jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thibault Lechien | Enrique Dehaerne | Bappaditya Dey | Víctor Blanco | Halder, Sandip | Stefan De Gendt | Wannes Meert
Source: arXiv (Cornell University) (review, 2023-08-16)
Field: Materials Science — Surfaces, Coatings and Films

Automated Semiconductor Defect Inspection in Scanning Electron Microscope Images: a Systematic Review A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical failures and wafer-yield limitations. As nodes and patterns get smaller, even high-resolution imaging techniques such as Scanning Electron Microscopy (SEM) produce noisy images due to operating close to sensitivity levels and due to varying physical properties of different underlayers or resist materials. This inherent noise is one of the main challenges for defect inspection. One promising approach is the use of machine learning algorithms, which can be trained to accurately classify and locate defects in semiconductor samples. Recently, convolutional neural networks have proved to be particularly useful in this regard. This systematic review provides a comprehensive overview of the state of automated semiconductor defect inspection on SEM images, including the most recent innovations and developments. 38 publications were selected on this topic, indexed in IEEE Xplore and SPIE databases. For each of these, the application, methodology, dataset, results, limitations and future work were summarized. A comprehensive overview and analysis of their methods is provided. Finally, promising avenues for future work in the field of SEM-based defect inspection are suggested. Wafer ; Computer science ; Convolutional neural network ; Process (computing) ; Semiconductor device fabrication ; Noise (video) ; Resist ; Semiconductor ; Semiconductor device ; Sensitivity (control systems) ; Reliability engineering ; Artificial intelligence ; Materials science ; Nanotechnology ; Electronic engineering ; Engineering ; Optoelectronics ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: Gaurav Deshmukh | Noah J. Wichrowski | Nikolaos Evangelou | Pushkar Ghanekar | Siddharth Deshpande | Ioannis G. Kevrekidis | Jeffrey Greeley
Source: ChemRxiv (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Active Learning of Ternary Alloy Structures and Energies High-throughput screening of catalysts using first-principles methods, such as density functional theory (DFT), has traditionally been limited by the large, complex, and multidimensional nature of the associated materials spaces. However, machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of these spaces in a data-efficient manner, typically through active learning-based workflows. In this work, we combine such an active learning scheme with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). Specifically, we train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and utilize the model to make and improve predictions on ternary structures. To do so, we perform reduced optimization over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the manifold learning scheme known as Diffusion Maps. Inspired by statistical mechanics, we derive and apply a dropout-informed acquisition function to select ensembles from which to sample additional structures. During each iteration of our active learning scheme, a representative number of crystals that minimize the acquisition function is selected, their energies are computed with DFT, and our dGCN model is retrained. We demonstrate that both of our reduced optimization techniques can be used to improve predictions of the formation free energy, the target property that determines HEA stability, in the ternary alloy space with a significantly reduced number of costly DFT calculations compared to a high-fidelity model. However, the manner in which these two disparate schemes converge to the target property differs: the physics-based scheme appears akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both active learning schemes can be extended further to incorporate greater number of elements, surface structures, and adsorbate motifs. Ternary operation ; Computer science ; Materials science ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Pushpanjali Bhat | Tanmay Shukla | Nithesh Naik | Daniel Korir | Princy Randhawa | Antony V. Samrot | S Ramya | S A Salmataj
Source: Engineered Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Deep Neural Network as a Tool to Classify and Identify the 316L and AZ31BMg Metal Surface Morphology: An Empirical Study Identifying the severity of corrosion is crucial in physical and biological sciences.Developing a highly accurate deep learning model capable of classifying corrosion severity across a wide metal surface, even with limited training data, represents a significant advancement over traditional investigation methods.Unlike traditional approaches such as electrochemical measurements that assess only provided inspection areas, deep learning models can classify the entire metal surface.In the realm of biomaterials, this corrosion identification approach aids researchers in selecting appropriate materials for body implants, reducing the impact of corroded metal reactions within the implanted body.This research advocates for an objective and automated examination of metal surfaces, employing convolutional neural networks to classify corrosion intensity based on scanning electron microscope images.Despite the limited number of samples from electrochemical laboratories, the deep learning model provides valuable insights across the entire metal plate surface, effectively distinguishing between different corrosion states.Electrochemical measurements were implemented to see the corrosion such as EIS, potentiodynamic polarisation techniques. Corrosion ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Identification (biology) ; Materials science ; Metal ; Electrochemistry ; Scanning electron microscope ; Artificial neural network ; Computer science ; Metallurgy ; Pattern recognition (psychology) ; Electrode ; Composite material ; Chemistry Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Satoshi Noguchi | Syuji Aihara | Junya Inoue
Source: ISIJ International (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Microstructure Estimation by Combining Deep Learning and Phase Transformation Model In material design, the establishment of process–structure–property relationship is crucial for analyzing and controlling material microstructures. For the establishment of process–structure–property relationship, a central problem is the analysis, characterization, and control of microstructures, since microstructures are highly sensitive to material processing and critically affect material's properties. Therefore, accurately estimating the morphology of material microstructures plays a significant role in understanding the process–structure–property relationship. In this paper, we propose a deep-learning framework for estimating material microstructures under specific process conditions. The framework utilizes two deep learning networks: vector quantized variational autoencoder (VQVAE) and pixel convolutional neural network (PixelCNN). The framework can predict material microstructures from the transformation behavior given by some physical models. In this sense, the framework is consistent with the physical knowledge accumulated in the field of material science. Importantly, our study demonstrates qualitative and quantitative evidence that incorporating physical models enhances the accuracy of microstructure prediction by deep learning models. These results highlight the importance of appropriately integrating field-specific knowledge when applying data-driven frameworks to materials design. Consequently, our results provide a basis for integrating data-driven methods with the accumulated knowledge in the field. This integration holds great potential for advancing material design through deep learning. Deep learning ; Artificial intelligence ; Microstructure ; Process (computing) ; Computer science ; Characterization (materials science) ; Property (philosophy) ; Transformation (genetics) ; Autoencoder ; Field (mathematics) ; Materials science ; Machine learning ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; X-ray Diffraction in Crystallography
Authors: Yanhao Deng | Yangyang Wang | Ke Xu | Yanming Wang
Source: Journal of Chemical Theory and Computation (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Lightweight Extendable Stacking Framework for Structure Classification in Atomistic Simulations Identifying an atom's local crystal structure is one crucial step in many atomistic simulation analyses. However, many traditional methods are available to only a few limited types of structures, and their performance often relies on manually determined parameters, which may lead to poor classification results in complex material systems. Machine learning models can enhance accuracy and generalizability, but they typically require large amounts of data and computation. This issue could be more severe for deep-learning-based frameworks, especially when confronted with unfamiliar crystal structures. To address this challenge, we propose a lightweight and extendable stacked structure (LESS) classifier, which adopts bond orientational order parameters as features and assembles several efficient machine learning methods as based models. The LESS classifier can recognize a variety of crystal structures, e.g., amorphous, mono, and binary structures, with over 98.8% accuracy on our validation data set, outperforming many current methods even including some deep-learning methods. Our model can also conduct probabilistic classification that aids in the interpretation of atomic structures in complicated environments such as heterogeneous interfaces. Furthermore, when exposed to a completely unknown crystal structure, the LESS framework can efficiently incorporate this new knowledge with generative sampled data from the current model. Overall, our model exhibits great potential as an accurate and flexible atomic structure identification tool featuring high efficiency in both learning and retraining. Computer science ; Artificial intelligence ; Machine learning ; Crystal structure prediction ; Stacking ; Classifier (UML) ; Generalizability theory ; Overfitting ; Crystal structure ; Artificial neural network ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Allen Jonathan Román | Shiyi Qin | Julio C. Rodríguez | Ví­ctor M. Zavala | Tim A. Osswald
Source: AIP conference proceedings (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Viscoelastic behavior prediction of natural rubber blends via machine learning and its application for reverse engineering materials Viscoelasticity ; Natural rubber ; Materials science ; Composite material ; Computer science Polymer Nanocomposites and Properties ; Infrastructure Maintenance and Monitoring ; Asphalt Pavement Performance Evaluation
Authors: Megumi Higashi | Hidekazu Ikeno
Source: MATERIALS TRANSACTIONS (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Extraction of Local Structure Information from X-ray Absorption Near-Edge Structure: A Machine Learning Approach In this work, we constructed machine learning models to predict structural descriptors that numerically represent the atomic structures in three dimensions from x-ray absorption near-edge structure (XANES) spectra. The neural network models that predict radial distribution functions (RDF) and orbital-field matrix (OFM), a descriptor that deals with the anisotropy of the local structure, the valence electron number of the ligand, and orbital information, were constructed. We used more than 120,000 O K-edge XAS spectra data from the Materials Project database as the training data set. We successfully constructed models that roughly predicted RDFs with 74% of the test data. Furthermore, the model that predicted OFM also captured an overview of OFM in 97% of the test data. These results demonstrate that the atomic structural information can be directly extracted from XANES spectra using neural network models. XANES ; Artificial neural network ; X-ray absorption spectroscopy ; Spectral line ; Materials science ; Valence (chemistry) ; Test set ; Electronic structure ; Absorption spectroscopy ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Physics ; Optics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Jiyong Yang | Renhai Shi | Alan A. Luo
Source: Metals (article, 2023-04-03)
Field: Materials Science — Biomaterials

