jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lifei Wei | Haochen Pu | Zhengxiang Wang | Ziran Yuan | Xinru Yan | Liqin Cao
Source: Sensors (article, 2020-07-21)
Field: Chemistry — Analytical Chemistry

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content. Hyperspectral imaging ; Arsenic ; Environmental science ; Partial least squares regression ; Remote sensing ; Computer science ; Soil science ; Artificial intelligence ; Chemistry ; Machine learning ; Geology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote-Sensing Image Classification
Authors: Derek P. Metcalf | Alexios Koutsoukas | Steven A. Spronk | Brian L. Claus | Deborah A. Loughney | Stephen R. Johnson | Daniel L. Cheney | C. David Sherrill
Source: The Journal of Chemical Physics (article, 2020-02-19)
Field: Materials Science — Materials Chemistry

Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory Accurate prediction of intermolecular interaction energies is a fundamental challenge in electronic structure theory due to their subtle character and small magnitudes relative to total molecular energies. Symmetry adapted perturbation theory (SAPT) provides rigorous quantum mechanical means for computing such quantities directly and accurately, but for a computational cost of at least O(N<sup>5</sup>), where N is the number of atoms. Here, we report machine learned models of SAPT components with a computational cost that scales asymptotically linearly, O(N). We use modified multi-target Behler-Parrinello neural networks and specialized intermolecular symmetry functions to address the idiosyncrasies of the intermolecular problem, achieving 1.2 kcal mol<sup>-1</sup> mean absolute errors on a test set of hydrogen bound complexes including structural data extracted from the Cambridge Structural Database and Protein Data Bank, spanning an interaction energy range of 20 kcal mol<sup>-1</sup>. Additionally, we recover accurate predictions of the physically meaningful SAPT component energies, of which dispersion and induction/polarization were the easiest to predict and electrostatics and exchange-repulsion are the most difficult. Intermolecular force ; Perturbation theory (quantum mechanics) ; Quantum ; Artificial neural network ; Statistical physics ; Chemistry ; Molecule ; Physics ; Chemical physics ; Quantum mechanics ; Atomic physics ; Molecular physics ; Computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yifan Wang | Yaqiong Su | Emiel J. M. Hensen | Dionisios G. Vlachos
Source: ACS Nano (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred<i>via</i>Statistical Learning and Genetic Algorithm-Based Optimization Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pd<sub><i>n</i></sub> (<i>n</i> = 1-21) clusters supported on CeO<sub>2</sub>(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pd<sub><i>n</i></sub> clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime. Cluster (spacecraft) ; Density functional theory ; Atom (system on chip) ; Metastability ; Materials science ; Cluster expansion ; Stability (learning theory) ; Chemical physics ; Hamiltonian (control theory) ; Statistical physics ; Monte Carlo method ; Nanotechnology ; Computational chemistry ; Physics ; Computer science ; Chemistry ; Thermodynamics ; Machine learning ; Quantum mechanics ; Mathematics ; Mathematical optimization Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Nuria García‐Moncada | G.J. van Rooij | Toine Cents | Leon Lefferts
Source: Catalysis Today (article, 2020-04-13)
Field: Materials Science — Materials Chemistry

Catalyst-assisted DBD plasma for coupling of methane: Minimizing carbon-deposits by structured reactors Catalysis ; Methane ; Oxidative coupling of methane ; Chemistry ; Layer (electronics) ; Chemical engineering ; Carbon fibers ; Heterogeneous catalysis ; Packed bed ; Selectivity ; Inorganic chemistry ; Materials science ; Chromatography ; Organic chemistry ; Composite material Catalytic Processes in Materials Science ; Plasma Applications and Diagnostics ; Catalysis and Oxidation Reactions
Authors: Xingang Zhao | Zhiming Shi | Xinjiang Wang | Hongshuai Zou | Yuhao Fu | Lijun Zhang
Source: InfoMat (article, 2020-10-04)
Field: Materials Science — Materials Chemistry

