jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhihao Lü | Shihao Huang | Xiaojun Zhang | Yuxuan shi | Wanneng Yang | Longfu Zhu | Chenglong Huang
Source: Plant Methods (article, 2023-07-29)
Field: Chemistry — Analytical Chemistry

Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion The study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt. Artificial intelligence ; Pattern recognition (psychology) ; Feature extraction ; Support vector machine ; Computer science ; Feature (linguistics) ; RGB color model ; Principal component analysis ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Hong Liu | Honggao Liu | Jieqing Li | Yuanzhong Wang
Source: ACS Omega (article, 2023-05-23)
Field: Chemistry — Analytical Chemistry

Rapid and Accurate Authentication of Porcini Mushroom Species Using Fourier Transform Near-Infrared Spectra Combined with Machine Learning and Chemometrics Porcini mushrooms have high nutritional value and great potential, but different species are easily confused, so it is essential to identify them rapidly and precisely. The diversity of nutrients in stipe and cap will lead to differences in spectral information. In this research, Fourier transform near-infrared (FT-NIR) spectral information about imparity species of porcini mushroom stipe and cap was collected and combined into four data matrices. FT-NIR spectra of four data sets were combined with chemometric methods and machine learning for accurate evaluation and identification of different porcini mushroom species. From the results: (1) improved visualization level of t-distributed stochastic neighbor embedding (t-SNE) results after the second derivative preprocessing compared with raw spectra; (2) after using multiple pretreatment combinations to process the four data matrices, the model accuracies based on support vector machine and partial least-square discriminant analysis (PLS-DA) under the best preprocessing method were 98.73-99.04% and 98.73-99.68%, respectively; (3) by comparing the modeling results of FT-NIR spectra with different data matrices, it was found that the PLS-DA model based on low-level data fusion has the highest accuracy (99.68%), but residual neural network (ResNet) model based on the stipe, cap, and average spectral data matrix worked better (100% accuracy). The above results suggest that distinct models should be selected for dissimilar spectral data matrices of porcini mushrooms. Additionally, FT-NIR spectra have the advantages of being nondevastate and fast; this method is expected to be a promising analytical tool in food safety control. Chemometrics ; Stipe (mycology) ; Pattern recognition (psychology) ; Hyperspectral imaging ; Artificial intelligence ; Mathematics ; Data pre-processing ; Fourier transform ; Biological system ; Computer science ; Machine learning ; Botany Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Bee Products Chemical Analysis
Authors: Andreas Erbs Hillers‐Bendtsen | Jacob Lynge Elholm | Oscar Berlin Obel | Helen Hölzel | Kasper Moth‐Poulsen | Kurt V. Mikkelsen
Source: Angewandte Chemie International Edition (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Searching the Chemical Space of Bicyclic Dienes for Molecular Solar Thermal Energy Storage Candidates Photoswitches are molecular systems that are chemically transformed subsequent to interaction with light and they find potential application in many new technologies. The design and discovery of photoswitch candidates require intricate molecular engineering of a range of properties to optimize a candidate to a specific applications, a task which can be tackled efficiently using quantum chemical screening procedures. In this paper, we perform a large scale screening of approximately half a million bicyclic diene photoswitches in the context of molecular solar thermal energy storage using ab initio quantum chemical methods. We further device an efficient strategy for scoring the systems based on their predicted solar energy conversion efficiency and elucidate potential pitfalls of this approach. Our search through the chemical space of bicyclic dienes reveals systems with unprecedented solar energy conversion efficiencies and storage densities that show promising design guidelines for next generation molecular solar thermal energy storage systems. Photoswitch ; Context (archaeology) ; Chemical space ; Bicyclic molecule ; Energy transformation ; Quantum chemical ; Energy storage ; Solar energy ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Molecule ; Organic chemistry ; Drug discovery ; Thermodynamics ; Engineering Photochromic and Fluorescence Chemistry ; Molecular Junctions and Nanostructures ; Porphyrin and Phthalocyanine Chemistry
Authors: Shuo Wang | Yunsheng Liu | Yifei Mo
Source: Angewandte Chemie (article, 2023-02-07)
Field: Materials Science — Materials Chemistry

Frustration in Super‐Ionic Conductors Unraveled by the Density of Atomistic States Abstract The frustration in super‐ionic conductors enables their exceptionally high ionic conductivities, which are desired for many technological applications including batteries and fuel cells. A key challenge in the study of frustration is the difficulties in analyzing a large number of disordered atomistic configurations. Using lithium super‐ionic conductors as model systems, we propose and demonstrate the density of atomistic states (DOAS) analytics to quantitatively characterize the onset and degree of disordering, reveal the energetics of local disorder, and elucidate how the frustration enhances diffusion through the broadening and overlapping of the energy levels of atomistic states. Furthermore, material design strategies aided by the DOAS are devised and demonstrated for new super‐ionic conductors. The DOAS is generally applicable analytics for unraveling fundamental mechanisms in complex atomistic systems and guiding material design. Frustration ; Ionic bonding ; Electrical conductor ; Lithium (medication) ; Chemical physics ; Diffusion ; Ionic conductivity ; Nanotechnology ; Ion ; Materials science ; Chemistry ; Physics ; Condensed matter physics ; Physical chemistry ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Fabienne Schwab | Barbara Rothen‐Rutishauser | Aline Scherz | Thierry Meyer | Bedia Begüm Karakoçak | Alke Petri‐Fink
Source: Nature Nanotechnology (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

The need for awareness and action in managing nanowaste Action (physics) ; Computer science ; Nanotechnology ; Internet privacy ; Business ; Materials science ; Physics Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Recycling and Waste Management Techniques
Authors: Manuel Henrich | Niklas Fehlemann | Felix Bexter | Maximilian Neite | Linghao Kong | Fuhui Shen | Markus Könemann | Michael Dölz | Sebastian Münstermann
Source: Heliyon (article, 2023-08-01)
Field: Materials Science — Metals and Alloys

DRAGen – A deep learning supported RVE generator framework for complex microstructure models In this study an improved version of the Discrete RVE Automation and Generation Framework, also called DRAGen, is presented. The Framework incorporates a generator for Representative Volume Elements (RVEs). Several complex microstructure features, extracted from real microstructures, have been added to the generator, to enable it to generate RVEs with realistic microstructures. DRAGen is now capable of reading trained neural networks as well as .csv-files as input data for the microstructure generation. Furthermore, features such as pores and inclusions, martensite bands, hierarchical substructures, and crystallographic textures can be reconstructed in the RVEs. Besides the features, the functionality for different solvers was introduced. Therefore, the code was extended by modules for the generation of Finite Element (FE) and spectral solver input files. DRAGen now has the ability to create models for three powerful multiphysics frameworks used in the community: DAMASK, Abaqus and MOOSE. The evaluation of the features, as well as the simulations performed on sample models, show that the new version of DRAGen is a very powerful tool with flexible applicability for scientists in the ICME community. Also, due to the modular architecture of the project, the code can easily be expanded with features of interest. Therefore, it delivers a variety of functions and possible outputs, which offers researchers a broad spectrum of microstructures that can be used in microstructure studies or microstructure design developments. Multiphysics ; Computer science ; Computational science ; Microstructure ; Modular design ; Generator (circuit theory) ; Finite element method ; Solver ; Code generation ; Code (set theory) ; Mechanical engineering ; Materials science ; Engineering ; Structural engineering ; Programming language ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and mechanical properties
Authors: Benjamin William Kaufold | Nithin Chintala | Pratima Pandeya | Sijia S. Dong
Source: Journal of Chemical Theory and Computation (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Automated Active Space Selection with Dipole Moments Multireference calculations can provide accurate information of systems with strong correlation, which have increasing importance in the development of new molecules and materials. However, selecting a suitable active space for multireference calculations is nontrivial, and the selection of an unsuitable active space can sometimes lead to results that are not physically meaningful. Active space selection often requires significant human input, and the selection that leads to reasonable results often goes beyond chemical intuition. In this work, we have developed and evaluated two protocols for automated selection of the active space for multireference calculations based on a simple physical observable, the dipole moment, for molecules with nonzero ground-state dipole moments. One protocol is based on the ground-state dipole moment, and the other is based on the excited-state dipole moments. To evaluate the protocols, we constructed a dataset of 1275 active spaces from 25 molecules, each with 51 active space sizes considered, and have mapped out the relationship between the active space, dipole moments, and vertical excitation energies. We have demonstrated that, within this dataset, our protocols allow one to choose among a number of accessible active spaces one that is likely to give reasonable vertical excitation energies, especially for the first three excitations, with no parameters manually decided by the user. We show that, with large active spaces removed from consideration, the accuracy is similar and the time-to-solution can be reduced by more than 10 fold. We also show that the protocols can be applied to potential energy surface scans and determining the spin states of transition metal oxides. Dipole ; Excitation ; Computer science ; Moment (physics) ; Excited state ; Observable ; Physics ; Statistical physics ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Erdal Mutlu | Ajay Panyala | Nitin Gawande | Abhishek Bagusetty | Jeffrey Glabe | Jinsung Kim | Karol Kowalski | Nicholas P. Bauman | Bo Peng | Himadri Pathak | Jiří Brabec | Sriram Krishnamoorthy
Source: The Journal of Chemical Physics (article, 2023-07-10)
Field: Chemistry — Spectroscopy

