jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sarah Coffland | Katie Christensen | Filip Jagodzinski | Brian Hutchinson
Source:  (preprint, 2023-09-03)
Field: Materials Science — Materials Chemistry

RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequently, modeling InDels computationally can supplement and inform wet lab experiments. In this work, we make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy of 0.775 over all Rosetta scores for the largest protein. Furthermore, a sensitivity analysis is performed to determine the necessary quantity of data required to accurately emulate the structural scores for computationally generated mutants. We show that the model can be trained on minimal data (< 50%) and still retain a high level of accuracy. Indel ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Deep learning ; Machine learning ; Statistics ; Biology ; Genetics ; Mathematics ; Gene Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Genomics and Chromatin Dynamics
Authors: Guohua Zhou | Yao Li | Yu-Meng Zeng | Le Yu | Li‐Lan Tian
Source: Crystals (article, 2023-08-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrically Tunable Liquid Crystal Phase Grating with Double Period Based on the VIS Mode A tunable liquid crystal (LC) phase grating based on vertical-field in-plane electrical switching (VIS) is proposed. The tunable LC phase grating is composed of four parts: an LC layer, the top-plane and bottom-plane electrodes, the polyimide (PI) layer, and the top and bottom periodical strip electrodes. On the one hand, a large period LC phase grating can be obtained by applying voltage to the upper plane electrode and the lower periodic strip electrodes. On the other hand, a small period LC phase grating can be obtained by applying voltage to the lower plane electrode and the upper period electrodes. As a result, two kinds of LC phase grating with different periods can be realized by changing the driving scheme. Naturally, the diffraction angle can be adjusted by controlling different LC phase grating periods. The simulation results show that this tunable LC phase grating based on the VIS mode has a relatively short response time and low operating voltage (5.4 V). These characteristics make the tunable LC phase grating have good application prospects in holographic 3D or augmented reality (AR) display. Grating ; Materials science ; Holographic grating ; Electrode ; Phase (matter) ; Blazed grating ; Optics ; Liquid crystal ; Diffraction grating ; Optoelectronics ; Voltage ; Ultrasonic grating ; Diffraction ; Liquid-crystal display ; Holography ; Chemistry ; Physics Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Photorefractive and Nonlinear Optics
Authors: Yaowalak Srisuwan | Prasong Srihanam | Theeraphol Phromsopha | Yodthong Baimark
Source: e-Polymers (article, 2023-01-01)
Field: Materials Science — Biomaterials

Effect of citric acid on thermal, phase morphological, and mechanical properties of poly(l-lactide)- <i>b</i> -poly(ethylene glycol)- <i>b</i> -poly(l-lactide)/thermoplastic starch blends Abstract This work investigated the thermal, morphological, and tensile properties of poly( l -lactide)- b -poly(ethylene glycol)- b -poly( l -lactide) (PLLA-PEG-PLLA)/thermoplastic starch (TPS) blends with 3 wt% citric acid (CA) treatment of TPS. The blends with PLLA-PEG-PLLA/CA-TPS ratios of 100/0, 90/10, 80/20, and 60/40 (w/w) were investigated and compared with PLLA-PEG-PLLA/CA-free TPS blends. Crystallizability of the blends decreased and thermal stability increased as the TPS content increased. The thermal stability of the blends was found to improve after CA treatment of TPS. The PLLA-PEG-PLLA/CA-TPS blends showed better phase compatibility than those of the PLLA-PEG-PLLA/CA-free TPS blends. The tensile properties of the blends were improved by CA treatment of TPS. In conclusion, improvement in thermal stability, phase compatibility, and tensile properties of the PLLA-PEG-PLLA/TPS blends was obtained by CA treatment of TPS. The resulting PLLA-PEG-PLLA/CA-TPS blends could potentially be used to prepare biodegradable and flexible bioplastics. Materials science ; Ultimate tensile strength ; Ethylene glycol ; Thermoplastic ; Thermal stability ; PEG ratio ; Citric acid ; Starch ; Composite material ; Polymer blend ; Chemical engineering ; Polymer chemistry ; Copolymer ; Polymer ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications
Authors: Inna Novianty | Walidatush Sholihah | Gema Parasti Mindara | Muhammad Iqbal Nurulhaq | Anifatul Faricha | Rismen Sinambela | Pradeka Brilyan Purwandoko | Muhammad Achirul Nanda
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2023-06-23)
Field: Chemistry — Analytical Chemistry

Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm &lt;span lang="EN-US"&gt;Indonesia is the world’s largest producer of palm oil. To preserve its competitive advantages, the Indonesian oil palm sector must expand high-quality palm oil output. In oil palm quality control, the water content is a crucial parameter as it can be used as a reference to determine the right harvest time. Thus, this study proposed a near-infrared (NIR) spectroscopy as a fast and non-destructive analysis to assess oil palm water content. NIR spectra were processed using Shannon entropy to describe the characteristics at each wavelength. In this study, oil palm fruit samples at various maturity levels were collected with eight different maturity fractions. Based on the analysis, the Shannon entropy value is closely related to any changes in the water content of palm oil. The entropy value has a decreasing trend as the water content increases. The proposed technique can predict the water content of an oil palm with satisfactory performance with values of 0.9746 of coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) and 2,487 of root mean square error (RMSE). Application of this model will lead to a fast and accurate prediction system related to oil palm water content.&lt;/span&gt; Palm oil ; Palm ; Coefficient of determination ; Water content ; Mathematics ; Environmental science ; Elaeis guineensis ; Food science ; Chemistry ; Statistics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Coconut Research and Applications
Authors: Per Söderlind | A. Landa | Emily E. Moore | Aurélien Perron | John D. Roehling | Joseph T. McKeown
Source: Applied Sciences (article, 2023-02-07)
Field: Materials Science — Materials Chemistry

High-Temperature Thermodynamics of Uranium from Ab Initio Modeling We present high-temperature thermodynamic properties for uranium in its γ phase (γ-U) from first-principles, relativistic, and anharmonic theory. The results are compared to CALPHAD modeling. The ab initio electronic structure is obtained from density-functional theory (DFT) that includes spin–orbit coupling and an added self-consistent orbital-polarization (OP) mechanism for more accurate treatment of magnetism. The first-principles method is coupled to a lattice dynamics scheme that is used to model anharmonic lattice vibrations, namely, Self-Consistent Ab Initio Lattice Dynamics (SCAILD). The methodology can be summarized in the acronym DFT + OP + SCAILD. Upon thermal expansion, γ-U develops non-negligible magnetic moments that are included for the first time in thermodynamic theory. The all-electron DFT approach is shown to model γ-U better than the commonly used pseudopotential method. In addition to CALPHAD, DFT + OP + SCAILD thermodynamic properties are compared with other ab initio and semiempirical modeling and experiments. Our first-principles approach produces Gibbs free energy that is essentially identical to CALPHAD. The DFT + OP + SCAILD heat capacity is close to CALPHAD and most experimental data and is predicted to have a significant thermal dependence due to the electronic contribution. CALPHAD ; Thermodynamics ; Ab initio ; Pseudopotential ; Anharmonicity ; Density functional theory ; Ab initio quantum chemistry methods ; Chemistry ; Materials science ; Phase diagram ; Computational chemistry ; Condensed matter physics ; Physics ; Phase (matter) ; Molecule Nuclear Materials and Properties ; High-pressure geophysics and materials ; Rare-earth and actinide compounds
Authors: Bruno Zêzere | Tiago V. B. Fonseca | Inês Portugal | Mário M.Q. Simões | Carlos M. Silva | José R. B. Gomes
Source: International Journal of Molecular Sciences (article, 2023-04-15)
Field: Chemistry — Spectroscopy

Influence of Ethanol Parametrization on Diffusion Coefficients Using OPLS-AA Force Field Molecular dynamics simulations employing the all-atom optimized potential for liquid simulations (OPLS-AA) force field were performed for determining self-diffusion coefficients (D11) of ethanol and tracer diffusion coefficients (D12) of solutes in ethanol at several temperature and pressure conditions. For simulations employing the original OPLS-AA diameter of ethanol's oxygen atom (σOH), calculated and experimental diffusivities of protic solutes differed by more than 25%. To correct this behavior, the σOH was reoptimized using the experimental D12 of quercetin and of gallic acid in liquid ethanol as benchmarks. A substantial improvement of the calculated diffusivities was found by changing σOH from its original value (0.312 nm) to 0.306 nm, with average absolute relative deviations (AARD) of 3.71% and 4.59% for quercetin and gallic acid, respectively. The new σOH value was further tested by computing D12 of ibuprofen and butan-1-ol in liquid ethanol with AARDs of 1.55% and 4.81%, respectively. A significant improvement was also obtained for the D11 of ethanol with AARD = 3.51%. It was also demonstrated that in the case of diffusion coefficients of non-polar solutes in ethanol, the original σOH=0.312 nm should be used for better agreement with experiment. If equilibrium properties such as enthalpy of vaporization and density are estimated, the original diameter should be once again adopted. Chemistry ; OPLS ; Vaporization ; Enthalpy of vaporization ; Diffusion ; Ethanol ; Force field (fiction) ; Enthalpy ; Molecular dynamics ; Gallic acid ; Thermodynamics ; Analytical Chemistry (journal) ; Computational chemistry ; Chromatography ; Organic chemistry Diffusion Coefficients in Liquids ; Thermodynamic properties of mixtures ; Phase Equilibria and Thermodynamics
Authors: Pau Besalú‐Sala | Fabien Bruneval | Á. J. Pérez‐Jiménez | J. C. Sancho-Garcı́a | Mauricio Rodríguez‐Mayorga
Source: Journal of Chemical Theory and Computation (article, 2023-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

RPA, an Accurate and Fast Method for the Computation of Static Nonlinear Optical Properties The accurate computation of static nonlinear optical properties (SNLOPs) in large polymers requires accounting for electronic correlation effects with a reasonable computational cost. The Random Phase Approximation (RPA) used in the adiabatic connection fluctuation theorem is known to be a reliable and cost-effective method to render electronic correlation effects when combined with density-fitting techniques and integration over imaginary frequencies. We explore the ability of the RPA energy expression to predict SNLOPs by evaluating RPA electronic energies in the presence of finite electric fields to obtain (using the finite difference method) static polarizabilities and hyperpolarizabilities. We show that the RPA based on hybrid functional self-consistent field calculations yields accurate SNLOPs as the best-tuned double-hybrid functionals developed today, with the additional advantage that the RPA avoids any system-specific adjustment. Random phase approximation ; Computation ; Nonlinear system ; Adiabatic process ; Computer science ; Statistical physics ; Energy (signal processing) ; Field (mathematics) ; Expression (computer science) ; Applied mathematics ; Physics ; Computational physics ; Quantum mechanics ; Algorithm ; Mathematics Nonlinear Optical Materials Research ; Spectroscopy and Quantum Chemical Studies ; Quantum Dots Synthesis And Properties
Authors: Fei Li | Peng Sun | Jian‐Lin Wu | Yin Zhang | Jiehua Wu | Guoqiang Liu | Haoyang Hu | Jun Hu | Xiaojian Tan | Shi He | Jun Jiang
Source: Processes (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator Thermoelectric generator (TEG) has important applications in automotive exhaust waste heat recovery. The Back propagation neural network (BP) can predict the electrical generating performance of TEG efficiently and accurately due to its advantage of being good at handing nonlinear data. However, BP algorithm is easy to fall into local optimum, and its training data usually have deviation since the data are obtained through the simulation software. Both of the problems will reduce the prediction accuracy. In order to further improve the prediction accuracy of BP algorithm, we use the genetic algorithm (GA) to optimize BP neural network by selection, crossover, and mutation operation. Meanwhile, we create a TEG for the heat waste recovery of automotive exhaust and test 84 groups of experimental data set to train the GA−BP prediction model to avoid the deviation caused by the simulation software. The results show that the prediction accuracy of the GA−BP model is better than that of the BP model. For the predicted values of output power and output voltage, the mean absolute percentage error (MAPE) increased to 2.83% and 2.28%, respectively, and the mean square error (MSE) is much smaller than the value before optimization, and the correlation coefficient (R2) of the network model is greater than 0.99. Mean absolute percentage error ; Artificial neural network ; Mean squared error ; Thermoelectric generator ; Genetic algorithm ; Automotive industry ; Crossover ; Standard deviation ; Correlation coefficient ; Waste heat recovery unit ; Computer science ; Approximation error ; Voltage ; Generator (circuit theory) ; Test data ; Backpropagation ; Power (physics) ; Automotive engineering ; Algorithm ; Engineering ; Thermoelectric effect ; Mathematics ; Statistics ; Mechanical engineering ; Artificial intelligence ; Machine learning Advanced Thermoelectric Materials and Devices ; Air Quality Monitoring and Forecasting ; Building Energy and Comfort Optimization
Authors: Xiaoyang XING | Maocheng Zhao | Xiwei Wang | TANG Yuweiyi
Source: Food Science and Technology (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral image-based measurement of total flavonoid content of leaf-use Ginkgo biloba L. Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, withRp2 and RMSEp on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for Rp2 and RMSEp, respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf. Hyperspectral imaging ; Ginkgo ; Partial least squares regression ; Ginkgo biloba ; Near-infrared spectroscopy ; Flavonoid ; Mathematics ; Artificial intelligence ; Pattern recognition (psychology) ; Botany ; Computer science ; Chemistry ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Neurological Disease Mechanisms and Treatments ; Leaf Properties and Growth Measurement
Authors: Mateusz Banach
Source: Biomolecules (article, 2023-02-17)
Field: Materials Science — Materials Chemistry

Improved Assessment of Globularity of Protein Structures and the Ellipsoid Profile of the Biological Assemblies from the PDB In this paper, we present an update to the ellipsoid profile algorithm (EP), a simple technique for the measurement of the globularity of protein structures without the calculation of molecular surfaces. The globularity property is understood in this context as the ability of the molecule to fill a minimum volume enclosing ellipsoid (MVEE) that approximates its assumed globular shape. The more of the interior of this ellipsoid is occupied by the atoms of the protein, the better are its globularity metrics. These metrics are derived from the comparison of the volume of the voxelized representation of the atoms and the volume of all voxels that can fit inside that ellipsoid (a uniform unit Å cube lattice). The so-called ellipsoid profile shows how the globularity changes with the distance from the center. Two of its values, the so-called ellipsoid indexes, are used to classify the structure as globular, semi-globular or non-globular. Here, we enhance the workflow of the EP algorithm via an improved outlier detection subroutine based on principal component analysis. It is capable of robust distinguishing between the dense parts of the molecules and, for example, disordered chain fragments fully exposed to the solvent. The PCA-based method replaces the current approach based on kernel density estimation. The improved EP algorithm was tested on 2124 representatives of domain superfamilies from SCOP 2.08. The second part of this work is dedicated to the survey of globularity of 3594 representatives of biological assemblies from molecules currently deposited in the PDB and analyzed by the 3DComplex database (monomers and complexes up to 60 chains). Ellipsoid ; Context (archaeology) ; Voxel ; Algorithm ; Representation (politics) ; Protein Data Bank (RCSB PDB) ; Kernel (algebra) ; Computer science ; Mathematics ; Physics ; Combinatorics ; Artificial intelligence ; Geology ; Nuclear magnetic resonance Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Samuel Tomi Aina | Hilda Dinah Kyomuhimbo | Barend Du Plessis | Vuyo Mjimba | Nils Haneklaus | Hendrik G. Brink
Source: International Journal of Molecular Sciences (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Cytotoxic-Ag-Modified Eggshell Membrane Nanocomposites as Bactericides in Concrete Mortar Against the backdrop of escalating infrastructure budgets worldwide, a notable portion-up to 45%-is allocated to maintenance endeavors rather than innovative infrastructure development. A substantial fraction of this maintenance commitment involves combatting concrete degradation due to microbial attacks. In response, this study endeavors to propose a remedial strategy employing nano metals and repurposed materials within cement mortar. The methodology entails the adsorption onto eggshell membranes (ESM) of silver nitrate (ESM/AgNO<sub>3</sub>) or silver nanoparticles (ESM/AgNPs) yielding silver-eggshell membrane composites. Subsequently, the resulting silver-eggshell membrane composites were introduced in different proportions to replace cement, resulting in the formulation of ten distinct mortar compositions. A thorough analysis encompassing a range of techniques, such as spectrophotometry, scanning electron microscopy, thermogravimetric analysis, X-ray fluorescence analysis, X-ray diffraction (XRD), and MTT assay, was performed on these composite blends. Additionally, evaluations of both compressive and tensile strengths were carried out. The mortar blends 3, 5, and 6, characterized by 2% ESM/AgNO<sub>3</sub>, 1% ESM/AgNPs, and 2% ESM/AgNPs cement replacement, respectively, exhibited remarkable antimicrobial efficacy, manifesting in substantial reduction in microbial cell viability (up to 50%) of typical waste activated sludge. Concurrently, a marginal reduction of approximately 10% in compressive strength was noted, juxtaposed with an insignificant change in tensile strength. This investigation sheds light on a promising avenue for addressing concrete deterioration while navigating the balance between material performance and structural integrity. Eggshell membrane ; Ultimate tensile strength ; Mortar ; Nanocomposite ; Thermogravimetric analysis ; Materials science ; Compressive strength ; Cement ; Silver nanoparticle ; Scanning electron microscope ; Silver nitrate ; Adsorption ; Composite material ; Membrane ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Nanotechnology ; Nanoparticle Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Membrane Separation Technologies