jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. А. Коптелов | Adam J. Thompson | Stephen R. Hallett | Bassam El Said
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

A Deep Learning Approach for Predicting the Architecture of 3d Textile Fabrics Textile ; Deep learning ; Architecture ; Artificial intelligence ; Computer science ; Engineering ; Visual arts ; Art ; History ; Archaeology Textile materials and evaluations ; Color perception and design ; 3D Shape Modeling and Analysis
Authors: Yifan Chen | Qingxin Yang | Yongjian Li | Hao Zhang | Changgeng Zhang
Source:  (article, 2023-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Deep Convolutional Neural Networks for Electromagnetic Field Estimation of Dry-type Transformer This paper aims to estimate the electromagnetic field distribution in a simplified transformer through two-dimensional (2-D) finite element analysis. We create two datasets, namely the original dataset (OD) to simulate grayscale images and the new dataset (ND) which incorporates distinct physical properties of materials. These datasets are helpful to investigate the impact of different data types on deep learning. In order to enhance the accuracy of estimation, we compare the performances of two convolutional neural network (CNN) architectures: U-net and its improved version, U-Resnet (which incorporates residual blocks from ResNet). Additionally, we introduce a specialized loss function, Add-RMSE, which is better suited for dense regression problems, thus improving the prediction accuracy. The effectiveness of our proposed method is validated through test experiments, where we analyze the estimation results obtained. Convolutional neural network ; Computer science ; Residual neural network ; Residual ; Transformer ; Artificial intelligence ; Pattern recognition (psychology) ; Mean squared error ; Deep learning ; Artificial neural network ; Algorithm ; Machine learning ; Mathematics ; Statistics ; Engineering Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses
Authors: Francesca Tavazza | Kamal Choudhary | Brian DeCost
Source: arXiv (Cornell University) (preprint, 2023-10-19)
Field: Materials Science — Materials Chemistry

Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances. While confidence intervals are commonly reported for such ML models, prediction intervals, i.e., the uncertainty on each prediction, are not as frequently available. Here, we investigate three easy-to-implement approaches to determine such individual uncertainty, comparing them across ten ML quantities spanning energetics, mechanical, electronic, optical, and spectral properties. Specifically, we focused on the Quantile approach, the direct machine learning of the prediction intervals and Ensemble methods. Quantile ; Computer science ; Machine learning ; Artificial intelligence ; Ensemble learning ; Confidence interval ; Uncertainty quantification ; Econometrics ; Statistics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Jerome Quenum | Iryna V. Zenyuk | Daniela Ushizima
Source: arXiv (Cornell University) (preprint, 2023-02-09)
Field: Materials Science — Surfaces, Coatings and Films

Lithium Metal Battery Quality Control via Transformer-CNN Segmentation Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as through several qualitatively comparative visualizations. Segmentation ; Computer science ; Artificial intelligence ; Artificial neural network ; Transformer ; Battery (electricity) ; Pattern recognition (psychology) ; Materials science ; Voltage ; Engineering ; Electrical engineering Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging ; Machine Learning in Materials Science
Authors: Xin Wei | Peng Ding | Lin Shen | Yuejie Ai | Zhanhui Lu
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Analyzing of Metal Organic Frameworks Performance in Gas Adsorption Using Machine Learning Techniques: A Gbrt Model Based on Small Training Dataset Metal-organic framework ; Training (meteorology) ; Adsorption ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications
Authors: Felix Mbuga | Kathy Lam | Wendy Lee
Source:  (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Artifact Detection for Long-Read Sequencing Data A major goal of cancer diagnostics is to be able to detect the presence of cancer in the earliest stages when it is most curable. Doing this using minimally invasive methods such as liquid biopsy which analyzes tumor material shed into bodily fluids is particularly attractive. Liquid biopsy methods that employ next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) face a challenge due to the low signal-to-noise ratio, where sequencing artifacts introduce noise at a similar level to ctDNA. To address this, we propose utilizing machine learning (ML) and deep learning (DL) techniques, leveraging Genome in a Bottle (GIAB) truth sets and data from the National Center for Biotechnology Information's Sequence Read Archive (NCBI SRA) database, to robustly identify sequencing artifacts. This approach holds promise for enhancing the accuracy and reliability of liquid biopsy-based cancer diagnostics. Artifact (error) ; Computer science ; Artificial intelligence ; Machine learning ; Computer vision ; Pattern recognition (psychology) Machine Learning in Materials Science
Authors: Keijin Nakaguro | Yuki Mitsuta | Shiro Koseki | Tomohiro Oshiyama | Toshio Asada
Source: Research Square (preprint, 2023-06-01)
Field: Materials Science — Materials Chemistry