TAMM: Tensor algebra for many-body methods Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks. Computer science ; Scalability ; Tensor (intrinsic definition) ; Computational science ; Tensor algebra ; Theoretical computer science ; Computation ; Graphics ; Scheduling (production processes) ; Modular design ; Distributed computing ; Algebra over a field ; Mathematical optimization ; Algorithm ; Mathematics ; Algebra representation ; Programming language Advanced NMR Techniques and Applications ; Advanced Data Storage Technologies ; Tensor decomposition and applications
Authors: Sandip Garai | Ranjit Kumar Paul | Debopam Rakshit | Md Yeasin | Walid Emam | Yusra Tashkandy | Christophe Chesneau
Source: Mathematics (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models. Wavelet ; Haar wavelet ; Computer science ; Benchmark (surveying) ; Discrete wavelet transform ; Wavelet packet decomposition ; Filter (signal processing) ; Wavelet transform ; Artificial intelligence ; Econometrics ; Machine learning ; Mathematics ; Statistics ; Pattern recognition (psychology) ; Geography ; Computer vision Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods ; Blind Source Separation Techniques
Authors: Hagen Neugebauer | Benedikt Bädorf | Sebastian Ehlert | Andreas Hansen | Stefan Grimme
Source: Journal of Computational Chemistry (article, 2023-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐throughput screening of spin states for transition metal complexes with spin‐polarized extended tight‐binding methods The semiempirical GFNn-xTB ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn></mml:math> ) tight-binding methods are extended with a spin-dependent energy term (spin-polarization), enabling the fast and efficient screening of different spin states for transition metal complexes. While GFNn-xTB methods inherently can not differentiate properly between high-spin (HS) and low-spin (LS) states, this shortcoming is corrected with the presented methods termed spGFNn-xTB. The performance of spGFNn-xTB methods for spin state energy splittings is evaluated on a newly compiled benchmark set of 90 complexes (27 HS and 63 LS complexes) containing 3d, 4d, and 5d transition metals (termed TM90S) employing DFT references at the TPSSh-D4/def2-QZVPP level of theory. The challenging TM90S set contains complexes with charges between <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>-</mml:mo></mml:math> 4 and +3, spin multiplicities between 1 and 6, and spin-splitting energies that range from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>-</mml:mo></mml:math> 47.8 to 146.6 kcal/mol with a mean average of 32.2 kcal/mol. On this set the (sp)GFNn-xTB methods, the PM6-D3H4 method, and the PM7 method are evaluated with spGFN1-xTB yielding the lowest MAD of 19.6 kcal/mol followed by spGFN2-xTB with 24.8 kcal/mol. While for the 4d and 5d subsets small or no improvements are observed with spin-polarization, large improvements are obtained for the 3d subset with spGFN1-xTB yielding the smallest MAD of 14.2 kcal/mol followed by spGFN2-xTB with 17.9 kcal/mol and PM6-D3H4 with 28.4 kcal/mol. The correct sign of the spin state splittings is obtained with spGFN2-xTB in 89% of all cases closely followed by spGFN1-xTB with 88%. On the full set, a pure semiempirical vertical spGFN2-xTB//GFN2-xTB-based workflow for screening purposes yields a slightly better MAD of 22.2 kcal/mol due to error compensation, while being qualitative correct for one additional case. In combination with their low computational cost (scanning spin states in seconds), the spGFNn-xTB methods represent robust tools for pre-screening steps of spin state calculations and high-throughput workflows. Spin states ; Spin (aerodynamics) ; Spin polarization ; Transition metal ; Chemistry ; Binding energy ; Metal ; Atomic physics ; Computational chemistry ; Physics ; Inorganic chemistry ; Thermodynamics ; Quantum mechanics Magnetism in coordination complexes ; Advanced Chemical Physics Studies ; Electron Spin Resonance Studies
Authors: Renan Falcioni | Werner Camargos Antunes | José Alexandre Melo Demattê | Marcos Rafael Nanni
Source: Biology (article, 2023-05-11)
Field: Chemistry — Analytical Chemistry

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in <i>Codiaeum variegatum</i> (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques. Biology ; Kinetics ; Fluorescence ; Chlorophyll fluorescence ; Reflectivity ; Chlorophyll ; Botany ; Photochemistry ; Biophysics ; Optics Spectroscopy and Chemometric Analyses ; Light effects on plants ; Seed and Plant Biochemistry
Authors: Giulia Gorla | Paolo Taborelli | Hawbeer Jamal Ahmed | Cristina Alamprese | Silvia Grassi | Ricard Boqué | Jordi Riu | Barbara Giussani
Source: Chemosensors (article, 2023-03-09)
Field: Chemistry — Analytical Chemistry

Miniaturized NIR Spectrometers in a Nutshell: Shining Light over Sources of Variance The increasing portability and accessibility of miniaturized NIR spectrometers are promoting the spread of in-field and online applications. Alongside the successful outcomes, there are also several problems related to the acquisition strategies for each instrument and to experimental factors that can influence the collected signals. An insightful investigation of such factors is necessary and could lead to advancements in experimental set-up and data modelling. This work aimed to identify variation sources when using miniaturized NIR sensors and to propose a methodology to investigate such sources based on a multivariate method (ANOVA—Simultaneous Component Analysis) that considers the effects and interactions between them. Five different spectrometers were chosen for their different spectroscopic range and technical characteristics, and samples of worldwide interest were chosen as the case study. Comparing various portable sensors is interesting since results could significantly vary in the same application, justifying the idea that this kind of spectrometer is not to be treated as a general class of instruments. Software portability ; Spectrometer ; Computer science ; Variance (accounting) ; Multivariate analysis of variance ; Multivariate statistics ; Range (aeronautics) ; Materials science ; Optics ; Physics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research