jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Markus Bursch | Jan‐Michael Mewes | Andreas Hansen | Stefan Grimme
Source: ChemRxiv (preprint, 2022-06-28)
Field: Materials Science — Materials Chemistry

Best Practice DFT Protocols for Basic Molecular Computational Chemistry Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share of these quantum-chemical calculations applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological and technical aspects of DFT calculations in three parts: Firstly, we set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible. Secondly, we present a recommendation matrix to guide the choice of functional and basis set depending on the task at hand. A particular focus is on achieving an optimal balance between accuracy, robustness, and efficiency through multi-level approaches. Finally, we discuss selected representative examples to illustrate the recommended protocols and the effect of methodological choices. Computer science ; Density functional theory ; Robustness (evolution) ; Basis set ; Quantum chemical ; Basis (linear algebra) ; Set (abstract data type) ; Protocol (science) ; Quantum chemistry ; Theoretical computer science ; Computational chemistry ; Molecule ; Chemistry ; Mathematics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Chao Qi | Murilo Sandroni | Jesper Cairo Westergaard | Ea Høegh Riis Sundmark | Merethe Bagge | Erik Alexandersson | Junfeng Gao
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

In-Field Early Disease Recognition of Potato Late Blight Based on Deep Learning and Proximal Hyperspectral Imaging Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Computer science ; Pattern recognition (psychology) ; Context (archaeology) ; Feature (linguistics) ; Blight ; Geography ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Chunhui Li | Piotr Zarzycki
Source: Frontiers in Nuclear Engineering (article, 2022-09-15)
Field: Chemistry — Electrochemistry

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases. Sorption ; Electrokinetic phenomena ; Pipeline (software) ; Electrolyte ; Electrostatics ; Computer science ; Range (aeronautics) ; Ion ; Chemistry ; Materials science ; Adsorption ; Nanotechnology Electrochemical Analysis and Applications ; Iron oxide chemistry and applications ; Radioactive element chemistry and processing
Authors: Nabil Hayeemasae | Siriwat Soontaranon | Abdulhakim Masa
Source: eXPRESS Polymer Letters (article, 2022-12-05)
Field: Materials Science — Polymers and Plastics

Influence of different vulcanizing agents on structures and properties of sepiolite-filled natural rubber composites This study aimed to explore the best cross-link agent for preparing natural rubber (NR) composites containing sepiolite as filler. Three types of vulcanizing agents, namely, sulfur, peroxide, and phenolic resin, were employed, and their effects on the thermomechanical and mechanical properties and microstructures were investigated. Compared to other vulcanizing agents, the greatest thermomechanical and mechanical properties improvement was achieved by using phenolic resin as a crosslinker. The highest tensile stress and tensile strength improvement were also achieved from the phenolic resin system. Tensile strength was improved by approximately 78%, nearly twice as much as that in the sulfur system. Such drastic improvement was attributed to the combined effect of homogeneity dispersion and strong adhesion between rubber and filler, as revealed by Fourier-transform infrared spectroscopy, facilitating the strain-induced crystallization process in NR. The phenolic resin was the best vulcanizing agent for preparing NR/sepiolite composites. Vulcanization ; Sepiolite ; Materials science ; Composite material ; Natural rubber ; Polymer science ; Ecology Polymer Nanocomposites and Properties ; Chemistry Education and Research
Authors: Juanjuan Zhu | Xiu Jin | Shaowen Li | Yalu Han | Wenrui Zheng
Source: Computational Intelligence and Neuroscience (article, 2022-08-11)
Field: Chemistry — Analytical Chemistry