Band structure engineering through van der Waals heterostructing superlattices of <scp>two‐dimensional</scp> transition metal dichalcogenides Abstract The indirect‐to‐direct band‐gap transition in transition metal dichalcogenides (TMDCs) from bulk to monolayer, accompanying with other unique properties of two‐dimensional materials, has endowed them great potential in optoelectronic devices. The easy transferability and feasible epitaxial growth pave a promising way to further tune the optical properties by constructing van der Waals heterostructures. Here, we performed a systematic high‐throughput first‐principles study of electronic structure and optical properties of the layer‐by‐layer stacking TMDCs heterostructing superlattices, with the configuration space of [(MX 2 ) n (M′X′ 2 ) 10− n ] (M/M′ = Cr, Mo, W; X/X′ = S, Se, Te; n = 0‐10). Our calculations involving long‐range dispersive interaction show that the indirect‐to‐direct band‐gap transition or even semiconductor‐to‐metal transition can be realized by changing component compositions of superlattices. Further analysis indicates that the indirect‐to‐direct band‐gap transition can be ascribed to the in‐plane strain induced by lattice mismatch. The semiconductor‐to‐metal transition may be attributed to the band offset among different components that is modified by the in‐plane strain. The superlattices with direct band‐gap show quite weak band‐gap optical transition because of the spacial separation of the electronic states involved. In general, the layers stacking‐order of superlattices results in a small up to 0.2 eV band gap fluctuation because of the built‐in potential. Our results provide useful guidance for engineering band structure and optical properties in TMDCs heterostructing superlattices. image Superlattice ; Band offset ; Condensed matter physics ; Direct and indirect band gaps ; Stacking ; Heterojunction ; Band gap ; Monolayer ; Semiconductor ; Materials science ; van der Waals force ; Transition metal ; Electronic band structure ; Electronic structure ; Optoelectronics ; Chemistry ; Nanotechnology ; Physics ; Molecule 2D Materials and Applications ; MXene and MAX Phase Materials ; Ga2O3 and related materials
Authors: Naozumi Hiranuma | Hahnbeom Park | Minkyung Baek | Ivan Anishchanka | Justas Dauparas | David Baker
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-07-19)
Field: Materials Science — Materials Chemistry

Improved protein structure refinement guided by deep learning based accuracy estimation Abstract We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules. Maxima and minima ; Computer science ; Protein structure prediction ; Network structure ; Algorithm ; Deep learning ; Protein structure ; Artificial intelligence ; Data mining ; Machine learning ; Mathematics ; Chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Mario F. Gómez-Núñez | Mariel Castillo-López | Fernando Sevilla-Castillo | Oscar J. Roque-Reyes | Fernanda Romero-Lechuga | Diana Medina-Santos | R Daniel | Alberto N. Peón
Source: Frontiers in Microbiology (review, 2020-11-25)
Field: Materials Science — Materials Chemistry

Nanoparticle-Based Devices in the Control of Antibiotic Resistant Bacteria Antimicrobial resistance (AR) is one of the most important public health challenges worldwide as it represents a serious complication that is able to increase the mortality, morbidity, disability, hospital stay and economic burden related to infectious diseases. As such, the spread of AR-pathogens must be considered as an emergency, and interdisciplinary approaches must be undertaken in order to develop not only drugs, but holistic strategies to undermine the epidemic and pathogenic potentials of multi-drug resistant (MDR) pathogens. One of such approaches has focused on the use of antimicrobial nanoparticles (ANPs), as they have demonstrated to possess strong antimicrobial effects on MDR pathogens. On the other hand, the ability of bacteria to develop resistance to such agents is minimal. In this way, ANPs may seem a good choice for the development of new drugs, but there is no certainty about their safety, which may delay its translation to the clinical setting. As MDR pathogens are quickly becoming more prevalent and drug development is slow and expensive, there is an increasing need for the rapid development of new strategies to control such agents. We hereby explore the possibility of designing ANP-based devices such as surgical masks and fabrics, wound dressings, catheters, prostheses, dentifrices, water filters, and nanoparticle-coated metals to exploit the potential of such materials in the combat of MDR pathogens, with a good potential for translation into the clinical setting. Antimicrobial ; Antibiotic resistance ; Intensive care medicine ; Medicine ; Antibiotics ; Infection control ; Drug resistance ; Drug ; Risk analysis (engineering) ; Biology ; Microbiology ; Pharmacology Nanoparticles: synthesis and applications ; Bacterial biofilms and quorum sensing ; Antimicrobial agents and applications
Authors: Filip Leonarski | A. Mozzanica | Martin Brückner | C. Lopez-Cuenca | S. Redford | L. Sala | Andrej Babič | H. Billich | Oliver Bunk | B. Schmitt | Meitian Wang
Source: Structural Dynamics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

JUNGFRAU detector for brighter x-ray sources: Solutions for IT and data science challenges in macromolecular crystallography In this paper, we present a data workflow developed to operate the adJUstiNg Gain detector FoR the Aramis User station (JUNGFRAU) adaptive gain charge integrating pixel-array detectors at macromolecular crystallography beamlines. We summarize current achievements for operating at 9 GB/s data-rate a JUNGFRAU with 4 Mpixel at 1.1 kHz frame-rate and preparations to operate at 46 GB/s data-rate a JUNGFRAU with 10 Mpixel at 2.2 kHz in the future. In this context, we highlight the challenges for computer architecture and how these challenges can be addressed with innovative hardware including IBM POWER9 servers and field-programmable gate arrays. We discuss also data science challenges, showing the effect of rounding and lossy compression schemes on the MX JUNGFRAU detector images. Detector ; Context (archaeology) ; Computer science ; Workflow ; X-ray detector ; Pixel ; IBM ; Frame rate ; Computer hardware ; Physics ; Electrical engineering ; Optics ; Engineering ; Artificial intelligence ; Telecommunications ; Database Enzyme Structure and Function ; Particle Detector Development and Performance ; Advanced Electron Microscopy Techniques and Applications
Authors: James L. Webb | Luca Troise | Nikolaj Winther Hansen | Jocelyn Achard | Ovidiu Brinza | Robert Staacke | Michael Kieschnick | Jan Meijer | Jean-François Perrier | Kirstine Berg‐Sørensen | Alexander Huck | Ulrik L. Andersen
Source: Frontiers in Physics (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Optimization of a Diamond Nitrogen Vacancy Centre Magnetometer for Sensing of Biological Signals Sensing of signals from biological processes, such as action potential propagation in nerves, are essential for clinical diagnosis and basic understanding of physiology. Sensing can be performed electrically by placing sensor probes near or inside a living specimen or dissected tissue using well established electrophysiology techniques. However, these electrical probe techniques have poor spatial resolution and cannot easily access tissue deep within a living subject, in particular within the brain. An alternative approach is to detect the magnetic field induced by the passage of the electrical signal, giving the equivalent readout without direct electrical contact. Such measurements are performed today using bulky and expensive superconducting sensors with poor spatial resolution. An alternative is to use nitrogen vacancy (NV) centres in diamond that promise biocompatibilty and high sensitivity without cryogenic cooling. In this work we present advances in biomagnetometry using NV centres, demonstrating magnetic field sensitivity of approximately 100 pT/sqrt(Hz) in the DC/low frequency range using a setup designed for biological measurements. Biocompatibility of the setup with a living sample (mouse brain slice) is studied and optimized, and we show work toward sensitivity improvements using a pulsed magnetometry scheme. In addition to the bulk magnetometry study, systematic artifacts in NV-ensemble widefield fluorescence imaging are investigated. Magnetometer ; Diamond ; Vacancy defect ; Nitrogen ; Materials science ; Environmental science ; Optoelectronics ; Physics ; Nuclear magnetic resonance ; Magnetic field ; Metallurgy Diamond and Carbon-based Materials Research ; Atomic and Subatomic Physics Research ; Analytical Chemistry and Sensors
Authors: Xiulin Bai | Chu Zhang | Qinlin Xiao | Yong He | Yidan Bao
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFNN model over 86%. Second, the seeds of silage maize and common maize were firstly classified with the classification accuracy over 88%, then the seeds were classified into four varieties, respectively. The classification accuracy of silage maize seeds was over 98%, and the classification accuracy of common maize seeds was over 97%. The results showed that the varieties of common maize seeds and silage maize seeds could be classified by NIR-HSI combined with chemometrics, which provided an effective means to ensure the purity of maize seeds, especially to isolate common seeds and silage seeds. Silage ; Hyperspectral imaging ; Chemometrics ; Principal component analysis ; Mathematics ; Agronomy ; Pattern recognition (psychology) ; Artificial intelligence ; Biology ; Machine learning ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ning Liu | Zizheng Xing | Ruomei Zhao | Lang Qiao | Minzan Li | Gang Liu | Hong Sun
Source: Remote Sensing (article, 2020-08-31)
Field: Chemistry — Analytical Chemistry

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops. Correlation coefficient ; Mean squared error ; Calibration ; Partial least squares regression ; Coefficient of determination ; Wavelet ; Mathematics ; Remote sensing ; Environmental science ; Biological system ; Statistics ; Computer science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Muhammad Waleed | Tai‐Won Um | Aftab Khan | Umair Khan
Source: Remote Sensing (article, 2020-02-26)
Field: Chemistry — Analytical Chemistry

Automatic Detection System of Olive Trees Using Improved K-Means Algorithm Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree count and crop yield is of high significance. Manual counting of trees over such large areas is humanly infeasible. To address this problem, we propose an automatic method for the detection and enumeration of olive trees. The algorithm is a multi-step classification system comprising pre-processing, image segmentation, feature extraction, and classification. RGB satellite images were acquired from the Spanish territory and pre-processed to suppress the additive noise. The region of interest was then segmented from the pre-processed images using K-Means segmentation, through which statistical features were extracted and classified. Promising results were achieved for all classifiers, namely Naive Bayesian, Support Vector Machines (SVMs), Random Forest and Multi-Layer Perceptrons (MLPs), at various division ratios of data samples. In a comparison of all the classification algorithms, Random Forest outperformed the rest by an overall accuracy of 97.5% at the division ratio of 70 to 30 for training to testing. Computer science ; Artificial intelligence ; Perceptron ; Random forest ; Support vector machine ; Image segmentation ; Tree (set theory) ; Segmentation ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Artificial neural network Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI