jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hairong Lei | Cho Teh | Liangjiang Yu | Gino Fu | Lingling Pu | Wei Fang
Source: Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV (article, 2021-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Denoising sample-limited SEM images without clean data Over the past few years, noise2noise, noise2void, noise2self, and unsupervised deep-learning (DL) denoising techniques have achieved great success, particularly in scenarios where ground truth data is not available or is difficult to obtain. For semiconductor SEM images, ground truth or clean target images with lower noise levels can be obtained by averaging hundreds of frames at the same wafer location, but it is expensive and can result in physical damage to the wafer. This paper’s scope is to denoise SEM images without clean target images and with limited image counts. Inspired by noise2noise, we proposed an additive noise algorithm and DL U-net. We achieved good denoising performance using a limited number of noisy SEM images, without the clean ground truth images. We proposed the “denoise2next” and “denoise2best”. We compared generative adversarial network(GAN) generated images and Additive noise images for data augmentation. This paper further quantified the impact of image noise level, pattern diversity, and continuous (aka transfer) learning. The data sets used in the work include both line/space and logic pattern. Sample (material) ; Noise reduction ; Computer science ; Clean-up ; Image denoising ; Artificial intelligence ; Pattern recognition (psychology) ; Computer vision ; Chemistry ; Chromatography Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: S. Wane | Tran Quang Hung | A. E. Cabrera | Lionel Salmon | Gábor Molnár | V. Huard | Azzedine Bousseksou
Source:  (article, 2021-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Smart Sensing of Vital-Signs: Co-Design of Tunable Quantum-Spin Crossover Materials with Secure Photonics and RF Front-End-Module In this paper, a new sensor technology solution is proposed based on the co-design of tunable quantum-spin crossover material with smart photonics and RF Front-End-Module (FEM) integrating innovative Thermal-Body harvesting. The quantum-spin crossover material, co-integrated with the Photonics and RF Front-End-Module, hosts smart Low-Power Signal-Processor (LPSP) embedding Artificial-Intelligence (AI) and Machine-Learning (ML) algorithms for accurate sensing of vital-signs information. The smart signal-processing capabilities are brought near the sensor to allow for real-time Correlation-based data analytics and decision-making process with secure wireless data transfer. The performances of the proposed technology solution are demonstrated by building a smart wearable Bracelet device for real-time sensing of Correlated body temperature and oxygen saturation levels. Porting of the Photonics and RF FEM into 22nm Fully Depleted Silicon On Insulator (FD-SOI) platform using Application Specific Integrated Circuit-based System on a Chip (ASIC-SoC) technology solution will foster a broad range of applications in mobile communications with smart devices and systems. Photonics ; Computer science ; Signal processing ; Radio frequency ; Silicon on insulator ; Electronic engineering ; Electrical engineering ; Quantum computer ; Wearable computer ; Front and back ends ; Embedded system ; Engineering ; Computer hardware ; Materials science ; Optoelectronics ; Quantum ; Physics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies
Authors: Cheng‐Wei Ju | Ethan French | Nadav Geva | Alexander W. Kohn | Zhou Lin
Source: ChemRxiv (preprint, 2021-08-02)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Machine Learning for Range-Separation Parameters High-throughput virtual materials and drug discovery based on density functional theory has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffers catastrophically from self-interaction error until the optimally tuned range-separated hybrid (OT-RSH) exchange--correlation functionals were developed. The accurate but expensive fi rst-principles OT-RSH transitions from a short-range (semi-)local functional to a long-range Hartree--Fock exchange at a distance characterized by the inverse of a molecule-specifi c, non-empirically-determined range-separation parameter (ω). In the present study, we proposed a promising stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic con figurations. We trained ML-ωPBE, the first functional in our series, using a database of 1,970 organic semiconducting molecules with sufficient structural diversity, and assessed its accuracy and efficiency using another 1,956 molecules. Compared with the first-principles OT-ωPBE, our ML-ωPBE reached a mean absolute error of 0:00504a0^{-1} for the optimal value of ω, reduced the computational cost for the test set by 2.66 orders of magnitude, and achieved a comparable predictive power in various optical properties. Range (aeronautics) ; Density functional theory ; Molecule ; Inverse ; Hybrid functional ; Statistical physics ; Series (stratigraphy) ; Ensemble learning ; Materials science ; Test set ; Power (physics) ; Computer science ; Physics ; Computational physics ; Biological system ; Algorithm ; Computational chemistry ; Chemistry ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Mengying Sun | Jing Xing | Huijun Wang | Bin Chen | Jiayu Zhou
Source: arXiv (Cornell University) (article, 2021-06-05)
Field: Materials Science — Materials Chemistry

MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge Recent years have seen a rapid growth of utilizing graph neural networks (GNNs) in the biomedical domain for tackling drug-related problems. However, like any other deep architectures, GNNs are data hungry. While requiring labels in real world is often expensive, pretraining GNNs in an unsupervised manner has been actively explored. Among them, graph contrastive learning, by maximizing the mutual information between paired graph augmentations, has been shown to be effective on various downstream tasks. However, the current graph contrastive learning framework has two limitations. First, the augmentations are designed for general graphs and thus may not be suitable or powerful enough for certain domains. Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks. Therefore, in this paper, we study graph contrastive learning in the context of biomedical domain, where molecular graphs are present. We propose a novel framework called MoCL, which utilizes domain knowledge at both local- and global-level to assist representation learning. The local-level domain knowledge guides the augmentation process such that variation is introduced without changing graph semantics. The global-level knowledge encodes the similarity information between graphs in the entire dataset and helps to learn representations with richer semantics. The entire model is learned through a double contrast objective. We evaluate MoCL on various molecular datasets under both linear and semi-supervised settings and results show that MoCL achieves state-of-the-art performance. Computer science ; Artificial intelligence ; Graph ; Domain knowledge ; Feature learning ; Semantics (computer science) ; Theoretical computer science ; Knowledge graph ; Natural language processing ; Machine learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Abdelkader Elaggoune | Tahar Seghier | Boubakeur Zegnini | Mohammed Belkheiri
Source: Transactions on Electrical and Electronic Materials (article, 2021-04-10)
Field: Materials Science — Materials Chemistry

Partial Discharge Activity Diagnosis in Electrical Cable Terminations Using Neural Networks Partial discharge ; Materials science ; Artificial neural network ; Electrical engineering ; Acoustics ; Voltage ; Artificial intelligence ; Computer science ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Paul Francoeur | David Ryan Koes
Source: Journal of Chemical Information and Modeling (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

Correction to “SolTranNet—A Machine Learning Tool for Fast Aqueous Solubility Prediction” ADVERTISEMENT RETURN TO ISSUEPREVErratumNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to "SolTranNet—A Machine Learning Tool for Fast Aqueous Solubility Prediction"Paul G. Francoeur*Paul G. FrancoeurDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States*E-mail: [email protected]More by Paul G. Francoeurhttps://orcid.org/0000-0002-1440-567X and David R. KoesDavid R. KoesDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United StatesMore by David R. Koeshttps://orcid.org/0000-0002-6892-6614Cite this: J. Chem. Inf. Model. 2021, 61, 8, 4120–4123Publication Date (Web):July 27, 2021Publication History Published online27 July 2021Published inissue 23 August 2021https://pubs.acs.org/doi/10.1021/acs.jcim.1c00806https://doi.org/10.1021/acs.jcim.1c00806correctionACS PublicationsCopyright © 2021 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1403Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-Alertsclose Get e-Alerts Solubility ; Computer science ; Machine learning ; Artificial intelligence ; Aqueous solution ; Chemistry ; Organic chemistry Crystallization and Solubility Studies
Authors: Nazli Mert-Ozupek | Levent Çavaş
Source: Analytical Biochemistry (article, 2021-05-19)
Field: Chemistry — Spectroscopy

Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis Stevia rebaudiana ; Rebaudioside A ; Chromatography ; High-performance liquid chromatography ; Chemistry ; Gradient elution ; Acetonitrile ; Glycoside ; Elution ; Steviol ; Stevioside ; Food science ; Stereochemistry Analytical Chemistry and Chromatography ; Biochemical Analysis and Sensing Techniques ; Advanced Chemical Sensor Technologies
Authors: Andrey V. Bukhtiyarov | Igor P. Prosvirin | Maxim A. Panafidin | Alexey Fedorov | Alexander Klyushin | Axel Knop‐Gericke | Yan V. Zubavichus | V. I. Bukhtiyarov
Source: Nanomaterials (article, 2021-12-04)
Field: Materials Science — Materials Chemistry

Near-Ambient Pressure XPS and MS Study of CO Oxidation over Model Pd-Au/HOPG Catalysts: The Effect of the Metal Ratio In this study, the dependence of the catalytic activity of highly oriented pyrolytic graphite (HOPG)-supported bimetallic Pd-Au catalysts towards the CO oxidation based on the Pd/Au atomic ratio was investigated. The activities of two model catalysts differing from each other in the initial Pd/Au atomic ratios appeared as distinctly different in terms of their ignition temperatures. More specifically, the PdAu-2 sample with a lower Pd/Au surface ratio (~0.75) was already active at temperatures less than 150 °C, while the PdAu-1 sample with a higher Pd/Au surface ratio (~1.0) became active only at temperatures above 200 °C. NAP XPS revealed that the exposure of the catalysts to a reaction mixture at RT induces the palladium surface segregation accompanied by an enrichment of the near-surface regions of the two-component Pd-Au alloy nanoparticles with Pd due to adsorption of CO on palladium atoms. The segregation extent depends on the initial Pd/Au surface ratio. The difference in activity between these two catalysts is determined by the presence or higher concentration of specific active Pd sites on the surface of bimetallic particles, i.e., by the ensemble effect. Upon cooling the sample down to room temperature, the reverse redistribution of the atomic composition within near-surface regions occurs, which switches the catalyst back into inactive state. This observation strongly suggests that the optimum active sites emerge under reaction conditions exclusively, involving both high temperature and a reactive atmosphere. Bimetallic strip ; Catalysis ; X-ray photoelectron spectroscopy ; Palladium ; Metal ; Materials science ; Adsorption ; Highly oriented pyrolytic graphite ; Analytical Chemistry (journal) ; Chemistry ; Chemical engineering ; Physical chemistry ; Graphite ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Electronic and Structural Properties of Oxides
Authors: M.A.M. El-Mansy | Medhat Ibrahim | Huda Soliman | Sara Atef | I Klimovich | A Zhilenkov | L uznetsova | L Frolova | O Yamilova | S Troyanov | K Lyssenko | P Troshin | J.-S Li | Y.-W Huang | Y.-C Lin | F.-H Chen | W.-C Chen | C.-C Chueh | X Xiao | B Shao | Y Lu | Q Cao | C Xia | F Chen | W Zhou | N Yutronkie | B Lessard | J Brusso | Y Wu | D Huo | G Chen | A Yan | M Shaik | M Nadiveedhi | M Gundluru | A Narreddy | K Thathireddy | R Ramakrishna | S Cirandur | A Basha | F Khan | S Muthu | M Raja | M El-Mansy | Ft-Ir | M El-Nahass | N Khusayfan | E El-Menyawy | M Ismail | I Yahia | M Kamel | A El-Barbary | M Ibrahim | A Asiri | B Wagner | G Morsy | H Mohamed | M Abd-Alrazk | H Soliman | K Eid | H Ali | S Atef | H Hassnin | H Duan | C Zhu | Y Li | H Zhang | S Zhang | F Xiao | S Amirkhanian | S Sardon | N Rahman | M Karim | N Zahid | N Salleh | A Almuqrin
Source: Biointerface Research in Applied Chemistry (article, 2021-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

FT-IR, Molecular Structure and Nonlinear Optical Properties of 2-(pyranoquinolin-4-yl)malononitrile (PQMN): A DFT Approach A combined experimental and theoretical study for Fourier transform infrared spectra for 2-(pyranoquinolin-4-yl)malononitrile (PQMN) compound has been made. In advance, we investigate many physical characteristics based on DFT/B3LYP using 6-311G(d,p) basis set such as optimum structure, vibrational frequencies, thermo-chemistry, overall dipole moment, HOMO/LUMO Bandgap, nuclear repulsive energy and ionization energies, electronic affinity and chemical potential, global electrophilicity index, global hardness and finally softness (ζ). Also, we studied the non-linear optical (NLO) properties of PQMN. Results emphasize both degeneracy and diamagnetic properties of PQMN. PQMN Frontiers’ molecular orbitals (FMOs) split into two distinguished alpha (spin ↑) and beta (spin ↓) states with the same energy 3.7 eV, although its singlet spins state. Moreover, the calculated dipole moment (DM) value (13.3 Debye) for PQMN explains the mystery behind its reactive tendency with the nearby media. PQMN is a unique model for a degenerate diamagnetic semiconductor that can be easily used for optoelectronic manufactured devices such as solar cells and spintronic devices. Hyperpolarizability ; Band gap ; HOMO/LUMO ; Diamagnetism ; Dipole ; Molecular orbital ; Malononitrile ; Ionization energy ; Chemistry ; Materials science ; Computational chemistry ; Molecular physics ; Physics ; Polarizability ; Molecule ; Optoelectronics ; Ionization ; Quantum mechanics ; Ion ; Organic chemistry Nonlinear Optical Materials Research ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Aliaksei Mazheika | Yang‐Gang Wang | Rosendo Valero | Francesc Viñes | Francesc Illas | Luca M. Ghiringhelli | Sergey V. Levchenko | Matthias Scheffler
Source: Research Square (preprint, 2021-08-26)
Field: Materials Science — Materials Chemistry

Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides Abstract Using subgroup discovery, an artificial intelligence (AI) approach that identifies statistically exceptional subgroups in a dataset, we develop a strategy for a rational design of catalytic materials. We identify “materials genes” (features of catalyst materials) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO 2 ) towards a chemical conversion. The approach is used to address the conversion of CO 2 to fuels and other useful chemicals. The AI model is trained on high-throughput first-principles data for a broad family of oxides. We demonstrate that bending of the gas-phase linear molecule, previously proposed as the indicator of activation, is insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI approach identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. The same conclusion is obtained by using the bending indicator only when incombination with the Sabatier principle. Based on these findings, we propose a set of new promising oxide-based catalyst materials for CO 2 conversion, and a recipe to find more. Our analysis also reveals advantages of local pattern discovery methods such as subgroup discovery over standard global regression approaches in discovering combinations of materials properties that result in a catalytic activation. Catalysis ; Semiconductor ; Gene ; Materials science ; Nanotechnology ; Computational biology ; Computer science ; Biology ; Optoelectronics ; Genetics ; Biochemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Conrad W. Rosenbrock | Konstantin Gubaev | Alexander V. Shapeev | Lívia B. Pártay | Noam Bernstein | Gábor Cśanyi | Gus L. W. Hart
Source: npj Computational Materials (preprint, 2021-01-29)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials for alloys and alloy phase diagrams Abstract We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density. Both types of potentials give excellent accuracy for a wide range of compositions, competitive with the accuracy of cluster expansion, a benchmark for this system. While both models are able to describe small deformations away from the lattice positions, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys. Statistical physics ; Lattice (music) ; Cluster expansion ; Phase diagram ; Interatomic potential ; Gaussian ; Fourier transform ; Computer science ; Physics ; Phase (matter) ; Mathematics ; Computational chemistry ; Chemistry ; Mathematical analysis ; Thermodynamics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jana Vejpravová
Source: Preprints.org (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Graphene – Diamond Nanomaterials: A Status Quo Review Carbon nanomaterials with a different character of the chemical bond &amp;ndash; graphene (sp2) and nanodiamond (sp3) are the building bricks for a new class of all-carbon hybrid nanomaterials, where the two different carbon networks with the sp3 and sp2 hybridization coexist, interact and even transform into one another. The unique electronic, mechanical, and chemical properties of the two border nanoallotropes of carbon ensure the immense application potential and versatility of these all-carbon graphene &amp;ndash; diamond nanomaterials. The review gives an overview of the current state of the art of graphene &amp;ndash; diamond nanomaterials, including their composites, heterojunctions, and other hybrids for sensing, electronic, energy storage, and other applications. Also, the graphene-to-diamond and diamond-to-graphene transformations at the nanoscale, essential for innovative fabrication, and stability and chemical reactivity assessment are discussed based on extensive theoretical, computational, and experimental studies. Graphene ; Nanotechnology ; Nanomaterials ; Diamond ; Materials science ; Carbon fibers ; Nanodiamond ; Composite number ; Composite material Diamond and Carbon-based Materials Research ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: P. Shobha | Albin John P. Paul Winston | S. Sunil | T. Manovah David | S. Mary Margaret | S. Muthupandi | P. Sagayaraj
Source: Journal of Nanomaterials (article, 2021-06-05)
Field: Materials Science — Materials Chemistry

Facile Synthesis of rGO/Mn3O4 Composite for Efficient Photodegradation of Phenol under Visible Light To enhance reusability and to maintain higher efficiency in degradation, Mn3O4/rGO nanocomposites were synthesized by a facile thermal treatment. Initially, Mn3O4 nanoparticles were prepared and analyzed by powder XRD and HR-SEM. The composition of manganese oxide was varied to obtain different nanocomposites. The Mn3O4 ions were found to be well anchored onto the rGO surface. The obtained samples were taken for the photodegradation studies with phenol as the pollutant. Under a dynamic mode, the absorption efficiency was found to be maximum for the MnsR0.75 sample for phenol. Photodegradation ; Materials science ; Phenol ; Nanocomposite ; Degradation (telecommunications) ; Composite number ; Chemical engineering ; Oxide ; Visible spectrum ; Absorption (acoustics) ; Nanoparticle ; Photocatalysis ; Nuclear chemistry ; Composite material ; Catalysis ; Nanotechnology ; Organic chemistry ; Metallurgy Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Graphene and Nanomaterials Applications