jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingying Wu | Jinchao Liu | Yan Wang | Stuart Gibson | Margarita Osadchy | Yongchun Fang
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence (article, 2023-12-27)
Field: Chemistry — Analytical Chemistry

Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistry, pharmacy and national defense. Recently, deep learning has been introduced into vibrational spectroscopy showing great potential. Different from images, text, etc. that offer large labeled data sets, vibrational spectroscopic data is very limited, which requires novel concepts beyond transfer and meta learning. To tackle this, we propose a task-enhanced augmentation network (TeaNet). The key component of TeaNet is a reconstruction module that inputs randomly masked spectra and outputs reconstructed samples that are similar to the original ones, but include additional variations learned from the domain. These augmented samples are used to train the classification model. The reconstruction and prediction parts are trained simultaneously, end-to-end with back-propagation. Results on both synthetic and real-world datasets verified the superiority of the proposed method. In the most difficult synthetic scenarios TeaNet outperformed CNN by 17%. We visualized and analysed the neuron responses of TeaNet and CNN, and found that TeaNet's ability to identify discriminant wavenumbers was excellent compared to CNN. Our approach is general and can be easily adapted to other domains, offering a solution to more accurate and interpretable few-shot learning. Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Discriminant ; Transfer of learning ; Linear discriminant analysis ; Deep learning ; Machine learning ; Synthetic data ; Range (aeronautics) ; Materials science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Chunyun Tu | Weijiang Huang | Sheng Liang | Kui Wang | Tian Qin | Wei Yan
Source: Physical Chemistry Chemical Physics (article, 2023-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-throughput virtual screening of organic second-order nonlinear optical chromophores within the donor–π-bridge–acceptor framework In view of the theoretical importance and huge application potential of second-order nonlinear optical (NLO) materials, it is of great significance to conduct high-throughput virtual screening (HTVS) on a compound library to find candidate NLO chromophores. Under the donor-π-bridge-acceptor structural framework, a virtual compound library (size = 27 090) was constructed by enumeration of structural fragments. The kernel property adopted for optimization is the static first hyperpolarizability (<i>β</i><sub>0</sub>). By combining machine learning and quantum chemical calculations, we have performed an HTVS procedure to sieve NLO chromophores out, and the response mechanism of the selected optimal NLO chromophores was examined. We have found: (a) The multi-layer perceptron/extended connectivity fingerprint combination with 20% selection ratio gives the highest prediction accuracy for the studied systems. (b) The two optimal donors are bis(4-diphenylaminophenyl)aminyl and bis(4-<i>tert</i>-butylphenyl)aminyl; the optimal π-bridges are composed of two thiophenyl, selenophenyl or furanyl units; and the two optimal acceptors are tri-<i>s</i>-triazinyl and 2,3-dicyanopyrazinyl. (c) The no. 1 candidate molecule can exhibit a calculated <i>β</i><sub>0</sub> equal to 8.55 × 10<sup>4</sup> a.u. (d) The difference in NLO responses of the optimal 16 molecules comes from the synergistic interaction of <i>E</i><sub>S<sub>1</sub></sub>, Δ<i>μ</i> and <i>f</i>, by employing the two-level model. In addition, the sizable Δ<i>μ</i> and <i>f</i> allow the studied optimal molecules to obtain a large NLO response in the meantime keeping a not-too-low excitation energy (retaining good optical transparency in the restricted range of the visible spectrum region). (e) With further modification on the acceptor, the designed DPA-π-TRZ-A' (A' = CN or NO<sub>2</sub>, π = oligo-thiophenyl or selenophenyl) systems can exhibit a rather large NLO response (maximum <i>β</i><sub>0</sub> = 3.17 × 10<sup>5</sup> a.u.), hence should have considerable potential as second-order NLO chromophores. With the above observations, we expect to provide some insight for the research community into the HTVS of organic second-order NLO chromophores. Chromophore ; Hyperpolarizability ; Acceptor ; Molecule ; Nonlinear optical ; Virtual screening ; Chemistry ; Nonlinear system ; Materials science ; Computational chemistry ; Physics ; Photochemistry ; Polarizability ; Organic chemistry ; Quantum mechanics Nonlinear Optical Materials Research ; Molecular spectroscopy and chirality ; Crystallography and molecular interactions
Authors: Adrien Teurtrie | Nathanaël Perraudin | Thomas Holvoet | Hui Chen | Duncan T. L. Alexander | Guillaume Obozinski | C. Hébert
Source: Ultramicroscopy (article, 2023-03-22)
Field: Materials Science — Surfaces, Coatings and Films

espm: A Python library for the simulation of STEM-EDXS datasets We present two open-source Python packages: “electron spectro-microscopy” (espm) and “electron microscopy tables” (emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes, based on user-defined chemical compositions and spatial abundance maps of constituent phases. The simulation process uses X-ray emission cross-sections generated via state-of-the-art calculations made with emtables. These tables are designed to be easily modifiable, either manually or using espm. The simulation framework is designed to test the application of decomposition algorithms for the analysis of STEM-EDX spectrum images with access to a known ground truth. We validate our approach using the case of a complex geology-related sample, comparing raw simulated and experimental datasets and the outputs of their non-negative matrix factorization. In addition to testing machine learning algorithms, our packages will also help experimental design, for instance, predicting dataset characteristics or establishing minimum counts needed to measure nanoscale features. Python (programming language) ; Computer science ; Computational science ; Ground truth ; Software ; Scanning transmission electron microscopy ; Factorization ; Nanoscopic scale ; Computer graphics (images) ; Artificial intelligence ; Transmission electron microscopy ; Algorithm ; Materials science ; Nanotechnology ; Programming language Electron and X-Ray Spectroscopy Techniques ; Geophysical and Geoelectrical Methods ; Machine Learning in Materials Science
Authors: Yuwen Zhang | Chunfeng Cui | Tao Ouyang | Chaoyu He | Jin Li | Mingxing Chen | Chao Tang
Source: Applied Physics Letters (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Thermal switch based on ferroelasticity V<i>A</i>-N binary compounds Ferroelastic materials possess two or more equally stable orientation variants and can be effectively modulated via external fields, including stress and electronic field. In this paper, taking the VA-N ferroelastic materials as examples, we propose a thermal switch device based on their ferroelastic characteristics. The results show that the VA-N binary compound exhibits excellent ferroelasticity, high reversible elastic strain (5.5%–54.1%), and suitable switching energy barriers (0.012–0.386 eV/atom) in both δ and α phases. Utilizing the advanced on-the-fly machine learning potential, we obtain physically well-defined quadratic dispersion curves in the long-wavelength limit and further evaluate their lattice thermal conductivity of δ and α phase VA-N binary compounds. Due to the difference in phonon group velocities, the lattice thermal conductivity of VA-N binary compounds along the armchair direction is obviously smaller than that along the zigzag direction. Such remarkable anisotropy and easily switchable features based on ferroelasticity endow reversible and real-time regulation of thermal conductivity of VA-N binary compounds. The ferroelastic-based thermal switch hosts high switch ratios range from 2.08 to 5.99 and does not require additional energy to maintain the modulation state. The results presented herein provide a pavement for designing next-generation thermal switches and propose a reliable solution for eliminating the nonphysical pseudo-phenomenon of phonon dispersion curve violation of quadratic dispersion in the long-wavelength limit. Ferroelasticity ; Phonon ; Condensed matter physics ; Anisotropy ; Thermal conductivity ; Materials science ; Binary number ; Thermal ; Dispersion (optics) ; Chemistry ; Thermodynamics ; Optics ; Optoelectronics ; Physics ; Dielectric ; Composite material Thermal properties of materials ; Ferroelectric and Piezoelectric Materials ; Adhesion, Friction, and Surface Interactions
Authors: Yuan Feng | Mehrisadat Makki Alamdari | Di Wu | Zhen Luo | Dong Ruan | Temitope Egbelakin | Xiaojun Chen | Wei Gao
Source: Engineering Structures (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Virtual modelling aided safety assessment for ductile structures against high-velocity impact This paper introduces a virtual modelling-aided computational analysis framework for assessing the safety of a ductile engineering object against high-velocity impact. By evaluating the data feedback continuously collected from the working site, the presented Virtual Modelling Aided Safety Assessment (VMASA) scheme is competent to effectively report the current safety level of the engineering object. The safety level (alternatively known as the reliability), of the impacted system can be quantified by using the first-passage theory. By considering various mercurial factors that are influencing the high-velocity impact for ductile materials, in this research, a new machine learning-aided virtual modelling technique as the clustering extended support vector regression (CXSVR), is implemented to evaluate the capacity of the engineering product against different conditions. Also, the John-Cook failure model is transformed into a random format to simulate the dynamic response of the engineering product against high-velocity impacts. A new T-spline kernel has also been developed within the CXSVR scheme. By using the VMASA, the inherent limit state function can be quantitatively certified by analysing the relationship between the system inputs and outputs. Both experimental and numerical investigations are implemented to demonstrate the accuracy, practicability, and efficiency of the proposed safety assessment framework. Computer science ; Reliability (semiconductor) ; Kernel (algebra) ; Reliability engineering ; Engineering ; Mathematics High-Velocity Impact and Material Behavior ; Advanced machining processes and optimization ; Metallurgy and Material Forming
Authors: Shweta Verma | Venkat Suprabath Bitra | Rashmi Singh | B. Tirumala Rao
Source: Materials Chemistry and Physics (article, 2023-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical response of Au films for reproducible Si nano-structuring and its application for efficient micro-drop SERS with portable Raman system Materials science ; Raman spectroscopy ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Rhodamine 6G ; Plasmon ; Raman scattering ; Nanostructure ; Nanopore ; Nanoparticle ; Thin film ; Analytical Chemistry (journal) ; Optoelectronics ; Molecule ; Chemistry ; Optics ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Nanowire Synthesis and Applications
Authors: Seyed Azim Hosseini | Hossein Maleki Toulabi
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Predicting the compressive strength of sulfur concrete using soft computing techniques Compressive strength ; Aggregate (composite) ; Sulfur ; Soft computing ; Artificial neural network ; Computer science ; Machine learning ; Materials science ; Composite material ; Metallurgy Structural mechanics and materials ; Structural Behavior of Reinforced Concrete ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: V. Vishnu | M.P. Harikrishnan | Aswin S. Warrier | Naveen Kumar Mahanti | M. Basil | T. Venkatesh | R. Pandiselvam | Anjineyulu Kothakota
Source: Journal of Food Process Engineering (article, 2023-02-20)
Field: Materials Science — Polymers and Plastics

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies Abstract Demand for natural fibers as an alternative to synthetic fibers is on the rise. Constraints of current fiber extraction units are limited to single raw material and has low energy efficiency. Hence, this study was taken up for the design and development of a multiple fiber extractor, modelling and optimization of the machine operating parameters, and economic analysis of the developed prototype. The designed machine operates based on the raspador principle. Box Behnken design (BBD) and artificial neural network (ANN) were used to evaluate the performance and optimize the variables of the designed multiple fiber extractor. The input variables included roller speed (550, 735, and 920 rpm), gap/clearance of the roller (2, 3, and 4 mm) and the pitch/distance between the spline slots (0.5, 1.5, and 2.5 mm), while the output variables were yield (kg), capacity (kg/h), energy consumption (kJ), and efficiency (%).The prediction accuracy of quadratic models ( R 2 = 0.962–0.985) developed in BBD were better than the ANN models ( R 2 = 0.831–0.946). The optimized combination was found to be 714.23 rpm roller speed, 3.12 mm gap, and 1.49 mm pitch of the blade, which yielded (1.48 kg) a desirability value of 0.95. The XRD results indicated the highest crystallinity index was recorded for Pineapple leaf fiber compared to banana fiber. SEM results revealed that extracted BF having typical network structure (cellulose, hemicellulose, lignin, and waxes) compared to smooth PLF (lignin and hemicellulose). The fibers extracted using the developed machine was utilized for making biodegradable cutlery. Practical Applications This study was taken up to design a multifiber extractor, to extract fibers from agricultural residues after their useful parts are taken for consumption or processing. Thus, a single machine capable of handling multiple byproducts can utilize those residues, which were previously discarded as waste. Moreover, the extracted fibers are used for the manufacture of biodegradable cutlery. With a small investment, this machine can provide an additional income to the farmers, along with taking away the concern regarding waste disposal. Hemicellulose ; Fiber ; Extraction (chemistry) ; Materials science ; Extractor ; Cellulose ; Lignin ; Pulp and paper industry ; Raw material ; Process engineering ; Mathematics ; Biological system ; Computer science ; Composite material ; Chromatography ; Chemistry ; Chemical engineering ; Engineering Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Agricultural Engineering and Mechanization
Authors: Haotian Wen | David Kordahl | Inga Kuschnerus | Philipp Reineck | Alexander Macmillan | Huan‐Cheng Chang | Christian Dwyer | Shery L. Y. Chang
Source: ACS Nano (article, 2023-08-18)
Field: Materials Science — Materials Chemistry

Correlative Fluorescence and Transmission Electron Microscopy Assisted by 3D Machine Learning Reveals Thin Nanodiamonds Fluoresce Brighter Nitrogen vacancy (NV) centers in fluorescent nanodiamonds (FNDs) draw widespread attention as quantum sensors due to their room-temperature luminescence, exceptional photo- and chemical stability, and biocompatibility. For bioscience applications, NV centers in FNDs offer high-spatial-resolution capabilities that are unparalleled by other solid-state nanoparticle emitters. On the other hand, pursuits to further improve the optical properties of FNDs have reached a bottleneck, with intense debate in the literature over which of the many factors are most pertinent. Here, we describe how substantial progress can be achieved using a correlative transmission electron microscopy and photoluminescence (TEMPL) method that we have developed. TEMPL enables a precise correlative analysis of the fluorescence brightness, size, and shape of individual FND particles. Augmented with machine learning, TEMPL can be used to analyze a large, statistically meaningful number of particles. Our results reveal that FND fluorescence is strongly dependent on particle shape, specifically, that thin, flake-shaped particles are up to several times brighter and that fluorescence increases with decreasing particle sphericity. Our theoretical analysis shows that these observations are attributable to the constructive interference of light waves within the FNDs. Our findings have significant implications for state-of-the-art sensing applications, and they offer potential avenues for improving the sensitivity and resolution of quantum sensing devices. Fluorescence ; Photoluminescence ; Materials science ; Nanotechnology ; Luminescence ; Brightness ; Microscopy ; Nanoparticle ; Quenching (fluorescence) ; Optoelectronics ; Optics ; Physics Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Ion-surface interactions and analysis
Authors: S.S. Shenouda | R. A. Mohamed | E. Baradács | Zoltán Erdélyi
Source: The European Physical Journal Plus (article, 2023-11-16)
Field: Materials Science — Surfaces, Coatings and Films

Prediction and tuning of the optical energy gap and refractive index of amorphous titania-alumina thin films prepared by atomic layer deposition using adaptive neuro-fuzzy inference system model Extrapolation ; Adaptive neuro fuzzy inference system ; Transmittance ; Materials science ; Refractive index ; Thin film ; Band gap ; Deposition (geology) ; Amorphous solid ; Atomic layer deposition ; Optoelectronics ; Optics ; Computer science ; Fuzzy logic ; Nanotechnology ; Mathematics ; Fuzzy control system ; Chemistry ; Artificial intelligence Optical Coatings and Gratings ; Semiconductor materials and devices ; Phase-change materials and chalcogenides
Authors: Sharon A. Ochieng | Konrad Patkowski
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Accurate three-body noncovalent interactions: the insights from energy decomposition An impressive collection of accurate two-body interaction energies for small complexes has been assembled into benchmark databases and used to improve the performance of multiple density functional, semiempirical, and machine learning methods. Similar benchmark data on nonadditive three-body energies in molecular trimers are comparatively scarce, and the existing ones are practically limited to homotrimers. In this work, we present a benchmark dataset of 20 equilibrium noncovalent interaction energies for a small but diverse selection of 10 heteromolecular trimers. The new 3BHET dataset presents complexes that combine different interactions including π-π, anion-π, cation-π, and various motifs of hydrogen and halogen bonding in each trimer. A detailed symmetry-adapted perturbation theory (SAPT)-based energy decomposition of the two- and three-body interaction energies shows that 3BHET consists of electrostatics- and dispersion-dominated complexes. The nonadditive three-body contribution is dominated by induction, but its influence on the overall bonding type in the complex (as exemplified by its position on the ternary diagram) is quite small. We also tested the extended SAPT (XSAPT) approach which is capable of including some nonadditive interactions in clusters of any size. The resulting three-body dispersion term (obtained from the many-body dispersion formalism) is mostly in good agreement with the supermolecular CCSD(T)-MP2 values and the nonadditive induction term is similar to the three-body SAPT(DFT) data, but the overall three-body XSAPT energies are not very accurate as they are missing the first-order exchange terms. Interaction energy ; Non-covalent interactions ; Chemistry ; Chemical physics ; Computational chemistry ; Hydrogen bond ; Statistical physics ; Physics ; Molecule Crystallography and molecular interactions ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Niraj Bhatt | Pravin Karna | Sandip Thakur | Ashutosh Giri
Source: Physical Review Materials (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Transition from electron-dominated to phonon-driven thermal transport in tungsten under extreme pressures By utilizing a combination of first-principles calculations and machine-learning-interatomic-potential-based molecular dynamics simulations, we show that (contrary to the conventional understanding of electron-driven thermal transport in metals) the dominant carriers of heat in tungsten are phonons at high pressures. More specifically, we show that the contribution of phonons to the total thermal conductivity increases monotonically from $\ensuremath{\sim}30%$ at ambient pressure to $\ensuremath{\sim}70%$ at 100 GPa. This is attributed to considerable phonon hardening leading to enhanced phonon lifetimes and group velocities in pressurized tungsten. In contrast to the phonon-driven thermal conductivity, calculations of the electronic thermal conductivity based on density functional perturbation theory calculations of the full electron-phonon coupling matrix show that there is negligible change in the electron-driven thermal conductivity throughout the entire pressure range (up to 100 GPa) studied in this paper. Spectrally resolved electron-phonon coupling at elevated pressures reveals that while phonons are hardened, the peaks of the spectral function are unchanged, thus resulting in negligible variation of the mass enhancement parameter that describes the strength of the overall electron-phonon coupling with increasing pressures. In contrast, the characteristically reduced phonon-phonon scattering, a signature of the isotropic and highly degenerate phonon branches, leads to the phonon-dominated heat conduction in pressurized tungsten. Taken together, our results show that the pressure-driven changes in thermal transport can result in large deviations of the Lorenz number from the traditionally accepted Sommerfeld value in tungsten. Our work reveals an efficient way to separately manipulate the phononic thermal transport from the electronic heat conduction through the application of external pressure in tungsten, and as such can be highly beneficial for applications such as in integrated circuits where tungsten could replace the widely used copper interconnects. Phonon ; Thermal conductivity ; Materials science ; Condensed matter physics ; Thermal conduction ; Tungsten ; Phonon scattering ; Electron ; Physics Thermal properties of materials ; Force Microscopy Techniques and Applications ; High-pressure geophysics and materials