Prediction of Soil Available Boron Content in Visible-Near-Infrared Hyperspectral Based on Different Preprocessing Transformations and Characteristic Wavelengths Modeling The trace element boron (Boron, B) is an important factor in crops' development, pollination, and fertilization. Available boron (AB) in soil is the main source of boron nutrient absorption for crops. Rapid detection of AB is of great significance for crop nutrition diagnosis, soil testing and fertilization, precision agriculture development, scientific production management, and guarantee of stable yield and high quality. In this study, we propose a new method to predict soil available boron content using handheld nonimaging hyperspectroscopy in the visible-near-infrared range (350-1655 nm). As boron content is one of the fewest soil chemical elements, a rapid and accurate method has yet to be developed to detect and quantify the soil available boron. Visible-near-infrared ray (VIS-NIR) spectroscopy is widely utilized in the detection and quantification of soil available nutrients. There is, however, scant research on the detection of soil boron based on NIR data, and the performance of current regression model is still far from satisfactory. Our soil samples were collected from southern Anhui, China, with their NIR spectroscopy examined and the NIR data pretreated by 29 transformations and modeled with 10 regression algorithms. Of all the tested methods, SVM_RBF, BPNN, and PLS_RBF algorithms demonstrated the best performance and gave 0.80∼0.82 coefficient of determination value. At the same time, Random Forest algorithm (RFA), Successive Projection Algorithm (SPA), and Variable Importance in Projection (VIP) were used to extract the spectral characteristic wavelength data of soil available boron, and then the characteristic wavelength data were modeled with three regression algorithms: SVM_RBF, PLS_RBF, and BPNN. A comparative analysis of the prediction performance (<i>R</i> <sup>2</sup>, RPD, RMSE, and RPIQ) of the models established at the full band showed that the RFA-MSC/BPNN model achieved the best performance. Compared with the best full-wavelength model DT/SVM_RBF, the test set achieved a 3.06% increase in <i>R</i> <sup>2</sup>, a 7.12% drop in RMSE, a 7.71% gain in RPD, and a 7.78% increase in RPIQ. Our work sheds lights on how to achieve rapid quantification of the soil available boron concentration. Hyperspectral imaging ; Preprocessor ; Infrared ; Wavelength ; Remote sensing ; Content (measure theory) ; Boron ; Near-infrared spectroscopy ; Computer science ; Environmental science ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Materials science ; Optics ; Chemistry ; Mathematics ; Physics ; Optoelectronics ; Geology Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Soil Geostatistics and Mapping
Authors: Tomasz Kostrzewa | Jakub Jończyk | Joanna Drzeżdżon | Dagmara Jacewicz | Magdalena Górska‐Ponikowska | Marcin Kołaczkowski | Alicja Kuban‐Jankowska
Source: International Journal of Molecular Sciences (article, 2022-06-24)
Field: Chemistry — Inorganic Chemistry

Synthesis, In Vitro, and Computational Studies of PTP1B Phosphatase Inhibitors Based on Oxovanadium(IV) and Dioxovanadium(V) Complexes One of the main goals of recent bioinorganic chemistry studies has been to design and synthesize novel substances to treat human diseases. The promising compounds are metal-based and metal ion binding components such as vanadium-based compounds. The potential anticancer action of vanadium-based compounds is one of area of investigation in this field. In this study, we present five oxovanadium(IV) and dioxovanadium(V) complexes as potential PTP1B inhibitors with anticancer activity against the MCF-7 breast cancer cell line, the triple negative MDA-MB-231 breast cancer cell line, and the human keratinocyte HaCaT cell line. We observed that all tested compounds were effective inhibitors of PTP1B, which correlates with anticancer activity. [VO(dipic)(dmbipy)]·2 H<sub>2</sub>O (Compound <b>4</b>) and [VOO(dipic)](2-phepyH)·H<sub>2</sub>O (Compound <b>5</b>) possessed the greatest inhibitory effect, with IC<sub>50</sub> 185.4 ± 9.8 and 167.2 ± 8.0 nM, respectively. To obtain a better understanding of the relationship between the structure of the examined compounds and their activity, we performed a computer simulation of their binding inside the active site of PTP1B. We observed a stronger binding of complexes containing dipicolinic acid with PTP1B. Based on our simulations, we suggested that the studied complexes exert their activity by stabilizing the WPD-loop in an open position and limiting access to the P-loop. Chemistry ; HaCaT ; Bioinorganic chemistry ; IC50 ; In vitro ; Stereochemistry ; Lead compound ; Biochemistry ; Combinatorial chemistry Vanadium and Halogenation Chemistry ; Protein Tyrosine Phosphatases ; Marine Sponges and Natural Products
Authors: Mina Maruyama | Susumu Okada
Source: Journal of Applied Physics (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

