jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Desmarita Leni | Arwizet Karudin | Muhammad Rabiu Abbas | Jai Kumar Sharma | Adriansyah Adriansyah
Source: EUREKA Physics and Engineering (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit The use of Exploratory Data Analysis (EDA) and machine learning in material science has rapidly advanced in recent years. EDA enables researchers to thoroughly explore and analyze material datasets, while machine learning allows for the development of predictive models capable of understanding complex patterns within the data. This study aims to develop an optimization tool to enhance the analysis of tensile strength in stainless steel by leveraging integrated data exploration and machine learning approaches within the Streamlit framework. The developed tool consists of four main features: data visualization, correlation analysis, 3D visualization, and machine learning. The developed machine learning model has 14 input variables, including chemical elements and heat treatment temperatures. In this research, the machine learning features comprise three models: Decision Tree, Random Forest, and Artificial Neural Network. The research findings indicate that the optimization tool can automatically display stainless steel tensile strength data using available pandas profiling in the visualization feature. The correlation feature can illustrate the relationship between chemical elements and heat treatment temperatures concerning stainless steel tensile strength. The 3D visualization feature can be utilized to identify optimal values of chemical elements and heat treatment temperatures according to desired tensile strength. Meanwhile, the machine learning feature can accurately predict stainless steel tensile strength based on chemical composition and heat treatment temperatures. This is evident from the performance evaluation metrics of the Random Forest model, which achieved MAE of 10.36, RMSE of 14.44, and R-squared of 0.97 Ultimate tensile strength ; Visualization ; Random forest ; Machine learning ; Artificial intelligence ; Artificial neural network ; Decision tree ; Feature (linguistics) ; Computer science ; Data visualization ; Materials science ; Data mining ; Pattern recognition (psychology) ; Composite material Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Advanced machining processes and optimization
Authors: Pengfei Fan | Chao Ma | Lei Zhang | Jiakang Li | Ziqi Su | Hui Li | Dayong Xu | Jingsong Du | Jian-wei Ma
Source:  (article, 2024-07-18)
Field: Chemistry — Analytical Chemistry

Research on detection method of moldy tobacco leaf raw materials based on hyperspectral and machine learning Using hyperspectral imaging technology and machine learning methods to classify and identify whether tobacco leaves have undergone mold contamination. Visible-near-infrared hyperspectral imaging technology was employed, and various preprocessing techniques such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), and convolutional smoothing (SG) were applied to preprocess the spectral data. Feature wavelength selection was carried out through successive projections algorithm (SPA) and principal component analysis loadings (PCA loadings). Classification models were built using random forest (RF), Softmax, and support vector machine (SVM).Among the preprocessing methods, SNV was identified as the optimal spectral preprocessing technique. The RF model established through feature wavelength selection using SPA demonstrated the best performance, with training and testing accuracies reaching 98.82% and 98.64%, respectively. The combination of hyperspectral imaging technology with the SPA-RF model proved to be effective in accurately classifying and identifying mold contamination in tobacco leaves. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Elliot Perviz | Antonio Cammarata | Tomáš Polcar
Source: Physical Review Materials (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Design rules for doped transition metal dichalcogenides heterostructures Transition metal dichalcogenides (TMDs) are the base materials for diverse technological devices such as photovoltaics, lithium-ion batteries, hydrogen-evolution catalysis, transistors, photodetectors, DNA detection, memory devices, and nanotribological systems. Their flexible ${\mathrm{MX}}_{2}$ stoichiometry enables the fine-tuning of their properties via cation or anion substitution, thus allowing heterostructures with diverse functionalities to be engineered at the nanoscale. In this respect, we perform first-principles simulations to individuate possible novel structures derived from monolayer and bilayer ${\mathrm{MoS}}_{2}$ and ${\mathrm{WS}}_{2}$ alloyed with various metal and nonmetal dopants at different concentrations. We evaluate the relative stability and characterize the mechanisms responsible for their formation through electronic descriptors. Specifically, we identify bond covalency and orbital polarization as collective indicators for favorable electronic distributions, while the electronic structure of the isolated atom may be used for the selection of suitable dopants. The proposed methodology constitutes a general protocol, which can easily be extended to van der Waals heterostructures beyond those based on TMDs. Finally, the methodology can be used to help machine learning algorithms screen material databases for high-throughput discovery of new van der Waals--based alloys. Materials science ; Heterojunction ; Doping ; Transition metal ; Engineering physics ; Metal ; Condensed matter physics ; Nanotechnology ; Optoelectronics ; Metallurgy ; Catalysis 2D Materials and Applications ; Perovskite Materials and Applications ; Nanocluster Synthesis and Applications
Authors: Fabian L. Thiemann | Niamh O’Neill | Venkat Kapil | Angelos Michaelides | Christoph Schran
Source: arXiv (Cornell University) (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

Introduction to machine learning potentials for atomistic simulations Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials (HD-NNPs) and Gaussian approximation potential (GAP), to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples - further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Ion-surface interactions and analysis
Authors: Gianmarco Terrones | Shih-Peng Huang | Matt Rivera | Shuwen Yue | Alondra Hernandez | Heather J. Kulik
Source: ChemRxiv (preprint, 2024-04-23)
Field: Chemistry — Inorganic Chemistry

WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models Metal-organic frameworks (MOFs) are porous materials with applications in gas separations and catalysis, but a lack of water stability often limits their practical use given the ubiquity of water in air and the environment. Consequently, it is useful to predict whether a MOF is water-stable before investing time and resources into synthesis. Existing heuristics for designing water-stable MOFs lack generality and artificially limit the diversity of explored chemistry due to narrowly defined criteria. Machine learning (ML) models offer the promise to improve generality of predictions but require diverse experimental MOF stability data to be trained. In an improvement on previous efforts, we enlarge the available training data for MOF water stability prediction by over 400%, adding 911 MOFs with water stability labels assigned through semi-automated manuscript analysis to curate the new data set WS24. The additional data is shown to improve ML model performance (test ROC-AUC > 0.8) over diverse chemistry for the prediction of both water stability and stability in harsher acidic conditions. We illustrate how the expanded data set and models can be used with previously developed activation stability models to carry out genetic algorithms to quickly screen ~10,000 MOFs from a space of hundreds of thousands for candidates with multivariate stability (i.e., for activation, in water, and in acid). Model analysis and genetic algorithm results uncover metal- and geometry-specific design rules for robust MOFs. The data set and ML models developed in this work, which we disseminate through an easy-to-use web interface, are expected to contribute toward the accelerated discovery of novel, water-stable MOFs for applications such as direct air gas capture and water treatment. Stability (learning theory) ; Data set ; Set (abstract data type) ; Data mining ; Computer science ; Environmental science ; Artificial intelligence ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Advanced Nanomaterials in Catalysis
Authors: Danpeng Cheng | Wuxin Sha | Yaqing Guo | Shun Tang | Jun Zhong | Jinqiao Du | Jie Tian | Yuan‐Cheng Cao
Source: MRS Bulletin (article, 2024-12-10)
Field: Materials Science — Surfaces, Coatings and Films

A real-time deep learning model to narrow the gap between atomic scanning transmission electron microscopy and theory calculations: Recognition, reconstruction, and simulation Transmission electron microscopy ; Scanning transmission electron microscopy ; Materials science ; Transmission (telecommunications) ; Computer science ; Nanotechnology ; Artificial intelligence ; Telecommunications Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Mouyang Cheng | Chenyan Wang | Chunling Qin | Yuxiang Zhang | Qingyuan Zhang | Li Han | Ji Chen
Source: arXiv (Cornell University) (preprint, 2024-10-04)
Field: Materials Science — Materials Chemistry

Predicting macroscopic properties of amorphous monolayer carbon via pair correlation function Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder. In this work, we develop SPRamNet, a neural network based machine-learning pipeline that effectively predicts structure-property relationship of amorphous material via global descriptors. Applying SPRamNet on the recently discovered amorphous monolayer carbon, we successfully predict the thermal and electronic properties. More importantly, we reveal that a short range of pair correlation function can readily encode sufficiently rich information of the structure of amorphous material. Utilizing powerful machine learning architectures, the encoded information can be decoded to reconstruct macroscopic properties involving many-body and long-range interactions. Establishing this hidden relationship offers a unified description of the degree of disorder and eliminates the heavy burden of measuring atomic structure, opening a new avenue in studying amorphous materials. Monolayer ; Correlation function (quantum field theory) ; Function (biology) ; Materials science ; Correlation ; Amorphous solid ; Carbon fibers ; Statistical physics ; Chemical physics ; Nanotechnology ; Chemistry ; Mathematics ; Physics ; Crystallography ; Composite material ; Geometry ; Optoelectronics ; Biology X-ray Diffraction in Crystallography ; Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics
Authors: Christian Devereux | Yoona Yang | Carles Martí | Judit Zádor | Michael Eldred | Habib N. Najm
Source: International Journal of Chemical Kinetics (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Force training neural network potential energy surface models Abstract Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However, such potentials are only as good as the data they are trained on, and building a comprehensive training set can be a costly process. Therefore, it is important to extract as much information from training data as possible without further increasing the computational cost. One way to accomplish this is by training on molecular forces in addition to energies. This allows for three additional labels per atom within the molecule. Here we develop a neural network potential energy surface for studying a hydrogen transfer reaction between two isomers of . We show that, for a much smaller training set, force training not only improves the accuracy of the model compared to only training on energies, but also provides more accurate and smoother first and second derivatives that are crucial to run dynamics and extract vibrational frequencies in the context of transition‐state theory. We also demonstrate the importance of choosing the proper force to energy weight ratio for the loss function to minimize the model test error. Chemistry ; Artificial neural network ; Training (meteorology) ; Potential energy surface ; Surface (topology) ; Artificial intelligence ; Computer science ; Physics ; Molecule Machine Learning in Materials Science ; Nuclear Materials and Properties ; Computational Drug Discovery Methods
Authors: Yogita Sawant | Lalita Admuthe
Source:  (article, 2024-06-24)
Field: Materials Science — Polymers and Plastics

Forecasting and Optimizing Nonwoven Filter Performance via Machine Learning Nonwoven materials are everywhere in various uses, such as clothing, insulation, and filtering. The industry aims to predict material properties based on production and structural parameters, which is a difficult task. This paper discusses a machine learning-based approach to predict nonwoven fabric filtration efficiency using a suitable machine learning model. Five different machine learning algorithms, comprising of Linear Regression(LR) Decision Tree (DT), Support Vector Machine Regressions (SVMR), Artificial Neural Network(ANN) and Hyperparameter Optimised Neural Network (HONN) were utilized for forecasting the performance of a nonwoven fabric filter as a function of fabric thickness, porosity and fiber diameter. The models were trained using experimental real-time fabric filtration evaluation data. All the models were judged according to performance matrices such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R_ squared and (Mean absolute Percentage Error (MAPE). It was found that a simple multilayer perceptron neural network with optimized parameters performs better than other ML models. Computer science ; Filter (signal processing) ; Artificial intelligence ; Computer vision Textile materials and evaluations
Authors: Darian Smalley | Stephanie D. Lough | Luke N. Holtzman | Madisen Holbrook | James Hone | Katayun Barmak | Masa Ishigami
Source: APL Machine Learning (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Determining the density and spatial descriptors of atomic scale defects of 2H–WSe2 with ensemble deep learning We have demonstrated atomic-scale defect characterization in scanning tunneling microscopy images of single crystal tungsten diselenide using an ensemble of U-Net-like convolutional neural networks. Coordinates, counts, densities, and spatial extents were determined from almost 16 000 defect detections, leading to the rapid identification of defect types and their densities. Our results show that analysis aided by machine learning can be used to rapidly determine the quality of transition metal dichalcogenides and provide much needed quantitative input, which may improve the synthesis process. Scale (ratio) ; Atomic units ; Statistical physics ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Materials science ; Physics ; Geography ; Cartography ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Shannon R. Petersen | David Kohan Marzagão | Georgina L. Gregory | Yichen Huang | David A. Clifton | Charlotte K. Williams | Clive R. Siviour
Source: Angewandte Chemie (article, 2024-11-29)
Field: Chemistry — Organic Chemistry

Property Prediction of Bio‐Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods Abstract Increasing the diversity of bio‐based polymers is needed to address the combined problems of plastic pollution and greenhouse gas emissions. The magnitude of the problems necessitates rapid discovery of new materials; however, identification of appropriate chemistries maybe slow using current iterative methods. Machine learning (ML) methods could significantly expedite new material discovery and property identification. Here, PolyAGM, a ML algorithm using graph kernel methods, is introduced and used to predict the properties of block copolymers and identify the responsible structural ‘motifs’. It applies a “fingerprinting” method to convert Graph representations of polymers into numerical vectors. The Graphs explicitly encode the entire copolymer of atoms and bonds such that the sequencing of chemical features and polymer chain length are included, alongside relevant stereochemical information. PolyAGM gives predictions for both thermal and mechanical properties that are in good agreement with experimental measurements. This work focuses on predicting the properties of bio‐derived ABA‐block polymer thermoplastic elastomers, but the general fingerprinting technique of PolyAGM should be relevant to other application fields. Copolymer ; Thermoplastic elastomer ; Elastomer ; Materials science ; Polymer science ; Block (permutation group theory) ; Thermoplastic ; Property (philosophy) ; Graph ; Kernel (algebra) ; Polymer chemistry ; Computer science ; Composite material ; Polymer ; Mathematics ; Combinatorics ; Theoretical computer science Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Orthopaedic implants and arthroplasty
Authors: Hongyang Dong | Simon D. M. Jacques | Keith T. Butler | Olof Gutowski | Ann‐Christin Dippel | M. von Zimmerman | Andrew M. Beale | Antonis Vamvakeros
Source: npj Computational Materials (article, 2024-09-02)
Field: Materials Science — Materials Chemistry

Obtaining parallax-free X-ray powder diffraction computed tomography data with a self-supervised neural network Abstract In this study, we introduce a method designed to eliminate parallax artefacts present in X-ray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as lattice parameters and crystallite sizes. Our approach integrates a 3D artificial neural network architecture with a forward projector that accounts for the experimental geometry and sample thickness. It is a self-supervised tomographic volume reconstruction approach designed to be chemistry-agnostic, eliminating the need for prior knowledge of the sample’s chemical composition. We showcase the efficacy of this method through its application on both simulated and experimental X-ray powder diffraction tomography data, acquired from a phantom sample and an NMC532 cylindrical lithium-ion battery. Parallax ; Computed tomography ; Artificial neural network ; Tomography ; X-ray ; Powder diffraction ; Computer science ; Artificial intelligence ; Computer vision ; Optics ; Physics ; Medicine ; Radiology ; Nuclear magnetic resonance X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Marcel Ruth | Tobias Gensch | Peter R. Schreiner
Source: Angewandte Chemie (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ<i>G</i><sup>≠</sup> versus Enantiomeric Excess Abstract With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess ( ee ) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔ G ≠ ), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔ G ≠ , escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists. Representation (politics) ; Computer science ; Biochemical engineering ; Chemistry ; Machine learning ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods