jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Darren C. Pagan | Calvin R. Pash | Austin R. Benson | Matthew Kasemer
Source: npj Computational Materials (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data Abstract Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in low solvus high-refractory (LSHR) Ni Superalloy and Ti 7 wt%Al (Ti-7Al) are predicted as example face-centered cubic and hexagonal closed packed alloys, respectively. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method (CEFEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored through comparisons to traditional mean-field theory predictions, reserved full-field CEFEM data, and measured far-field HEDM data. The effects of elastic anisotropy on GNN model performance and outlooks for the extension of the framework are discussed. Materials science ; Anisotropy ; Microscale chemistry ; Crystallite ; Microstructure ; Solvus ; Artificial neural network ; Grain boundary ; Superalloy ; Artificial intelligence ; Computer science ; Metallurgy ; Mathematics ; Optics ; Physics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels
Authors: Shuang Han | Giovanni Barcaro | Alessandro Fortunelli | Steen Lysgaard | Tejs Vegge | Heine Anton Hansen
Source: npj Computational Materials (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential Abstract The structural stability of nanoalloys is a challenging research subject due to the complexity of size, shape, composition, and chemical ordering. The genetic algorithm is a popular global optimization method that can efficiently search for the ground-state nanoalloy structure. However, the algorithm suffers from three significant limitations: the efficiency and accuracy of the energy evaluator and the algorithm’s efficiency. Here we describe the construction of a neural network potential intended for rapid and accurate energy predictions of Pt-Ni nanoalloys of various sizes, shapes, and compositions. We further introduce a symmetry-constrained genetic algorithm that significantly improves the efficiency and viability of the algorithm for realistic size nanoalloys. The combination of the two allows us to explore the space of homotops and compositions of Pt-Ni nanoalloys consisting of up to 4033 atoms and quantitatively report the interplay of shape, size, and composition on the dominant chemical ordering patterns. Genetic algorithm ; Stability (learning theory) ; Algorithm ; Symmetry (geometry) ; Artificial neural network ; Computer science ; Space (punctuation) ; Energy (signal processing) ; Biological system ; Materials science ; Artificial intelligence ; Mathematics ; Machine learning ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Chalcogenide Semiconductor Thin Films
Authors: Noah Hoffmann | Tiago F. T. Cerqueira | Jonathan Schmidt | Miguel A. L. Marques
Source: npj Computational Materials (article, 2022-07-13)
Field: Materials Science — Materials Chemistry

Superconductivity in antiperovskites Abstract We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ 3 where X and Z are metals, and Y is H, B, C, N, O, and P. Our starting point are electron–phonon calculations for 397 materials performed with density-functional perturbation theory. While 43% of the materials are dynamically unstable, we discovered 16 compounds close to thermodynamic stability and with T c higher than 5 K. Using these results to train interpretable machine-learning models, leads us to predict a further 57 (thermodynamically unstable) materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe 3 . Furthermore, the models give us an understanding of the mechanism of superconductivity in antiperovskites. The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials and is easily extendable to other families of compounds or physical properties. Superconductivity ; Perturbation theory (quantum mechanics) ; Phonon ; Density functional theory ; Condensed matter physics ; Stability (learning theory) ; Chemical stability ; Materials science ; Physics ; Statistical physics ; Thermodynamics ; Computer science ; Quantum mechanics ; Machine learning Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Sang‐Gyu Kim | Seung-Hyeok Shin | Byoungchul Hwang
Source: Journal of Materials Research and Technology (article, 2022-06-14)
Field: Materials Science — Metals and Alloys

Machine learning approach for prediction of hydrogen environment embrittlement in austenitic steels This study introduces a machine learning approach to predict the effect of alloying elements and test conditions on the hydrogen environment embrittlement (HEE) index of austenitic steels for the first time. The correlation between input features and the HEE index was analyzed with Pearson's correlation coefficient (PCC) and Maximum Information Coefficient (MIC) algorithms. The correlation analysis results identified Ni and Mo as dominant features influencing the HEE index of austenitic steels. Based on the analysis results, the performance of the four representative machine learning models as a function of the number of top-ranked features was evaluated: random forest (RF), linear regression (LR), Bayesian ridge (BR), and support vector machine (SVM). Regardless of the type and the number of top-ranking features, the RF model had the highest accuracy among various models. The machine learning-based approach is expected to be useful in designing new steels having mechanical properties required for hydrogen applications. Materials science ; Random forest ; Austenite ; Support vector machine ; Correlation coefficient ; Machine learning ; Artificial intelligence ; Hydrogen embrittlement ; Ranking (information retrieval) ; Metallurgy ; Pattern recognition (psychology) ; Computer science ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms
Authors: Jianpan Huang | Zilin Chen | Se Weon Park | Joseph H. C. Lai | Kannie W. Y. Chan
Source: Pharmaceutics (review, 2022-02-20)
Field: Materials Science — Materials Chemistry

Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future. Molecular imaging ; Magnetic resonance imaging ; Glioma ; Brain tumor ; Grading (engineering) ; Isocitrate dehydrogenase ; Chemistry ; Medicine ; Cancer research ; Pathology ; Radiology ; Biology ; In vivo ; Biochemistry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Magnetism in coordination complexes
Authors: Matthew J. Olszta | Derek Hopkins | K Fiedler | Marjolein Oostrom | Sarah Akers | Steven R. Spurgeon
Source: Microscopy and Microanalysis (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited <i>a priori</i> knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies. Analytics ; Computer science ; Data analysis ; Controller (irrigation) ; Automation ; A priori and a posteriori ; Data science ; Artificial intelligence ; Cornerstone ; Throughput ; Machine learning ; Nanotechnology ; Data mining ; Engineering ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Bohayra Mortazavi | Fazel Shojaei | Xiaoying Zhuang
Source: Materials Today Nano (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

A novel two-dimensional C36 fullerene network; an isotropic, auxetic semiconductor with low thermal conductivity and remarkable stiffness Materials science ; Auxetics ; Fullerene ; Thermal conductivity ; Isotropy ; Anisotropy ; Bulk modulus ; Condensed matter physics ; Phonon ; Poisson's ratio ; Semiconductor ; Nanotechnology ; Composite material ; Optoelectronics ; Physics ; Optics ; Poisson distribution Graphene research and applications ; Fullerene Chemistry and Applications ; Thermal properties of materials
Authors: Shaik Amer Ahmed | Shaik Rajiya | M. A. Samee | Shaik Kareem Ahmmad | Kaleem Ahmed Jaleeli
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2022-01-18)
Field: Materials Science — Ceramics and Composites

Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques Bismuth ; Raman spectroscopy ; Artificial neural network ; Doping ; Analytical Chemistry (journal) ; Boron ; Fast Fourier transform ; Glass transition ; Linear regression Glass properties and applications ; Phase-change materials and chalcogenides ; Metallurgical and Alloy Processes
Authors: Madhusudan G. Lanjewar | Pranay P. Morajkar | Jivan S. Parab
Source: Multimedia Tools and Applications (article, 2022-03-03)
Field: Chemistry — Analytical Chemistry

Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud Adulterant ; Convolutional neural network ; Tartrazine ; Computer science ; Cloud computing ; Curcuma ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Traditional medicine ; Chemistry Dye analysis and toxicity ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Jiachen Sun | Jun Chang | Yubin Wei | Zhifeng Zhang | Shan Lin | Fupeng Wang | Qinduan Zhang
Source: Sensors and Actuators B Chemical (article, 2022-09-17)
Field: Chemistry — Spectroscopy

Dual gas sensor with innovative signal analysis based on neural network Separator (oil production) ; Artificial neural network ; Methane ; Carbon monoxide ; Interference (communication) ; Spectral line ; Autoencoder ; Biological system ; Computer science ; Chemistry ; Analytical Chemistry (journal) ; Artificial intelligence ; Physics ; Telecommunications ; Chromatography Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Gui-Qin Liang | Jian Zhang
Source: Computational Materials Science (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

A machine learning model for screening thermodynamic stable lead-free halide double perovskites Halide ; Stability (learning theory) ; Perovskite (structure) ; Chemical stability ; Convex hull ; Density functional theory ; Periodic table ; Computer science ; Materials science ; Chemistry ; Thermodynamics ; Algorithm ; Applied mathematics ; Machine learning ; Regular polygon ; Mathematics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Reza Maleki | Mohsen Asadnia | Amir Razmjou
Source: Advanced Intelligent Systems (article, 2022-08-04)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Based Material Discovery for Clean Energy Future Artificial intelligence (AI)‐assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low‐carbon economy that is material‐intensive. AI‐assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI‐assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This perspective paves the way for a better understanding of the future of AI applications in clean energies. Clean energy ; Perspective (graphical) ; Clean technology ; Computer science ; Nanotechnology ; Biochemical engineering ; Process engineering ; Engineering ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Shu Zhang | Jinbo Pang | Yufen Li | Feng Yang | Thomas Gemming | Kai Wang | Xiao Wang | Songang Peng | Xiaoyan Liu | Bin Chang | Hong Liu | Weijia Zhou | Gianaurelio Cuniberti | Mark H. Rümmeli
Source: Nano Research (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Emerging Internet of Things driven carbon nanotubes-based devices Abstract Carbon nanotubes (CNTs) have attracted great attentions in the field of electronics, sensors, healthcare, and energy conversion. Such emerging applications have driven the carbon nanotube research in a rapid fashion. Indeed, the structure control over CNTs has inspired an intensive research vortex due to the high promises in electronic and optical device applications. Here, this in-depth review is anticipated to provide insights into the controllable synthesis and applications of high-quality CNTs. First, the general synthesis and post-purification of CNTs are briefly discussed. Then, the state-of-the-art electronic device applications are discussed, including field-effect transistors, gas sensors, DNA biosensors, and pressure gauges. Besides, the optical sensors are delivered based on the photoluminescence. In addition, energy applications of CNTs are discussed such as thermoelectric energy generators. Eventually, future opportunities are proposed for the Internet of Things (IoT) oriented sensors, data processing, and artificial intelligence. Carbon nanotube ; Nanotechnology ; Materials science ; Electronics ; Electrical engineering ; Engineering Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Molecular Communication and Nanonetworks
Authors: Kyann De Smit | Tom Wieme | Yoshi W. Marien | Paul Van Steenberge | Dagmar D’hooge | Mariya Edeleva
Source: Reaction Chemistry & Engineering (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Multi-scale reactive extrusion modelling approaches to design polymer synthesis, modification and mechanical recycling Reactive extrusion (REX) is an important processing and production technique with applications in the field of polymer synthesis, modification and recycling. Reactive extrusion ; Extrusion ; Surface modification ; Polymer ; Materials science ; Process engineering ; Chemical engineering ; Engineering ; Composite material Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Polymer composites and self-healing
Authors: Yubo Liu | Xiaohong Chen | Yuhang Gao | Deng‐Guang Yu | Ping Liu
Source: Journal of Nanobiotechnology (article, 2022-05-28)
Field: Materials Science — Biomaterials