All carbon p-n border in bilayer graphene by the molecular orientation of intercalated corannulene Geometric and electronic structures of a corannulene (C20H10) intercalated bilayer graphene are investigated in terms of the molecular conformation using density functional theory. Our calculations indicate that the electronic structure of bilayer graphene is tunable by controlling the molecular conformation of corannulene. Holes and electrons coexist on the upper and lower layers of graphene, which are situated at the convex region and edge of corannulene when it has the bowl conformation. In contrast, bilayer graphene has a tiny gap of 4.7 meV at the K point owing to the substantial interaction between graphene and corannulene when corannulene has flat conformation. Electron and hole redistribution in bilayer graphene intercalating corannulene indicated the possibility of all carbon p-n border at an interface between corannulene with convex and concave arrangements. The intercalation substantially decreases the energy difference between the ground state bowl conformation and the metastable state flat conformation by approximately 400 meV. Accordingly, the two-dimensional nano-spacing between the graphene layers changes the molecular conformation of corannulene from a bowl to a flat structure at 139 MPa. Corannulene ; Graphene ; Bilayer graphene ; Bilayer ; Intercalation (chemistry) ; Crystallography ; Chemical physics ; Chemistry ; Fullerene ; Materials science ; Nanotechnology ; Organic chemistry Graphene research and applications ; Carbon Nanotubes in Composites ; Topological Materials and Phenomena
Authors: Joshua T. Horton | Simon Boothroyd | Jeffrey Wagner | Joshua Mitchell | Trevor Gokey | David Dotson | Pavan Kumar Behara | Venkata Krishnan Ramaswamy | Mark Mackey | John D. Chodera | Jamshed Anwar | David L. Mobley | Daniel J. Cole
Source: ChemRxiv (preprint, 2022-09-08)
Field: Materials Science — Materials Chemistry

Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization At Scale The development of accurate transferable force fields is key to realizing the full potential of atomistic modelling in the study of biological processes such as protein--ligand binding for drug discovery. State-of-the-art transferable force fields, such as those produced by the Open Force Field Initiative, use modern software engineering and automation techniques to progressively yield accuracy improvements. However, force field torsion parameters, which must effectively account for many stereoelectronic and steric effects, are generally considered to be less transferable than other force field parameters and are therefore often targets for bespoke parametrization. Here, we present the Open Force Field QCSubmit and BespokeFit software packages that, when combined, facilitate the fitting of torsion parameters to quantum mechanical reference data at scale. We demonstrate the use of QCSubmit for simplifying the process of creating and archiving large numbers of quantum chemical calculations, by generating a dataset of 671 torsion scans for drug-like fragments. We use BespokeFit to derive individual torsion parameters for each of these molecules, thereby reducing the root mean squared error in the potential energy surface from 1.1 kcal/mol, using the original transferable force field, to 0.4 kcal/mol using the bespoke version. Furthermore, we employ the bespoke force fields to compute the relative binding free energies of a congeneric series of inhibitors of the TYK2 protein, and demonstrate further improvements in accuracy, compared to the base force field (MUE reduced from 0.56$^{0.77}_{0.39}$ to 0.42$^{0.59}_{0.28}$ kcal/mol and R$^2$ correlation improved from 0.72$^{0.87}_{0.35}$ to 0.93$^{0.97}_{0.84}$). Bespoke ; Force field (fiction) ; Torsion (gastropod) ; Computer science ; Software ; Parametrization (atmospheric modeling) ; Steric effects ; Chemistry ; Physics ; Artificial intelligence ; Stereochemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Jie He | Yongke Sun | Yu Chunjiang | Yong Cao | Youjie Zhao | Guanben Du
Source: Forests (article, 2022-08-25)
Field: Chemistry — Organic Chemistry