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase using Random Forest Technique and Computer Simulation Method Abstract The molecular design system of small organic molecules was developed to realize high hole mobility in the amorphous phase by using the random forest technique and the computer simulation method. Since there was no accessible datasets of the hole mobilities in the amorphous phase, we have constructed a datasets of experimental hole mobilities for 321 organic molecules with calculated MO energies to utilize machine learning techniques for molecular generation procedures. Random forest (RF) was applied to the machine learning technique using the constructed datasets. The optimized RF with 69 structure-based descriptors exhibited correlation coefficients of 0.885 and 0.764 for the training and testing datasets, respectively. RF confirmed that molecules with two triphenylamine (TPA) and/or carbazole groups are likely to display high hole mobilities according to the topological polar surface area (TPSA). The modified ChemTS to use optimized RF was adapted to design molecules with high hole mobilities. The SC model that uses ab initio molecular orbital (MO) calculations and the Marcus theory was adapted to ensure the predicted mobilities. The generated molecules were verified to have high calculated hole mobility (the order of \({10}^{-2} \text{c}{\text{m}}^{2}/\text{V}\text{s})\) owing to the high transfer integral and low reorganization energy by the successive conduction (SC) model. The datasets and programs used to design molecules in this manuscript were publicity released on GitHub. Mobilities ; Triphenylamine ; Amorphous solid ; Ab initio ; Phase (matter) ; Molecule ; Materials science ; Electron mobility ; Organic molecules ; Chemical physics ; Chemistry ; Physics ; Optoelectronics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Kangshu Li | Xiaocang Han | Yanhui Hong | Meng Yuan | Xiang Chen | Junxian Li | Jing‐Yang You | Lin Yao | Wenchao Hu | Zhiyi Xia | Guolin Ke | Linfeng Zhang | Jin Zhang | Xiaoxu Zhao
Source: arXiv (Cornell University) (preprint, 2023-11-25)
Field: Materials Science — Surfaces, Coatings and Films

Single-image based deep learning for precise atomic defects identification Defect engineering has been profoundly employed to confer desirable functionality to materials that pristine lattices inherently lack. Although single atomic-resolution scanning transmission electron microscopy (STEM) images are widely accessible for defect engineering, harnessing atomic-scale images containing various defects through traditional image analysis methods is hindered by random noise and human bias. Yet the rise of deep learning (DL) offering an alternative approach, its widespread application is primarily restricted by the need for large amounts of training data with labeled ground truth. In this study, we propose a two-stage method to address the problems of high annotation cost and image noise in the detection of atomic defects in monolayer 2D materials. In the first stage, to tackle the issue of data scarcity, we employ a two-state transformation network based on U-GAT-IT for adding realistic noise to simulated images with pre-located ground truth labels, thereby infinitely expanding the training dataset. In the second stage, atomic defects in monolayer 2D materials are effectively detected with high accuracy using U-Net models trained with the data generated in the first stage, avoiding random noise and human bias issues. In both stages, we utilize segmented unit-cell-level images to simplify the model's task and enhance its accuracy. Our results demonstrate that not only sulfur vacancies, we are also able to visualize oxygen dopants in monolayer MoS2, which are usually overwhelmed by random background noise. As the training was based on a few segmented unit-cell-level realistic images, this method can be readily extended to other 2D materials. Therefore, our results outline novel ways to train the model with minimized datasets, offering great opportunities to fully exploit the power of machine learning (ML) applicable to a broad materials science community. Computer science ; Ground truth ; Noise (video) ; Artificial intelligence ; Atomic units ; Deep learning ; Scarcity ; Visualization ; Image (mathematics) ; Pattern recognition (psychology) ; Machine learning ; Physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Paulina Portales Picazo | Alexander Gray | Navid Zobeiry
Source:  (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

A Novel Framework for Accelerated Characterization of Pyrolysis Kinetics of High-Temperature Composites Using Theory-Guided Probabilistic Machine Learning Abstract Establishing a relationship between processing conditions during the manufacturing process and end-part properties is essential for the efficient fabrication of advanced composites. For fiber-reinforced polymer composites, this manufacturing process typically includes multiple complex stages such as lay-up, curing, high-temperature pyrolysis, several resin backfill steps followed by pyrolytic process repeats, and graphitization. In particular, the high-temperature pyrolysis step is critical, because the process temperature cycle significantly affects the degradation reactions and phase transformations undergone by the polymer resin. Therefore, it is crucial to identify the ideal parameters for optimal performance. Characterizing pyrolysis kinetics to achieve this commonly requires an exhaustive testing campaign to study the effect of different temperature cycles on final properties. This involves thousands of possible combinations of multi-heating ramps and holds, resulting in an intractable search space. Additionally, established literature succeeds in characterizing the pyrolysis kinetics for simple dynamic temperature cycles, but fails to account for the effect of more complex cycles. This paper aims to address the above challenges by introducing a novel probabilistic machine learning-based framework for accelerated characterization of pyrolysis kinetics using theory-based transformations of limited and noisy experimental data. The degradation reactions and fraction of volatile components of a polymer-based composite are studied using thermogravimetric analysis techniques, and results are analyzed using theory-guided Gaussian Processes (GPs) to accurately characterize the pyrolysis kinetics of complex temperature cycles. The framework is then used to identify optimal parameters for achieving the desired yield. Using this approach, the experimental effort is considerably reduced while developing an accurate pyrolysis kinetics model for the material and establishing optimal processing parameters. Materials science ; Pyrolysis ; Curing (chemistry) ; Thermogravimetric analysis ; Kinetics ; Polymer ; Pyrolytic carbon ; Probabilistic logic ; Gaussian process ; Composite material ; Process (computing) ; Process engineering ; Computer science ; Gaussian ; Chemical engineering ; Artificial intelligence ; Engineering ; Chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Polymer crystallization and properties
Authors: Shoieb Ahmed Chowdhury | M. F. N. Taufique | Jing Wang | Marissa Masden | Madison Wenzlick | Ram Devanathan | Alan Schemer-Kohrn | Keerti Kappagantula
Source: arXiv (Cornell University) (preprint, 2023-05-12)
Field: Materials Science — Metals and Alloys

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization. Grain boundary ; Electron backscatter diffraction ; Segmentation ; Artificial intelligence ; Materials science ; Convolutional neural network ; Pixel ; Computer science ; Microstructure ; Pattern recognition (psychology) ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Sanggyu Chong | Federico Grasselli | Chiheb Ben Mahmoud | Joe D. Morrow | Volker L. Deringer | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2023-06-27)
Field: Materials Science — Materials Chemistry

Robustness of Local Predictions in Atomistic Machine Learning Models Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and also allow for the identification and post-hoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted. Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the aspects of model training, particularly the composition of training dataset, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models. Interpretability ; Robustness (evolution) ; Transferability ; Computer science ; Scaling ; Machine learning ; Metric (unit) ; Artificial intelligence ; Mathematics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Alan M. Lewis | Paolo Lazzaroni | Mariana Rossi
Source: arXiv (Cornell University) (preprint, 2023-04-18)
Field: Materials Science — Materials Chemistry

Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations We present a local and transferable machine learning approach capable of predicting the real-space density response of both molecules and periodic systems to external homogeneous electric fields. The new method, SALTER, builds on the Symmetry-Adapted Gaussian Process Regression SALTED framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10% with barely more than 100 training structures. Derived quantities, such as polarizability tensors and even Raman spectra further derived from these tensors show a good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. This method is thus capable of learning vector fields in a chemical context and serves as a landmark for further developments. Polarizability ; Context (archaeology) ; Electric field ; Tensor (intrinsic definition) ; Gaussian ; Symmetry (geometry) ; Statistical physics ; Raman spectroscopy ; Molecule ; Chemistry ; Chemical physics ; Biological system ; Molecular physics ; Physics ; Computational chemistry ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Spectroscopy and Quantum Chemical Studies
Authors: Y. Shiozaki | Hyeon‐Gu Jeon | Haruo Ihori
Source:  (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

Classification of Electrical Treeing Images by Machine Learning of Supervised and Unsupervised Learning In recent years, machine learning has been widely applied in the field of materials such as material informatics. There are several examples its application to the development of insulating materials. The machine learning is good at classification, judgment, and prediction. However, there are few examples of machine learning applied to various discharge experimental data in insulating materials. Therefore, we would like to predict the progress of electrical tree in silicone gel by using our data. In this paper, general machine learning was applied to consecutive images of treeing phenomena. These images can be classified roughly 2 categories. Also, the two types of supervised and unsupervised learning were applied. As a result, classification was possible in both learning. In addition, we found that insight the possibility of predicting the progress of electrical tree. Electrical treeing ; Artificial intelligence ; Machine learning ; Unsupervised learning ; Computer science ; Decision tree ; Field (mathematics) ; Tree (set theory) ; Supervised learning ; Artificial neural network ; Partial discharge ; Engineering ; Mathematics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Geophysical Methods and Applications