jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew J. Lew | Cayla A. Stifler | Astrid Cantamessa | Alexandra Tits | Davide Ruffoni | Benjamin Gilbert | Markus J. Buehler
Source: Matter (article, 2023-04-17)
Field: Materials Science — Materials Chemistry

Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design Characterization (materials science) ; Computer science ; Artificial intelligence ; Artificial neural network ; Deep learning ; Focus (optics) ; Throughput ; Biological system ; Materials science ; Nanotechnology Machine Learning in Materials Science
Authors: Mikhail Popov | Fedor Khorobrykh | С. А. Климин | Valentin Churkin | D. A. Ovsyannikov | Alexander G. Kvashnin
Source: Nanomaterials (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Surface Tamm States of 2–5 nm Nanodiamond via Raman Spectroscopy We observed resonance effects in the Raman scattering of nanodiamonds with an average size of 2-5 nm excited at a wavelength of 1064 nm (1.16 eV). The resonant Raman spectrum of the 2-5 nm nanodiamonds consists of bands at wavelengths of 1325 and 1600 cm<sup>-1</sup>, a band at 1100-1250 cm<sup>-1</sup>, and a plateau in the range from 1420 to 1630 cm<sup>-1</sup>. When excited away from the resonance (at a wavelength of 405 nm, 3.1 eV), the Raman spectrum consists of only three bands at 1325, 1500, and 1600 cm<sup>-1</sup>. It is important to note that the additional lines (1500 and 1600 cm<sup>-1</sup>) belong to the sp<sup>3</sup>-hybridized carbon bonds. The phonon density of states for the nanodiamonds (~1 nm) was calculated using moment tensor potentials (MTP), a class of machine-learning interatomic potentials. The presence of these modes in agreement with the lattice dynamics indicates the existence of bonds with force constants higher than in single-crystal diamonds. The observed resonant phenomena of the Raman scattering and the increase in the bulk modulus are explained by the presence of Tamm states with an energy of electronic transitions of approximately 1 eV, previously observed on the surface of single-crystal diamonds. Raman spectroscopy ; Excited state ; Raman scattering ; Materials science ; Resonance (particle physics) ; Phonon ; Molecular physics ; Nanodiamond ; Crystal (programming language) ; Atomic physics ; Wavelength ; X-ray Raman scattering ; Diamond ; Condensed matter physics ; Chemistry ; Optics ; Physics ; Optoelectronics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Ali Khalvandi | Lobat Tayebi | Saeed Kamarian | Saeed Saber‐Samandari | Jung‐il Song
Source: International Journal of Biological Macromolecules (article, 2023-09-15)
Field: Materials Science — Biomaterials

Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments Self-healing hydrogels ; Gelatin ; Materials science ; Compressive strength ; Polyvinyl alcohol ; Porosity ; Biocompatibility ; Composite material ; Biomedical engineering ; Tissue engineering ; Porosimetry ; Swelling ; Chemical engineering ; Porous medium ; Chemistry ; Polymer chemistry Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; 3D Printing in Biomedical Research
Authors: Nanda V. Ranade | Vivek V. Ranade
Source: Ultrasonics Sonochemistry (article, 2023-02-11)
Field: Materials Science — Materials Chemistry

ANN based surrogate model for key Physico-chemical effects of cavitation Intense and localised physico-chemical effects realised by cavitation such as generation of hydroxyl radicals, high-speed jets, and very high energy dissipation rates are being harnessed for a wide range of applications from emulsions, crystallisation, reactions to water treatment and waste valorisation. Single cavity models are typically used to quantitatively estimate such localised effects of cavity collapse. However, these models demand significant computing resources for resolving fast dynamics and therefore are very difficult, if not impossible, to integrate with CFD based cavitation device or reactor scale models. This severely limits the utility of device/ reactor scale models in simulating key applications of interest. In this work, we present, for the first time, artificial neural network (ANN) based surrogate models which accurately represent complex physico-chemical effects of cavity collapse. Recently developed cavity dynamics model was used for generating training data set encompassing both acoustic and hydrodynamic cavitation. Appropriate methodology for training ANN was developed. A shallow three hidden layer dense ANN was found to be more effective for estimating three main effects of cavity collapse: jet velocity, •OH generation and localised energy dissipation rate. The performance of trained ANN was then evaluated by comparing the predictions with the totally unseen data obtained from the cavity dynamics model. The developed ANN was shown to simulate unseen data very well not just within the range of training data (interpolation) but also beyond (extrapolation). Algebraic equations representing ANN are included to facilitate incorporation in device/ reactor scale CFD models. The presented methodology and results will be useful for developing high-fidelity CFD models of cavitation devices/ reactors based on key physico-chemical effects of cavity collapse. Cavitation ; Extrapolation ; Computational fluid dynamics ; Range (aeronautics) ; Computer science ; Dissipation ; Artificial neural network ; Scale (ratio) ; Interpolation (computer graphics) ; Mechanics ; Work (physics) ; Biological system ; Simulation ; Mechanical engineering ; Artificial intelligence ; Thermodynamics ; Mathematics ; Engineering ; Physics ; Aerospace engineering Ultrasound and Cavitation Phenomena ; Cavitation Phenomena in Pumps ; Flow Measurement and Analysis
Authors: Lei Guo | Jiyang Dong | Xiangnan Xu | Zhichao Wu | Yinbin Zhang | Yongwei Wang | Peng‐Fei Li | Zhi Tang | Chao Zhao | Zongwei Cai
Source: Analytical Chemistry (article, 2023-01-12)
Field: Chemistry — Spectroscopy

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on <i>in situ</i> molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase. Mass spectrometry imaging ; Identification (biology) ; Computational biology ; Divide and conquer algorithms ; Segmentation ; Phenotype ; Computer science ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Mass spectrometry ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Sergei Gladyshev | Theodosios D. Karamanos | Lina Kuhn | Dominik Beutel | Thomas Weiß | Carsten Rockstuhl | Andrey Bogdanov
Source: Nanophotonics (article, 2023-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of all‐dielectric metasurfaces with accidental bound states in the continuum Metasurfaces with bound states in the continuum (BICs) have proven to be a powerful platform for drastically enhancing light-matter interactions, improving biosensing, and precisely manipulating near- and far-fields. However, engineering metasurfaces to provide an on-demand spectral and angular position for a BIC remains a prime challenge. A conventional solution involves a fine adjustment of geometrical parameters, requiring multiple time-consuming calculations. In this work, to circumvent such tedious processes, we develop a physics-inspired, inverse design method on all-dielectric metasurfaces for an on-demand spectral and angular position of a BIC. Our suggested method predicts the core-shell particles that constitute the unit cell of the metasurface, while considering practical limitations on geometry and available materials. Our method is based on a smart combination of a semi-analytical solution, for predicting the required dipolar Mie coefficients of the meta-atom, and a machine learning algorithm, for finding a practical design of the meta-atom that provides these Mie coefficients. Although our approach is exemplified in designing a metasurface sustaining a BIC, it can, also, be applied to many more objective functions. With that, we pave the way toward a general framework for the inverse design of metasurfaces in specific and nanophotonic structures in general. Inverse ; Nanodevice ; Inverse problem ; Nanophotonics ; Dipole ; Dielectric ; Angular spectrum method ; Mie scattering ; Computer science ; Robustness (evolution) ; Physics ; Optics ; Nanotechnology ; Mathematics ; Quantum mechanics ; Materials science ; Geometry ; Light scattering ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Z.H. Wang | Yu Ma | G.H. Tang | Hu Zhang | Ji Feng | Qiang Sheng
Source: Energy (article, 2023-05-12)
Field: Materials Science — Materials Chemistry

Integration of thermal insulation and thermoelectric conversion embedded with phase change materials Thermoelectric generator ; Thermoelectric effect ; Materials science ; Thermal ; Thermoelectric materials ; Mechanical engineering ; Work (physics) ; Generator (circuit theory) ; Electricity generation ; Phase-change material ; Thermal insulation ; Thermal energy ; Waste heat ; Nuclear engineering ; Process engineering ; Power (physics) ; Automotive engineering ; Thermal conductivity ; Engineering ; Composite material ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Phase Change Materials Research ; Thermal properties of materials
Authors: Xue Jia | Honghao Yao | Zhijie Yang | Jianyang Shi | Jinxin Yu | Rongpei Shi | Haijun Zhang | Feng Cao | Xi Lin | Jun Mao | Cuiping Wang | Qian Zhang | Xingjun Liu
Source: Applied Physics Letters (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations The data-driven machine learning technique is widely used to assist in accelerating the design of thermoelectric materials. In this study, we proposed a positive and unlabeled learning (PU learning) method, a semi-supervised learning, to train a classifier to distinguish the positive samples from the unlabeled samples, in which the positive class was labeled by matching the formulas in our dataset with the published article titles. The probabilities that the unlabeled materials belong to the positive class were predicted by PU learning, and 40 candidate thermoelectric materials were determined. The transport properties were calculated by high-throughput first-principles calculations, among which 8 p-type and 12 n-type materials have the maximum theoretical zT values greater than 1. Specifically, a series of AX2 binary compounds, (Cd/Zn)(GaTe2)2 ternary compounds, and Cs(Dy/Ho/Tb)2Ag3Te5 quaternary compounds deserve further investigations in the future. Ternary operation ; Classifier (UML) ; Binary number ; Thermoelectric effect ; Throughput ; Materials science ; Computer science ; Matching (statistics) ; Binary classification ; Artificial intelligence ; Class (philosophy) ; Machine learning ; Mathematics ; Thermodynamics ; Physics ; Statistics ; Support vector machine ; Arithmetic Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Burcu Üstün | Hamide Aydın | Serkan Naci Koç | Ahmet Uluslu | Ü. Kurtan
Source: Journal of Energy Storage (article, 2023-09-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrospun polyethylenimine (PEI)-derived nitrogen enriched carbon nanofiber for supercapacitors with artificial neural network modeling Supercapacitor ; Polyethylenimine ; Materials science ; Electrospinning ; Capacitance ; Specific surface area ; Chemical engineering ; Heteroatom ; Nanofiber ; Mesoporous material ; Carbon fibers ; Barrer ; Polymer ; Electrode ; Microporous material ; Polyetherimide ; Nitrogen ; Activated carbon ; Nanotechnology ; Composite material ; Membrane ; Chemistry ; Organic chemistry ; Composite number ; Gas separation ; Adsorption ; Catalysis Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications
Authors: Sang‐Bum Kim | Donghyeon Son | Joo‐Hee Kang | Youho Lee
Source: Journal of Nuclear Materials (article, 2023-04-22)
Field: Materials Science — Materials Chemistry

Recrystallization and grain growth of Zr-Nb-Sn alloy in 400–500 °C and effect on hydride embrittlement Materials science ; Recrystallization (geology) ; Grain growth ; Hydride ; Metallurgy ; Electron backscatter diffraction ; Embrittlement ; Dynamic recrystallization ; Annealing (glass) ; Grain boundary ; Alloy ; Grain size ; Microstructure ; Hot working Nuclear Materials and Properties ; Fusion materials and technologies ; High Temperature Alloys and Creep
Authors: Rui Liu | Yuechuan Tang | Jie Tian | Jing Huang | Chaoyang Zhang | Linyuan Wang | Jian Liu
Source: Chemical Engineering Journal (article, 2023-08-28)
Field: Chemistry — Organic Chemistry

QSPR models for sublimation enthalpy of energetic compounds Sublimation (psychology) ; Enthalpy ; Quantitative structure–activity relationship ; Detonation ; Particle swarm optimization ; Chemistry ; Molecule ; Thermodynamics ; Computer science ; Algorithm ; Physics ; Stereochemistry ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Tong Xie | Yuwei Wan | Yufei Zhou | Wei Huang | Yixuan Liu | Qingyuan Linghu | Shaozhou Wang | Chunyu Kit | Clara Grazian | Wenjie Zhang | Bram Hoex
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Large Language Models as Master Key: Unlocking the Secrets of Materials Science Key (lock) ; Computer science ; Data science ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yoonjung Kim | Taeyoung Yoon | Woo B. Park | Sungsoo Na
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2023-02-21)
Field: Materials Science — Biomaterials

Predicting mechanical properties of silk from its amino acid sequences via machine learning SILK ; Amino acid ; Biocompatibility ; Sequence (biology) ; Materials science ; Composite material ; Chemistry ; Biochemistry Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Antimicrobial Peptides and Activities
Authors: Chengquan Zhong | Jingzi Zhang | Xiaoting Lu | Ke Zhang | Jiakai Liu | Kailong Hu | Junjie Chen | Xi Lin
Source: ACS Applied Materials & Interfaces (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted <i>T</i><sub>c</sub> &gt; 77 K Identifying new superconductors with high transition temperatures (<i>T</i><sub>c</sub> > 77 K) is a major goal in modern condensed matter physics. The inverse design of high <i>T</i><sub>c</sub> superconductors relies heavily on an effective representation of the superconductor hyperspace due to the underlying complexity involving many-body physics, doping chemistry and materials, and defect structures. In this study, we propose a deep generative model that combines two widely used machine learning algorithms, namely, the variational auto-encoder (VAE) and the generative adversarial network (GAN), to systematically generate unknown superconductors under the given high <i>T</i><sub>c</sub> condition. After training, we successfully identified the distribution of the representative hyperspace of superconductors with different <i>T</i><sub>c</sub>, in which many superconductor constituent elements were found adjacent to each other with their neighbors in the periodic table. Equipped with the conditional distribution of <i>T</i><sub>c</sub>, our deep generative model predicted hundreds of superconductors with <i>T</i><sub>c</sub> > 77 K, as predicted by the published <i>T</i><sub>c</sub> prediction models in the literature. For the copper-based superconductors, our results reproduced the variation in Tc as a function of the Cu concentration and predicted an optimal <i>T</i><sub>c</sub> = 129.4 K, when the Cu concentration reached 2.41 in Hg<sub>0.37</sub>Ba<sub>1.73</sub>Ca<sub>1.18</sub>Cu<sub>2.41</sub>O<sub>6.93</sub>Tl<sub>0.69</sub>. We expect that such an inverse design model and comprehensive list of potential high Tc superconductors would greatly facilitate future research activities in superconductors. Superconductivity ; Inverse ; Condensed matter physics ; High-temperature superconductivity ; Generative grammar ; Materials science ; Generative model ; Statistical physics ; Physics ; Computer science ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Zhenfang Liu | Yu Yang | Min Huang | Qibing Zhu
Source: Sensors (article, 2023-03-05)
Field: Chemistry — Analytical Chemistry

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The <i>R</i><sup>2</sup>, <i>RMSE</i>, and <i>RPD</i> of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp. Offset (computer science) ; Shrimp ; Computer science ; Raman spectroscopy ; Raman scattering ; Artificial intelligence ; Principal component analysis ; Pattern recognition (psychology) ; Biological system ; Environmental science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Morteza Mehrabian | Javad Mahmoudimehr
Source: Renewable Energy (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

A correlation for optimal steam-to-fuel ratio in a biogas-fueled solid oxide fuel cell with internal steam reforming by using Artificial Neural Networks Biogas ; Solid oxide fuel cell ; Steam reforming ; Methane ; Waste management ; Process engineering ; Environmental science ; Engineering ; Chemistry ; Hydrogen ; Hydrogen production Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Lena Dyckhoff | N. Huber
Source: International Journal of Mechanical Sciences (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

Data-driven modelling of the multiaxial yield behaviour of nanoporous metals Nanoporous metals, built out of complex ligament networks, can be produced with an additional level of hierarchy. The resulting complexity of the structure makes modelling of the mechanical behaviour computationally expensive and time consuming. In addition, multiaxial stresses occur in the higher hierarchy ligaments. Therefore, knowledge of the multiaxial material behaviour, including the 6D yield surface, is required. Surrogate models, predicting the mechanical behaviour of the lower level of hierarchy, represented by finite element beam models, are a promising approach to overcome such challenges, when existing analytical models are not able to describe the material behaviour. Therefore, as a first step, we studied the elastic behaviour and the yield surfaces of representative volume elements with idealised diamond and Kelvin structure in finite element simulations. The yield surfaces showed pronounced anisotropy and could not be described by the Deshpande-Fleck model for isotropic solid foams. Instead, we used data-driven and hybrid artificial neural networks, as well as data-driven support vector machines and compared them regarding their potential for the prediction of yield surfaces. All considered methods were well suited and resulted in relative errors <4.5%. Support vector machines showed the best generalisation and accuracy in 6D stress space and are suitable for extrapolation outside the range of training data. Extrapolation ; Finite element method ; Yield (engineering) ; Hierarchy ; Isotropy ; Nanoporous ; Anisotropy ; Representation (politics) ; Yield surface ; Artificial neural network ; Materials science ; Range (aeronautics) ; Computer science ; Structural engineering ; Biological system ; Mechanical engineering ; Machine learning ; Engineering ; Mathematics ; Composite material ; Nanotechnology ; Mathematical analysis ; Constitutive equation ; Physics Nanoporous metals and alloys ; Anodic Oxide Films and Nanostructures ; Corrosion Behavior and Inhibition