An Improved Wood Recognition Method Based on the One-Class Algorithm Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization. Support vector machine ; Generalization ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Class (philosophy) ; Recall ; Feature extraction ; Feature (linguistics) ; Machine learning ; Mathematics ; Psychology Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Zhenyu ZHANG | Qinggang LI | Jun Ji | Yingying CHEN | Deli Ma | Sique CHEN | Hailing Yang | Guopu SHI | Zhi Wang | Mengyong Sun | Fei Chen | Shifeng Huang
Source: Journal of Advanced Ceramics (article, 2022-12-23)
Field: Materials Science — Materials Chemistry

Formation mechanisms of Ti <sub>3</sub>(Si,Al)C <sub>2</sub>/Al <sub>2</sub>O <sub>3</sub> composites from Ti <sub>3</sub>AlC <sub>2</sub> and SiO via low-temperature sintering Ti<sub>3</sub>SiC<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> composites have attracted attention due to their excellent mechanical and electromagnetic properties, but the high temperatures (≥ 1400 ℃) required for the densification of aluminum oxide (Al<sub>2</sub>O<sub>3</sub>) leads to the decomposition of Ti<sub>3</sub>SiC<sub>2</sub>. To address this issue, Ti<sub>3</sub>(Si<sub><i>x</i></sub>Al<sub>1−<i>x</i></sub>)C<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> (<i>x</i> represents the Si content) composites were synthesized for the first time via hot-pressing (HP) sintering and current-assisted sintering (CAS) of mixed Ti<sub>3</sub>AlC<sub>2</sub> and silicon monoxide (SiO) powders at 1300 and 1200 ℃, respectively. Both approaches produced composites with <i>x</i> values greater than 0.9, indicating that the compositions of the prepared composites were similar to those of Ti<sub>3</sub>SiC<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> composites. The synthetic mechanism involved substitution and continuous interdiffusion of Al and Si atoms. The composite prepared by CAS at 1200 ℃ was compacted, whereas the composite prepared by HP had a low density. The low-temperature densification mechanism is attributed to the combined effects of amorphous SiO, liquid Al, and the high heating rates for CAS. The flexural strength and hardness of the composite prepared by CAS were also comparable to those of compacted Ti<sub>3</sub>SiC<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> composites. Materials science ; Sintering ; Hot pressing ; Flexural strength ; Composite material ; Aluminium oxides MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Advanced materials and composites
Authors: Qiang Wang | Shao‐Xiang Go | Chen Liu | Minghua Li | Yao Zhu | Lunna Li | Tae Hoon Lee | Desmond K. Loke
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Computational understanding role of vacancies and distortions in wurtzite ferroelectric memory materials: implications for device miniaturization Herein, we demonstrate that the stable AlScN model with wurtzite structure is described by small/negligible vacancy concentrations and minimal localized distortions. Wurtzite crystal structure ; Ferroelectricity ; Miniaturization ; Vacancy defect ; Materials science ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Physics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Photorefractive and Nonlinear Optics
Authors: Peng Yuan | Lei Pang | Lianming Wang | Lei Yan
Source: International Food Research Journal (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging to discriminate waxy corn seed vigour after aging A hyperspectral imaging system covering 400 - 1000 nm spectral range was applied for vigour detection of waxy maize seeds after artificial aging. After spectral pre-processing, the characteristic wavelength was selected by uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random frog (RF) methods. The moisture, starch, protein, and fat contents were measured for each grade of seed, and these values were correlated with the spectrum. Finally, the vitality detection model was established by least squares support vector machine (LS-SVM), extreme learning machine (ELM), and random forest (RF). The prediction sets exhibited high classification accuracy (&gt; 99%) for 115 features. The model constructed from the bands significantly correlated with chemical composition (CC), and was better than the classic feature selection methods. The overall results indicated that hyperspectral imaging could be a potential technique to assess seed vigour. Hyperspectral imaging ; Random forest ; Support vector machine ; Feature selection ; Extreme learning machine ; Pattern recognition (psychology) ; Artificial intelligence ; Biological system ; Starch ; Partial least squares regression ; Least squares support vector machine ; Mathematics ; Computer science ; Chemistry ; Food science ; Statistics ; Biology ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis