jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ajit Kumar Dash | Hariharan Swaminathan | Ethan Berger | Mainak Mondal | Touko Lehenkari | Pushp Raj Prasad | Kenji Watanabe | Takashi Taniguchi | Hannu‐Pekka Komsa | Akshay Singh
Source: arXiv (Cornell University) (preprint, 2022-10-10)
Field: Materials Science — Materials Chemistry

Controlled defect production in monolayer MoS2 via electron irradiation at ultralow accelerating voltages Control on spatial location and density of defects in 2D materials can be achieved using electron beam irradiation. Conversely, ultralow accelerating voltages (less than or equal to 5kV) are used to measure surface morphology, with no expected defect creation. We find clear signatures of defect creation in monolayer (ML) MoS2 at these voltages. Evolution of E' and A1' Raman modes with electron dose, and appearance of defect activated peaks indicate defect formation. To simulate Raman spectra of MoS2 at realistic defect distributions, while retaining density-functional theory accuracy, we combine machine-learning force fields for phonons and eigenmode projection approach for Raman tensors. Simulated spectra agree with experiments, with sulphur vacancies as suggested defects. We decouple defects, doping and carbonaceous contamination using control (hBN covered and encapsulated MoS2) samples. We observe cryogenic PL quenching and defect peaks, and find that carbonaceous contamination does not affect defect creation. These studies have applications in photonics and quantum emitters. Raman spectroscopy ; Monolayer ; Materials science ; Doping ; Phonon ; Molecular physics ; Electron ; Irradiation ; Quenching (fluorescence) ; Spectral line ; Optoelectronics ; Nanotechnology ; Optics ; Condensed matter physics ; Chemistry ; Fluorescence ; Physics 2D Materials and Applications ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor Quantum Structures and Devices
Authors: Gloire Rubambiza | Fernando Romero Galvan | Ryan Pavlick | Hakim Weatherspoon | Kaitlin M. Gold
Source:  (preprint, 2022-12-13)
Field: Chemistry — Analytical Chemistry

Towards Cloud-Native, Machine Learning Based Detection of Crop Disease with Imaging Spectroscopy Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual \$200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology \& Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASA’s AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy. Cloud computing ; Computer science ; Data science ; Homeland security ; Agriculture ; Stakeholder ; Plant disease ; Artificial intelligence ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Jialin Tang | Guotai Li | Qi Wang | Jiongzhi Zheng | Lin Cheng | Ruiqiang Guo
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Effect of Four-Phonon Scattering on Anisotropic Thermal Transport in Bulk Hexagonal Boron Nitride by Machine Learning Interatomic Potential Phonon ; Hexagonal boron nitride ; Condensed matter physics ; Anisotropy ; Materials science ; Scattering ; Thermal ; Hexagonal crystal system ; Boron nitride ; Phonon scattering ; Engineering physics ; Nanotechnology ; Crystallography ; Physics ; Optics ; Thermodynamics ; Chemistry ; Graphene Thermal properties of materials ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Lei Gan | Zheng Zhong | Hao Wu
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Probabilistic &lt;i&gt;S-N&lt;/i&gt; Curves for Steels Based Oncomposition and Processing Parameters Probabilistic logic ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Zakariya El‐Machachi | Mark R. Wilson | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2022-07-19)
Field: Materials Science — Materials Chemistry

Exploring the configurational space of amorphous graphene with machine-learned atomic energies Two-dimensionally extended amorphous carbon ("amorphous graphene") is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous graphene with an atomistic machine-learning (ML) model. We create structural models by introducing defects into ordered graphene through Monte-Carlo bond switching, defining acceptance criteria using the machine-learned local, atomic energies associated with a defect, as well as the nearest-neighbor (NN) environments. We find that physically meaningful structural models arise from ML atomic energies in this way, ranging from continuous random networks to paracrystalline structures. Our results show that ML atomic energies can be used to guide Monte-Carlo structural searches in principle, and that their predictions of local stability can be linked to short- and medium-range order in amorphous graphene. We expect that the former point will be relevant more generally to the study of amorphous materials, and that the latter has wider implications for the interpretation of ML potential models. Graphene ; Amorphous solid ; Monte Carlo method ; Materials science ; Statistical physics ; Space (punctuation) ; Amorphous carbon ; Interpretation (philosophy) ; Nanotechnology ; Chemical physics ; Physics ; Computer science ; Chemistry ; Crystallography ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; Ion-surface interactions and analysis
Authors: F. E. Abeng
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Electrochemical-Kinetics, Computational and Machine Learning Model for Prediction of Azo Dye as Eco-Friendly Corrosion Inhibitor for Stainless Steel in Chloride Solution Kinetics ; Chloride ; Electrochemistry ; Corrosion ; Corrosion inhibitor ; Environmentally friendly ; Materials science ; Metallurgy ; Chemistry ; Chemical engineering ; Engineering ; Physical chemistry ; Electrode ; Physics Corrosion Behavior and Inhibition
Authors: Mahtab Ghasemi Dogaheh | Hesmatollah Ebrahimi-Najafabadi | Fatemeh Yousefbeyk | Saeed Ghasemi
Source: SHILAP Revista de lepidopterología (article, 2022-05-01)
Field: Chemistry — Organic Chemistry

2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors Introduction: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor derivatives play an important role in the treatment of cancer. We aim to construct 2D-QSAR models using various chemometrics using 4-anilinoquinazoline-containing EGFR TKIs. In addition, the binding profile of these compounds was evaluated using a docking study.\nMaterials and Methods: In this study, 122 compounds of seven different structural categories with a 4-anilinoquinazoline scaffold were obtained from the pieces of literature. 2D-QSAR models were prepared using the linear method, including multiple linear regression (MLR), alongside non-linear methods, including artificial neural networks (ANN) and support vector machines (SVM). The validation of suggested 2D-QSAR models was performed using internal and external validation techniques. For molecular docking, three compounds, including compounds 32, 75, and 98, which had the highest pIC50, were used. Molecular docking was performed using AutoDock 4.2.\nResults: Selected descriptors indicated that atomic electronegativity, hydrogen bonding ability, lipophilicity, and molecular shape and volume were the factors affecting the activity of the compounds. Statistical criteria, related to interpretation and validation of the models, were in the appropriate range. The model obtained by the ANN method, with the lowest mean absolute error of 0.365 for both training and testing sets, was the best. The compound 75 as the most potent compound indicated binding energies of -8.33 kcal/mol.\nConclusion: Finally, it was found that the models could effectively predict the activity of the compounds. It was also discovered that the compounds were properly bound to the active site of the receptor. In addition, validation results showed that all processes were sufficiently valid. Quantitative structure–activity relationship ; Docking (animal) ; Tyrosine kinase ; Epidermal growth factor receptor ; Receptor tyrosine kinase ; Chemistry ; Stereochemistry ; Computational biology ; Pharmacology ; Biology ; Receptor ; Biochemistry ; Medicine Synthesis and Characterization of Heterocyclic Compounds ; Quinazolinone synthesis and applications ; Synthesis of Tetrazole Derivatives
Authors: S. He | Baicheng LIU | R. Feng | H. Lu | Y. Han | Lei-Lei Jia | Y. Zhang | Jenny Xiong | Ruixuan Yuan | Z. Wu
Source: IET conference proceedings. (article, 2022-07-22)
Field: Materials Science — Materials Chemistry