Elaborate design of shell component for manipulating the sustained release behavior from core–shell nanofibres Here, the functional F2-F4 fibres had been successfully produced by using the advanced modified triaxial electrospinning nanotechnology with different polymer matrices. The simple strategy in this work has remarkable potential to manipulate hydrophilicity and sustained release of drug carriers, meantime it can also enrich the preparation approaches of functional nanomaterials. Shell (structure) ; Component (thermodynamics) ; Nanotechnology ; Core (optical fiber) ; Materials science ; Chemistry ; Physics ; Composite material Electrospun Nanofibers in Biomedical Applications ; Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites
Authors: Andy S. Anker | Emil T. S. Kjær | Mikkel Juelsholt | Troels Lindahl Christiansen | Susanne Linn Skjærvø | Mads R. V. Jørgensen | I. Kantor | Daniel Risskov Sørensen | Simon J. L. Billinge | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: npj Computational Materials (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning Abstract Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our M achine L earning based Mot if Ex tractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML. Function (biology) ; Computer science ; Distribution (mathematics) ; Pair distribution function ; Nanostructure ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Materials science ; Nanotechnology ; Mathematics ; Biology ; Mathematical analysis ; Evolutionary biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yan Liu | Rui Wang | Zhigang Wang | Da Li | Tian Cui
Source: Nature Communications (article, 2022-01-20)
Field: Chemistry — Physical and Theoretical Chemistry

Formation of twelve-fold iodine coordination at high pressure Halogen compounds have been studied widely due to their unique hypercoordinated and hypervalent features. Generally, in halogen compounds, the maximal coordination number of halogens is smaller than eight. Here, based on the particle swarm optimization method and first-principles calculations, we report an exotically icosahedral cage-like hypercoordinated IN<sub>6</sub> compound composed of N<sub>6</sub> rings and an unusual iodine-nitrogen covalent bond network. To the best of our knowledge, this is the first halogen compound showing twelve-fold coordination of halogen. High pressure and the presence of N<sub>6</sub> rings reduce the energy level of the 5d orbitals of iodine, making them part of the valence orbital. Highly symmetrical covalent bonding networks contribute to the formation of twelve-fold iodine hypercoordination. Moreover, our theoretical analysis suggests that a halogen element with a lower atomic number has a weaker propensity for valence expansion in halogen nitrides. Halogen ; Hypervalent molecule ; Covalent bond ; Halogen bond ; Iodine ; Valence (chemistry) ; Icosahedral symmetry ; Atomic orbital Crystallography and molecular interactions ; Inorganic Fluorides and Related Compounds ; Boron Compounds in Chemistry
Authors: Jason K. Kawasaki | Shouvik Chatterjee | P. C. Canfield | Guest Editors
Source: MRS Bulletin (article, 2022-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Full and half-Heusler compounds Spintronics ; Materials science ; Intermetallic ; Superconductivity ; Ternary operation ; Heusler compound ; Condensed matter physics ; Multiferroics ; Superlattice ; Trigonal crystal system ; Thermoelectric materials ; Skyrmion ; Electronic structure ; Crystal structure ; Nanotechnology ; Ferromagnetism ; Crystallography ; Physics ; Optoelectronics ; Chemistry ; Computer science ; Metallurgy Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; 2D Materials and Applications