jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diba Behnoudfar | Cory M. Simon | Joshua Schrier
Source: The Journal of Physical Chemistry B (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

Data-Driven Imputation of Miscibility of Aqueous Solutions via Graph-Regularized Logistic Matrix Factorization Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how to impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization (GR-LMF) to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph where each node is a solution and edges are relationships indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). For an experimental data set of the pairwise miscibility of 68 solutions from Peacock et al. [<i>ACS Appl. Mater. Interfaces</i> <b>2021</b>, <i>13</i>, 11449-11460], we find that GR-LMF more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers that use physicochemical features of the solutes. GR-LMF obviates the need for features of the solutions and solutions to impute missing miscibility outcomes, but it cannot predict the miscibility of a new solution without some observations of its miscibility with other solutions in the training data set. Miscibility ; Pairwise comparison ; Aqueous solution ; Thermodynamics ; Mathematics ; Chemistry ; Materials science ; Statistics ; Polymer ; Physical chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Christoph H. van der Broeck | Timothy A. Polom | Rik W. De Doncker
Source: IEEE Transactions on Industry Applications (article, 2023-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Degradation Diagnosis of Power Modules Based on Thermal Phase Response Sensing and Artificial Neural Networks This work proposes a degradation diagnosis methodology for power modules that utilizes changes in thermal response to estimate convection, thermal interface material, and die-attach degradation. The methodology extracts phase response changes of either junction temperature or heat sink temperature and applies artificial neural networks (ANNs) to detect, separate and quantify different degradation modes. The article develops a piece-wise linear thermal model that describes the variable effects of convection, thermal interface material, and die-attach degradation, and compares it with experimental measurements to demonstrate the feasibility of the approach. A comprehensive analysis identifies the frequencies at which each of the three degradation modes has an effect on the thermal impedance phase response that can be measured with thermal impedance spectroscopy. The analysis shows that junction temperature measurements with moderate bandwidth of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5 \,\mathrm{Hz}$</tex-math></inline-formula> allow detecting each of the three degradation modes even if they occur superimposed. A heat sink temperature measurement only with a bandwidth of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$100 \,\mathrm{m}\mathrm{Hz}$</tex-math></inline-formula> , can still detect individual and superimposed degradation of the convection and thermal interface material. ANNs are designed and trained to estimate the life-time varying parameters that model different degradation modes. The evaluation of the ANN based degradation estimation shows that the three degradation modes can be identified robustly even if they occur superimposed with a maximal error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1 {\%}\,\mathrm{to}\, 2 {\%}$</tex-math></inline-formula> . Overall, this works demonstrates degradation monitoring and diagnosis that provides valuable data on the the state-of-health and power capability of a converter but is realized with moderate implementation complexity. Degradation (telecommunications) ; Artificial neural network ; Computer science ; Thermal ; Power (physics) ; Control engineering ; Engineering ; Electronic engineering ; Artificial intelligence ; Physics Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Induction Heating and Inverter Technology
Authors: Iin Indriyani | Ida Ayu Dwi Giriantari | Made Sudarma | I Made Oka Widyantara
Source: Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications (article, 2023-06-30)
Field: Chemistry — Analytical Chemistry

Facial Skin Type Detection for Race Classification using Convolutional Neural Network and Haar Cascade Method Every organism possesses a unique structural makeup, extending from molecules to organ systems. One such organ, the skin, is our body's largest and the most complex. Its diversity in color corresponds with various human races, although the facial skin type, an often-overlooked factor, also plays a significant role in race identification. In this research, a system was developed to recognize racial classifications from facial skin types by focusing on features in the facial T and U areas, using the Convolutional Neural Network (CNN) and Haar cascade methodologies. CNN was employed due to its ability to use the convolution process, moving a kernel across an image, multiplying it with the applied filter, and thereby generating new representative information. It is especially effective in image recognition and processing. The Haar cascade method, on the other hand, was used to outline the T and U areas on the face for the skin type detection system. The T area, known for oil detection, identifies skin types, while the U area identifies race types by forming facial patterns. This system, trained on 1670 race and 60 skin type datasets and optimized using the Adam optimizer, exhibited high accuracy levels. Upon testing with five new samples, it demonstrated an average accuracy of 98% in race detection and 97% in skin type detection. Convolutional neural network ; Artificial intelligence ; Haar-like features ; Pattern recognition (psychology) ; Computer science ; Kernel (algebra) ; Face (sociological concept) ; Haar ; Identification (biology) ; Cascade ; Skin color ; Face detection ; Filter (signal processing) ; Computer vision ; Facial recognition system ; Mathematics ; Biology ; Engineering Dye analysis and toxicity ; melanin and skin pigmentation ; Textile materials and evaluations
Authors: Rui Qiu | Guanmao Zhang | Shaokai Du | Jie Liu | Hongyu Ji | Kaiyun Bi | Bochuan Xing | Guangchao Diao
Source: Optics Communications (article, 2023-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network-assisted design of GSST-based achromatic metalens with continuously variable focal heights Achromatic lens ; Optics ; Focal length ; Numerical aperture ; Chromatic aberration ; Materials science ; Wavelength ; Focus (optics) ; Aperture (computer memory) ; Physics ; Chromatic scale ; Lens (geology) Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic and Optical Devices
Authors: Henrique C. Silva | Heloisa N. S. Menezes | Gláucio B. Ferreira | Guilherme P. Guedes
Source: Inorganic Chemistry (article, 2023-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid and Accurate Prediction of the Axial Magnetic Anisotropy in Cobalt(II) Complexes Using a Machine-Learning Approach Estimating the magnetic anisotropy for single-ion magnets is complex due to its multireference nature. This study demonstrates that deep neural networks (DNNs) can provide accurate axial magnetic anisotropy (<i>D</i>) values, closely matching the complete-active-space self-consistent-field (CASSCF) quality using density functional theory (DFT) data. We curated an 86-parameter database (UFF1) with electronic data from over 33000 cobalt(II) compounds. The DNN achieved an <i>R</i><sup>2</sup> of 0.906 and a mean absolute error of 18.1 cm<sup>-1</sup> in comparison to reference CASSCF <i>D</i> values. Remarkably, it is 11 times more accurate than DFT methods and 7700 times faster. This approach hints at DNNs predicting the anisotropy in larger molecules, even when trained on smaller ligands. Chemistry ; Anisotropy ; Density functional theory ; Complete active space ; Cobalt ; Magnetic anisotropy ; Matching (statistics) ; Magnet ; Molecule ; Space (punctuation) ; Field (mathematics) ; Computational chemistry ; Computational physics ; Magnetic field ; Quantum mechanics ; Physics ; Computer science ; Statistics ; Inorganic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Pedro O. Pinheiro | Joshua A. Rackers | Joseph Kleinhenz | Michael Maser | Omar Abdulkareem Mahmood | Andrew M. Watkins | Stephen Ra | Vishnu Sresht | Saeed Saremi
Source: arXiv (Cornell University) (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

3D molecule generation by denoising voxel grids We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules. Then, we follow the neural empirical Bayes framework (Saremi and Hyvarinen, 19) and generate molecules in two steps: (i) sample noisy density grids from a smooth distribution via underdamped Langevin Markov chain Monte Carlo, and (ii) recover the "clean" molecule by denoising the noisy grid with a single step. Our method, VoxMol, generates molecules in a fundamentally different way than the current state of the art (ie, diffusion models applied to atom point clouds). It differs in terms of the data representation, the noise model, the network architecture and the generative modeling algorithm. Our experiments show that VoxMol captures the distribution of drug-like molecules better than state of the art, while being faster to generate samples. Computer science ; Grid ; Noise reduction ; Algorithm ; Noise (video) ; Statistical physics ; Voxel ; Artificial neural network ; Artificial intelligence ; Mathematics ; Physics ; Image (mathematics) Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Protein Structure and Dynamics
Authors: Kaibin Yao | Xin Xie | Jiawei Jiao | Anping Liu | Yingzhou Huang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2023-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic cellulose microfilament assisted SERS detection in microfluidics Microfluidics ; Plasmon ; Nanotechnology ; Materials science ; Microfilament ; Surface-enhanced Raman spectroscopy ; Plasmonic nanoparticles ; Cellulose ; Microchannel ; Nanoparticle ; Raman spectroscopy ; Chemistry ; Optoelectronics ; Raman scattering ; Optics ; Cytoskeleton ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Xiaopin Lai | Kaiqing Liang | Yang Su | Kunbin Guo | Xin Wang | Yunhui Wan | Cuiqiong Ye | Zhou Chengke | Rongjia Chen | Wenhua Gao | Yuping Chen | Wen Lin | Wen‐Xiu Ni | Yan Lin | Kwan‐Ming Ng
Source: ACS Applied Materials & Interfaces (article, 2023-07-18)
Field: Chemistry — Spectroscopy

Serum Lipidomic Fingerprints Encode Early Diagnosis and Staging of Lung Cancer on a Novel PbS/Au-Layered Substrate Lung cancer (LC) is a major cause of mortality among malignant tumors. Early diagnosis through lipidomic profiling can improve prognostic outcomes. In this study, a uniform PbS/Au-layered substrate that enhances the laser desorption/ionization process, an interfacial process triggered on the substrate surface upon laser excitation, was designed to efficiently characterize the lipidomic profiles of LC patient serum. By controlling the stacking arrangement and particle sizes of PbS QDs and AuNPs, the optimized substrate promotes the generation of excited electrons and creates an enhanced electric field that polarizes analyte molecules, facilitating ion adduction formation ([M + Na]<sup>+</sup> and [M + K]<sup>+</sup>) and enhancing detection sensitivity down to the femtomole level. Combining multivariate statistics and machine learning, a distinct lipidomic biomarker panel is successfully identified for the early diagnosis and staging of LC, with an accurate prediction validated by an area under the curve of 0.9479 and 0.9034, respectively. We also found that 18 biomarkers were significantly correlated with six metabolic pathways associated with LC. These results demonstrate the potential of this innovative PbS/Au-layered substrate as a sensitive platform for accurate diagnosis of LC and facilitate the development of lipidomic-based diagnostic tools for other cancers. Materials science ; Substrate (aquarium) ; Analyte ; Nanotechnology ; Chromatography ; Chemistry ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Donghan Wang | Wenze Li | Dong Xu | Hongzhi Li | Lihong Hu
Source: Journal of Chemical Information and Modeling (article, 2023-01-18)
Field: Materials Science — Materials Chemistry

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory. Interpretability ; Computer science ; Encoder ; Artificial intelligence ; Visualization ; Pattern recognition (psychology) ; Benchmark (surveying) ; Transformer ; Machine learning ; Feature extraction ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Nasser Zouli | Ibrahim M. Maafa | Ahmed Abutaleb | Ayman Yousef | M.M. El-Halwany
Source: Polymers (article, 2023-02-06)
Field: Materials Science — Materials Chemistry

Membrane Nanofiber-Supported Cobalt–Nickel Nanoparticles as an Effective and Durable Catalyst for H2 Evolution via Sodium Borohydride Hydrolysis The successful support of bimetallic NiCo alloy nanoparticles (NPs) on poly(vinylidene fluoride-co-hexafluoropropylene) nanofibers (PVDF-HFP NFs) was achieved through electrospinning (ES) and in situ reduction. The synthesis and physicochemical characterization of Ni-Co@PVDF-HFP NFs with a range of bimetallic compositions (Ni<sub>1-x</sub>Co<sub>x</sub>, x = 0, 0.1, 0.3, 0.5, 0.7, 0.9, and 1) supported on PVDF-HFP NFs was undertaken. In comparison to their counterparts (Ni-PVDF-HFB and Co-PVDF-HFB), the bimetallic hybrid NF membranes demonstrated a significantly increased volume of H<sub>2</sub> generation from sodium borohydride (SBH). The high performance of bimetallic catalysts can be attributed mostly to the synergistic impact of Ni and Co. Among all fabricated catalysts, Ni<sub>0.3</sub>Co<sub>0.7</sub>@PVDF-HFP produced the highest H<sub>2</sub> production in a short time. The maximum generated H<sub>2</sub>volume was 118 mL in 11.5, 9, 6, and 4.5 min at 298, 308, 318, and 328 K, respectively. Kinetic analyses showed that the hydrolysis process proceeded as a quasi-first-order reaction with respect to the amount of catalyst and as a zero-order reaction with respect to the concentration of SBH. Thermodynamics studies were also undertaken and the parameters were calculated as <i>E<sub>a</sub></i>, Δ<i>S</i>, and Δ<i>H</i> = 30.17 kJ/mol, 0.065 kJ/mol, and 27.57 kJ/mol K, respectively. The introduced NFs can be easily separated and reused, which facilitates their industrialization and commercialization applications in hydrogen storage systems. Bimetallic strip ; Sodium borohydride ; Catalysis ; Electrospinning ; Materials science ; Nanofiber ; Cobalt ; Chemical engineering ; Hydrogen storage ; Nanoparticle ; Membrane ; Nickel ; Hydrolysis ; Hydrogen production ; Nuclear chemistry ; Inorganic chemistry ; Alloy ; Chemistry ; Nanotechnology ; Organic chemistry ; Metallurgy ; Composite material ; Polymer Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced battery technologies research
Authors: Bohayra Mortazavi
Source: Journal of Composites Science (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

A Theoretical Investigation of the Structural, Electronic and Mechanical Properties of Pristine and Nitrogen-Terminated Carbon Nanoribbons Composed of 4–5–6–8-Membered Rings Among the exciting recent advances in the field of carbon-based nanomaterials, the successful realization of a carbon nanoribbon composed of 4–5–6–8-membered rings (ACS Nano 2023 17, 8717) is a particularly inspiring accomplishment. In this communication motivated by the aforementioned achievement, we performed density functional theory calculations to explore the structural, electronic and mechanical properties of the pristine 4–5–6–8-membered carbon nanoribbons. Moreover, we also constructed four different nitrogen-terminated nanoribbons and analyzed their resulting physical properties. The acquired results confirm that the pristine and nitrogen-terminated nanoribbons are are thermally stable direct-gap semiconductors, with very close HSE06 band gaps between 1.12 and 1.25 eV. The elastic modulus and tensile strength of the nitrogen-free 4–5–6–8-membered nanoribbon are estimated to be remarkably high, 534 and 41 GPa, respectively. It is shown that nitrogen termination can result in noticeable declines in the tensile strength and elastic modulus to 473 and 33 GPa, respectively. This study provides useful information on the structural, thermal stability, electronic and mechanical properties of the pristine and nitrogen-terminated 4–5–6–8-membered carbon nanoribbons and suggests them as strong direct-gap semiconductors for electronics, optoelectronics and energy storage systems. Materials science ; Semiconductor ; Ultimate tensile strength ; Carbon fibers ; Band gap ; Density functional theory ; Modulus ; Nanomaterials ; Nanotechnology ; Electronic structure ; Thermal stability ; Nitrogen ; Composite material ; Computational chemistry ; Optoelectronics ; Chemistry ; Composite number Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites
Authors: Cecilia Merino-Robledillo | Marco Marazzi
Source: Frontiers in Chemistry (article, 2023-04-12)
Field: Materials Science — Materials Chemistry

Taking up the quest for novel molecular solar thermal systems: Pros and cons of storing energy with cubane and cubadiene Molecular solar thermal (MOST) systems are working their way as a possible technology to store solar light and release it when necessary. Such systems could, in principle, constitute a solution to the energy storage problem characteristic of solar cells and are conceived, at a first instance, as simple molecular photoswitches. Nevertheless, the optimization of their different required properties is presently limiting their technological scale up. From the chemical perspective, we need to design a novel MOST system based on unconventional photoswitches. Here, by applying multi-configurational quantum chemistry methods, we unravel the potentialities of <i>ad hoc</i>-designed molecular photoswitches, which aim to photoproduce cubane or cubadiene as high-energy isomers that can be thermally (or eventually catalytically) reverted to the initial structure, releasing their stored energy. Specifically, while cubane can be photoproduced <i>via</i> different paths depending on the reactant tricycle diene conformation, an undesired bicyclic by-product limits its application to MOST systems. An evolution of this starting design toward cubadiene formation is therefore proposed, avoiding conformational equilibria and by-products, considerably red shifting the absorption to reach the visible portion of the solar spectrum and maintaining an estimated storage density that is expected to overcome the current MOST reference system (norbornadiene/quadricyclane), although consistently increasing the photoisomerization energy barrier. cons ; Cubane ; Thermal ; Nanotechnology ; Chemistry ; Materials science ; Astrobiology ; Physics ; Computer science ; Molecule ; Thermodynamics ; Organic chemistry Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies ; Porphyrin and Phthalocyanine Chemistry