Database construction of insulator impulse test and prediction of flashover voltage based on machine learning The lightning impulse flashover characteristics of insulators are the basis of lightning protection analysis methods and external insulation design of transmission lines. The parameters such as the leader propagation model, the U50%, and flashover path are quite different. When the mechanism of long-gap discharge is not clear, it is difficult to obtain a universal insulator impulse flashover model. Therefore, this paper firstly analyses a large number of test data under lightning impulse voltage of insulators and air gaps, including different voltage levels, impulse waveforms, meteorological conditions, insulator materials and structures and other working conditions, and analyse structure of the data. Then, we build a database of insulator impulse flashover tests including all test conditions. Finally, machine learning and prediction are carried out for the flashover voltage of composite insulators with different voltage levels, a learning model is proposed, and it is verified that the learning error can be controlled within 4%. The research results provide a shared data platform and research ideas for the study of lightning impulse flashover characteristics and models of insulators under different conditions. It is expected to improve the accuracy of insulator lightning impulse flashover criteria on the basis of reducing lots of repetitive tests. Arc flash ; Insulator (electricity) ; Impulse (physics) ; Voltage ; Electric power transmission ; Waveform ; Engineering ; Test data ; Acoustics ; Electrical engineering ; Electronic engineering ; Computer science ; Physics High voltage insulation and dielectric phenomena
Authors: T. Honda
Source: NIPPON GOMU KYOKAISHI (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Development of Soft Blends Analyzer (SOBA) and Virtual Generation of bucky-paper Soft Blends Analyzer (SOBA) was developed to link machine learning and computer simulation with Python interface. Using the SOBA, Lenard-Jones Potential particles dynamics were done to generating two dimensional images of the filler dispersion for the education data of Depp Learning (DL). The DL was performed to learn the relationship between the image features and the filler density. The technique was applied to bucky-paper (BP:Non-woven film of CNT). The tiling images of SEM images with different four magnifications were used as DL training data, and accurate classification model was obtained. The model enabled to predict the physical properties of BP not used for learning by the sum of multiplying the probability distribution of classification by the properties of the educated BP. Next, the Generative Adversarial Network (GAN) training was performed with the same SEM images. Finally, GAN's morphing method have made it possible to virtually generate images of BP mixing different CNTs and established a framework of virtual experiment method for predicting its physical properties with the DL model. By the framework, the physical properties of more than 1,700 types of bucky-paper were estimated, which has contributed to reduce the time of material development. Python (programming language) ; Computer science ; Morphing ; Materials science ; Composite material ; Artificial intelligence ; Programming language Machine Learning in Materials Science
Authors: Stewart Pearson | Parinaz Naseri | Sean V. Hum
Source: arXiv (Cornell University) (preprint, 2022-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype. Surface (topology) ; Inverse ; Computer science ; Process (computing) ; Field (mathematics) ; Iterative and incremental development ; Inverse problem ; Translation (biology) ; Beam (structure) ; Algorithm ; Physics ; Optics ; Mathematics ; Geometry ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Yong Keun Koo | Sang won Kim | Eun seol Kim | Yong Woon Jung | Byeong Jae Park | Dong Hwa Kim | Seung ju Lee
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Development of a Secondary Model for the Growth of Salmonella Enterica in Food Applying Artificial Neural Network and Combase (Database) Salmonella enterica ; Artificial neural network ; Salmonella ; Artificial intelligence ; Computer science ; Database ; Biology ; Bacteria Spectroscopy and Chemometric Analyses
Authors: idit kosti | Tibor Nánási | Agustı́n Ruiz | Merçé Boada | Marta Marquié | Amanda Cano | Steven P. Braithwaite | Benoit Lehallier
Source: Alzheimer s & Dementia (article, 2022-12-01)
Field: Chemistry — Spectroscopy

Intricate connections between aging and Alzheimer’s diseases molecular trajectories revealed by plasma proteomics Abstract Background Aging is the greatest non‐genetic risk factor for many diseases including Alzheimer’s Disease (AD) and its mechanisms are thus increasingly recognized as potential therapeutic targets. Since the molecular relationships between normal aging and AD are still largely unknown, we took advantage of recent methodological developments to measure more than 7k proteins in plasma to identify common but also different molecular mechanisms involved in aging and AD. Method Recently, we developed new data‐driven bioinformatics approaches that uncovered marked non‐linear alterations in the human plasma proteome with age (Lehallier et al. Nature Medicine 2019). We applied these machine learning approaches to 271 patients at different stages of AD (61 subjects with Subjective Cognitive Decline: SCD, 106 with Mild Cognitive Impairment: MCI and 104 with AD) and compared these changes to those observed in a healthy aging cohort (370 subjects, 18‐69 years). Result The analysis of proteomics trajectories confirmed marked non‐linear alterations in the human plasma proteome with aging and uncovered the complex molecular choreography of AD. By analyzing trajectories of groups of proteins, we identified multiple clusters of proteins that are differentially affected during aging and AD. These clusters were enriched for various biological processes relevant for AD progression such as the complement and coagulation cascades, axon guidance and angiogenesis (q&lt;0.05) Conclusion Altogether, our results reveal the intricate connections between aging and AD at the molecular level. The integrated proteomics trajectories analysis performed in this study allows the identification of new targets possibly playing a key role in AD but also pinpoints when these proteins are deviating from their normal trajectories and thus when they should be targeted by new therapies. Proteomics ; Proteome ; Disease ; Cognitive decline ; Computational biology ; Alzheimer's disease ; Bioinformatics ; Neuroscience ; Dementia ; Biology ; Medicine ; Genetics ; Internal medicine ; Gene Advanced Proteomics Techniques and Applications ; Alzheimer's disease research and treatments ; Machine Learning in Bioinformatics
Authors: Inga Kuschnerus | Haotian Wen | Yee Yee Khine | Juanfang Ruan | Chun‐Jen Su | U‐Ser Jeng | George Opletal | Amanda S. Barnard | Eiji Ōsawa | Olga Shenderova | Vadym N. Mochalin | Ming Liu | Masahiro Nishikawa | Shery L. Y. Chang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Fabrication Process Independent and Robust Aggregation Formation of Detonation Nanodiamonds in Aqueous Media Detonation ; Fabrication ; Process (computing) ; Materials science ; Nanotechnology ; Aqueous solution ; Aqueous medium ; Explosive material ; Process engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Laser-Ablation Synthesis of Nanoparticles
Authors: Fawei Zheng
Source: arXiv (Cornell University) (preprint, 2022-05-13)
Field: Materials Science — Materials Chemistry

Magnetic skyrmion lattices in a novel two-dimensional twisted bilayer magnet Magnetic skyrmions are topologically protected spin swirling vertices, which are promising in device applications due to their particle-like nature and excellent controlability. Magnetic skyrmions have been extensively studied in a variety of materials and were proposed to exist in the extreme two-dimensional limit, i.e., in twisted bilayer CrI$_3$ (TBCI). Unfortunately, the magnetic states of TBCIs with small twist angles are disorderly distributed ferromagnetic (FM) and antiferromagnetic (AFM) domains in recent experiments, and thus the method to get rid of disorders in TBCIs is highly desirable. Here we use intralayer exchange interactions up to the third nearest neighbors without empirical parameters and very accurate interlayer exchange interactions to study the magnetic states of TBCIs. We propose the functions of interlayer exchange interactions obtained using first-principles calculations and stored in symmetry-adapted artificial neural networks. Based on them, the subsequent Landau-Lifshitz-Gillbert equation calculations explain the disorderly distributed FM-AFM domains in TBCIs with small twist angles and predict the orderly distributed skyrmions in TBCIs with large twist angles. This novel twisted two-dimensional bilayer magnet can be used to design memory devices, monochromatic spin wave generators and many kinds of skyrmion lattices. Skyrmion ; Antiferromagnetism ; Condensed matter physics ; Ferromagnetism ; Magnet ; Physics ; Bilayer ; Twist ; Spin (aerodynamics) ; Symmetry (geometry) ; Quantum mechanics ; Chemistry ; Geometry 2D Materials and Applications ; Magnetic properties of thin films ; Multiferroics and related materials
Authors: Milad Ramezankhani | Amir Nazemi | Apurva Narayan | Heinz Voggenreiter | Mehrtash Harandi | Rudolf Seethaler | Abbas S. Milani
Source: arXiv (Cornell University) (preprint, 2022-02-12)
Field: Materials Science — Materials Chemistry

A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accurate model requires extensive hyperparameter tuning, network architecture design, and costly and exhaustive training processes. To avoid such obstacles and make the training of physics-informed models less precarious, in this paper, a data-driven multi-fidelity physics-informed framework is proposed based on transfer learning principles. The framework incorporates the knowledge from low-fidelity auxiliary systems and limited labeled data from target actual system to significantly improve the performance of conventional physics-informed models. While minimizing the efforts of designing a complex task-specific network for the problem at hand, the proposed settings guide the physics-informed model towards a fast and efficient convergence to a global optimum. An adaptive weighting method is utilized to further enhance the optimization of the model composite loss function during the training process. A data-driven strategy is also introduced for maintaining high performance in subdomains with significant divergence between low- and high-fidelity behaviours. The heat transfer of composite materials undergoing a cure cycle is investigated as a case study to demonstrate the proposed framework's performance compared to conventional physics-informed models. Hyperparameter ; Weighting ; Fidelity ; Computer science ; Implementation ; Process (computing) ; Convergence (economics) ; Artificial neural network ; Divergence (linguistics) ; Machine learning ; Artificial intelligence ; Systems engineering ; Engineering ; Software engineering ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks