field: Materials Science — Materials Chemistry

Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties Machine learning (ML) has facilitated property prediction for intricate materials by integrating materials and experimental features such as processing and measurement conditions. However, ML models designed for material properties have often disregarded a common issue of "leakage," resulting in an overestimation of model performance and a decrease in model transferability. This issue can arise from biases inherent in multiple data points obtained from the same experimental group. We provide a critical examination and prevention method of leakage in property prediction for polymer composites. Our proposed method utilizes data partitioning based on the experimental group to ensure that data from the same group are not mixed in both the training and test sets. Evaluation results highlight that the conventional random partitioning unintentionally inflates ML performance through the misuse of experimental features for leaking data bias within the same experimental group rather than explaining the physical causality. In contrast, the proposed method enables the leakage-free utilization of experimental features to improve prediction accuracy while ensuring model transferability. Specifically, when integrating experimental features with polymer and filler features, the conventional method overestimates the prediction performance of electrical conductivity in reducing RMSE by 26% depending on leakage, whereas the proposed method achieves a reduction in RMSE by 5% without leakage. These findings offer valuable guidance for the effective utilization of experimental features in data-driven materials science. Leakage (economics) ; Transferability ; Computer science ; Experimental data ; Mean squared error ; Composite number ; Machine learning ; Training set ; Artificial intelligence ; Leakage power ; Data mining ; Algorithm ; Statistics ; Mathematics ; Engineering ; Voltage Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Kapil Dev Mahato
Source: IOP Conference Series Materials Science and Engineering (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Absorption and emission maxima prediction of fluorescent organic materials using optimized neural network models Abstract The aim of the study is to predict the absorption and emission maxima from the given dataset, which consists of 3066 fluorescent organic materials. To fulfill this requirement, five optimized neural network models are employed. Out of them, the wide neural network (WNN) model outperformed the other models on validation as well as test results. The results were obtained on the basis of three evaluation parameters: mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R 2 ). According to the validation scores, the WNN was the best-predictive model with MAE-18.05 nm, RMSE-28.93 nm, and R 2 -89.55%, and MAE-29.58 nm, RMSE-42.62 nm, and R 2 -78.87% for absorption and emission maxima, respectively. On the other hand, on the basis of tested scores, the WNN was the best-predictive model with MAE-19.35 nm, RMSE-29.33 nm, and R 2 -92.14%, and MAE-29.17 nm, RMSE-41.87 nm, and R 2 -79.46% for absorption and emission maxima, respectively. The presented automated method does not require an extensive understanding of computer programming to estimate the absorption and emission maxima. The absorption and emission maxima may be predicted, which helps with the design of new fluorescent organic materials and their many uses in electronics, chemistry, materials science, medicine, and other areas. Maxima ; Fluorescence ; Artificial neural network ; Absorption (acoustics) ; Biological system ; Materials science ; Environmental science ; Artificial intelligence ; Computer science ; Optics ; Physics ; Composite material ; Biology Machine Learning in Materials Science ; Advanced Computing and Algorithms ; Computational Drug Discovery Methods