Unveiling the Alloying-Processing-Microstructure Correlations in High-Formability Sheet Magnesium Alloys Designing magnesium sheet alloys for room temperature (RT) forming is a challenge due to the limited deformation modes offered by the hexagonal close-packed crystal structure of magnesium. To overcome this challenge for lightweight applications, critical understanding of alloying-processing–microstructure relationship in magnesium alloys is needed. In this work, machine learning (ML) algorithms have been used to fundamentally understand the alloying-processing–microstructure correlations for RT formability in magnesium alloys. Three databases built from 135 data collected from the literature were trained using 10 commonly used machine learning models. The accuracy of the model is obviously improved with the increase in the number of features. The ML results were analyzed using advanced SHapley Additive exPlanations (SHAP) technique, and the formability descriptors are ranked as follows: (1) microstructure: texture intensity &gt; grain size; (2) annealing processing: time &gt; temperature; and (3) alloying elements: Ca &gt; Zn &gt; Al &gt; Mn &gt; Gd &gt; Ce &gt; Y &gt; Ag &gt; Zr &gt; Si &gt; Sc &gt; Li &gt; Cu &gt; Nd. Overall, the texture intensity, annealing time and alloying Ca are the most important factors which can be used as a guide for high-formability sheet magnesium alloy design. Formability ; Microstructure ; Materials science ; Magnesium ; Metallurgy ; Magnesium alloy ; Annealing (glass) ; Grain size ; Thermomechanical processing ; Texture (cosmology) ; Alloy ; Artificial intelligence ; Computer science Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Honglei Yuan | Shengqiang Yang | Han Peng | Mingya Sun | Chao Zhou
Source: Toxicology Mechanisms and Methods (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia <i>via</i> its action potential function. Isoflurane ; Drug ; Mechanism (biology) ; Gene ; Pharmacology ; Anesthetic ; Medicine ; Biology ; Anesthesia ; Computational biology ; Genetics Machine Learning in Materials Science ; Anesthesia and Neurotoxicity Research ; Metabolomics and Mass Spectrometry Studies
Authors: Frank Tristram | Nicole Jung | Patrick Hodapp | Rasmus R. Schröder | Christof Wöll | Stefan Bräse
Source: Advanced Functional Materials (article, 2023-08-27)
Field: Materials Science — Materials Chemistry

The Impact of Digitalized Data Management on Materials Systems Workflows Abstract The basic modules for materials research are systems for the design, synthesis, preparation, analysis, and application of materials and materials systems. To be efficient and produce findable, accessible, interoperable, and reusable (FAIR) data, state‐of‐the‐art materials research needs to consider the integration of research data management (RDM) workflows and, in the end, the implementation of process automation concepts for all parts of the main modules. Here, the state‐of‐the‐art methods of RDM in academia are described and a perspective on the future of digitalized molecular material systems workflows is given. The different elements of an integrated research data management strategy are described, and examples of automated processes are depicted. As such, the use of electronic lab notebooks for comprehensive documentation, the use of data‐integration and data‐conversion strategies, and the establishment of two platforms that enable the automated synthesis of chemical components for materials and the analysis of materials by electron microscopy, are highlighted. Two examples of beneficial effects of successful RDM strategies are presented, showing a sophisticated tool for data prediction based on machine learning and options for creating community‐driven databases by extracting and re‐using data from different scientific projects. Workflow ; RDM ; Interoperability ; Computer science ; Documentation ; Data management ; Automation ; Process (computing) ; Systems engineering ; Data integration ; Data science ; Software engineering ; World Wide Web ; Database ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Scientific Computing and Data Management
Authors: Hasan M. Sayeed | Sterling G. Baird | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-10)
Field: Materials Science — Materials Chemistry

Structure feature vectors derived from Robocrystallographer text descriptions of crystal structures using word embeddings Capturing structure-property relationships of materials for property prediction using machine learning requires the representation or featurization of the structural aspects of materials at different levels, including atomic, crystal, and microscales. While crystal structure-based modeling techniques are effective for materials informatics, many materials datasets do not have complete structural information. On the other hand, when it comes to discovering novel materials, structural information of the chemical compounds is not known beforehand. These two cases make the application of structure-based learning techniques limited. Tools for the automated generation of structural features are limited in materials science. Indeed, most structural descriptions of materials are done via human analysis and then stored as text entries in scientific documents. One way to extract a structure-based feature vector from this corpus of knowledge would be to leverage natural language processing to create word embeddings trained on these structural descriptions. This approach could encode the information about the kind of structures any individual element of the periodic table forms which can be leveraged to tackle both the situation mentioned above: lack of structural details in a dataset, structural information for novel materials. In this work, we created word embeddings by training models on automatically generated text descriptions of crystal structure from Robocrystallographer and assess the utility of these structure-based feature vectors in predicting materials properties by comparing the performance against mat2vec and one-hot encoded features. Computer science ; Leverage (statistics) ; ENCODE ; Natural language processing ; Artificial intelligence ; Feature (linguistics) ; Word (group theory) ; Property (philosophy) ; Feature vector ; Representation (politics) ; Information retrieval ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zheyu Hu | Yaolin Guo | Zhen Liu | Diwei Shi | Yifan Li | Yu Hu | Moran Bu | Kan Luo | Jian He | Chong Wang | Shiyu Du
Source: Journal of Chemical Information and Modeling (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO<sub>2</sub>. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions. Ternary operation ; Encoding (memory) ; Embedding ; Autoencoder ; Function (biology) ; Computer science ; Artificial neural network ; Biological system ; Feature (linguistics) ; Energy (signal processing) ; Statistical physics ; Chemical physics ; Algorithm ; Materials science ; Chemistry ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods