jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bowen Luo
Source: Applied and Computational Engineering (article, 2023-11-06)
Field: Materials Science — Biomaterials

Poly (Lactic Acid) Applications in Biomedical Engineering Medical devices like implants are playing a pivotal role in medicine. As a polyester, Polylactic acid (PLA) and its copolymers are widely used in the medical area. They are used in several sections including cardiovascular and orthopedic devices, as well as advanced technologies of tissue engineering and drug delivery. In this paper, cardiovascular devices including applications of PLLA coating as drug delivery system and BVS material are introduced. Specifically, PLA can be used to foster scaffolds for bone tissue and cardiovascular tissue engineering. Then this paper introduces PLA applied in the form of nanoparticles and micelles in drug delivery. The special degrading process makes PLA an active drug delivery carrier. And it could also be used to produce biodegradable orthopaedic operation devices and offer the advantage of being free from taking out of the human body. Processing techniques are pivotal prerequisites of applications of medical usage, like electrospun fibers are used in cardiovascular and drug delivery applications. The paper summarizes the use of PLA material in those areas, and foresees the future for a prediction of the attractive prospect. Although the applications are faced with limitations like mechanical properties now, they still have a promising future in biomedicine. Drug delivery ; Polylactic acid ; Tissue engineering ; Biomedicine ; Process (computing) ; Polyester ; Nanotechnology ; Biomedical engineering ; Materials science ; Computer science ; Medicine ; Bioinformatics ; Polymer biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Dhruv Parikh | Bingyi Zhang | Rajgopal Kannan | Viktor K. Prasanna | Carl Busart
Source: arXiv (Cornell University) (preprint, 2023-09-17)
Field: Materials Science — Materials Chemistry

Performance of Graph Neural Networks for Point Cloud Applications Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-the-art for point cloud applications; such applications (viz. autonomous driving) require real-time processing at the edge with tight latency and memory constraints. Conducting performance analysis on such DGNNs, thus, becomes a crucial task to evaluate network suitability. This paper presents a profiling analysis of EdgeConv-based DGNNs applied to point cloud inputs. We assess their inference performance in terms of end-to-end latency and memory consumption on state-of-the-art CPU and GPU platforms. The EdgeConv layer has two stages: (1) dynamic graph generation using k-Nearest Neighbors (kNN) and, (2) node feature updation. The addition of dynamic graph generation via kNN in each (EdgeConv) layer enhances network performance compared to networks that work with the same static graph in each layer; such performance enhancement comes, however, at the added computational cost associated with the dynamic graph generation stage (via kNN algorithm). Understanding its costs is essential for identifying the performance bottleneck and exploring potential avenues for hardware acceleration. To this end, this paper aims to shed light on the performance characteristics of EdgeConv-based DGNNs for point cloud inputs. Our performance analysis on a state-of-the-art EdgeConv network for classification shows that the dynamic graph construction via kNN takes up upwards of 95% of network latency on the GPU and almost 90% on the CPU. Moreover, we propose a quasi-Dynamic Graph Neural Network (qDGNN) that halts dynamic graph updates after a specific depth within the network to significantly reduce the latency on both CPU and GPU whilst matching the original networks inference accuracy. Computer science ; Bottleneck ; Cloud computing ; Graph ; Inference ; Latency (audio) ; Artificial neural network ; Profiling (computer programming) ; Parallel computing ; Theoretical computer science ; Distributed computing ; Artificial intelligence ; Operating system ; Embedded system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Iuliia Melchakova | G.T. Oyeniyi | Sergey P. Polyutov | Павел В. Аврамов
Source: Micromachines (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

Spin Polarization and Flat Bands in Eu-Doped Nanoporous and Twisted Bilayer Graphenes Advanced two-dimensional spin-polarized heterostructures based on twisted (TBG) and nanoporous (NPBG) bilayer graphenes doped with Eu ions were theoretically proposed and studied using Periodic Boundary Conditions Density Functional theory electronic structure calculations. The significant polarization of the electronic states at the Fermi level was discovered for both Eu/NPBG(AA) and Eu/TBG lattices. <i>Eu</i> ions' chemi- and physisorption to both graphenes may lead to structural deformations, drop of symmetry of low-dimensional lattices, interlayer fusion, and mutual slides of TBG graphene fragments. The frontier bands in the valence region at the vicinity of the Fermi level of both spin-polarized 2D Eu/NPBG(AA) and Eu/TBG lattices clearly demonstrate flat dispersion laws caused by localized electronic states formed by TBG Moiré patterns, which could lead to strong electron correlations and the formation of exotic quantum phases. Condensed matter physics ; Nanoporous ; Graphene ; Fermi level ; Doping ; Bilayer graphene ; Ion ; Electronic structure ; Materials science ; Spin (aerodynamics) ; Electron ; Physics ; Nanotechnology ; Quantum mechanics Graphene research and applications ; Advancements in Battery Materials ; 2D Materials and Applications
Authors: Yiheng Zhu | Zhenqiu Ouyang | Ben Liao | Jialu Wu | Yixuan Wu | Chang‐Yu Hsieh | Tingjun Hou | Jian Wu
Source: arXiv (Cornell University) (preprint, 2023-05-15)
Field: Materials Science — Materials Chemistry

MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative models for graphs. However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models. The primary challenge to hierarchical generation is the non-differentiable issue caused by the generation of intermediate discrete coarsened graph structures. To sidestep this issue, we cast the tricky hierarchical generation problem over discrete spaces as the reverse process of hierarchical representation learning and propose MolHF, a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF first generates bonds through a multi-scale architecture, then generates atoms based on the coarsened graph structure at each scale. We demonstrate that MolHF achieves state-of-the-art performance in random generation and property optimization, implying its high capacity to model data distribution. Furthermore, MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms. The code and models are available at https://github.com/violet-sto/MolHF. Computer science ; Generative model ; Exploit ; Differentiable function ; Theoretical computer science ; Representation (politics) ; Graph ; Hierarchical database model ; Generative grammar ; Artificial intelligence ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ling Zhou | Jiangjun Yao | Xu Honggang | Yahui Zhang | Pengcheng Nie
Source: Molecules (article, 2023-09-07)
Field: Chemistry — Analytical Chemistry

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future. Nitrogen ; Near-infrared spectroscopy ; Soil test ; Chemistry ; Spectroscopy ; Water content ; Environmental science ; Soil science ; Moisture ; Soil water ; Analytical Chemistry (journal) ; Environmental chemistry Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jehad Yamin | E. A. Eh Sheet | Ayad M. Al Jubori
Source: GAZI UNIVERSITY JOURNAL OF SCIENCE (article, 2023-05-05)
Field: Chemistry — Analytical Chemistry

ANN and RSM Modelling and Optimization of Paraffins and Aromatics in Crude Oil Distillation Products’ Properties in Iraq Back-Propagation neural networks, as well as RSM-DOE techniques, were used to predict the properties of various compositions of Iraqi oil, which were presented in this study. Paraffin and Aromatics’ effect on petroleum properties, e.g., yield, density, calorific value, and other essential properties, were studied. The input-output data to the neural networks were obtained from existing local refineries in Iraq. Several network activation functions to simulate the hydrocracking process were tested and compared. the network function that gave satisfactory results in terms of convergence time and accuracy was adopted. The data were divided into training and testing parts. The results of the trained artificial neural network models for each one of the tested functions have been cross-validated with the experimental data. The network that compared well against this new set of data (i.e. testing data), with an average percent error always less than 3% for the various products of the hydrocracking unit were chosen for the study. Aromatics showed to have more profound effect on the Octane number at low concentrations of paraffin, while, for specific gravity and calorific value they have similar effects. As for boiling points and sulfur contents, aromatics have almost no effect at lower levels of paraffin. Artificial neural network ; Distillation ; Boiling point ; Oil refinery ; Heat of combustion ; Yield (engineering) ; Response surface methodology ; Octane ; Octane rating ; Biological system ; Mathematics ; Process engineering ; API gravity ; Pulp and paper industry ; Petroleum engineering ; Crude oil ; Materials science ; Computer science ; Chemistry ; Engineering ; Combustion ; Waste management ; Chemical engineering ; Statistics ; Organic chemistry ; Composite material ; Artificial intelligence Petroleum Processing and Analysis ; Process Optimization and Integration ; Biodiesel Production and Applications
Authors: Loris Felardos | Jérôme Hénin | Guillaume Charpiat
Source: arXiv (Cornell University) (preprint, 2023-01-13)
Field: Materials Science — Materials Chemistry

Designing losses for data-free training of normalizing flows on Boltzmann distributions Generating a Boltzmann distribution in high dimension has recently been achieved with Normalizing Flows, which enable fast and exact computation of the generated density, and thus unbiased estimation of expectations. However, current implementations rely on accurate training data, which typically comes from computationally expensive simulations. There is therefore a clear incentive to train models with incomplete or no data by relying solely on the target density, which can be obtained from a physical energy model (up to a constant factor). For that purpose, we analyze the properties of standard losses based on Kullback-Leibler divergences. We showcase their limitations, in particular a strong propensity for mode collapse during optimization on high-dimensional distributions. We then propose strategies to alleviate these issues, most importantly a new loss function well-grounded in theory and with suitable optimization properties. Using as a benchmark the generation of 3D molecular configurations, we show on several tasks that, for the first time, imperfect pre-trained models can be further optimized in the absence of training data. Computer science ; Computation ; Benchmark (surveying) ; Dimension (graph theory) ; Imperfect ; Mathematical optimization ; Algorithm ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Lattice Boltzmann Simulation Studies
Authors: Fangning Ren | Fang Liu
Source: ChemRxiv (preprint, 2023-09-25)
Field: Chemistry — Analytical Chemistry

Data-Driven Insights into the Fluorescence of Asphaltene Aggregates Using Extended Frenkel Exciton Model The origin of the fluorescence redshift during asphaltene aggregation remains debated due to the great diversity of asphaltene molecules, while the extended Frenkel exciton model provided a theoretical framework for studying multi-chromophore systems such as asphaltene aggregates. We investigated the fluorescence energy of hundreds of asphaltene dimers based on 132 experimentally determined asphaltene monomer structures. Our result shows that the dimer’s fluorescence energy is always lower than both of its monomers regardless of its intermolecular conformation, with its redshift dominated by superexchange. The dimer oscillator strength predominantly depends on the monomer with the lower fluorescence energy, and the involvement of charge-transfer states and the cancellation between monomer transition dipole moments suppressed the fluorescence especially when two monomers have similar fluorescence energy. The above findings hold for all asphaltene dimers despite their diversity, which offers a theoretical interpretation for comprehending the relationship between asphaltene aggregation and its fluorescence variation. Furthermore, this work provides theoretical insights into other research areas related to organic planar conjugated systems with multiple chromophores. Chromophore ; Monomer ; Asphaltene ; Fluorescence ; Intermolecular force ; Chemical physics ; Dimer ; Exciton ; Chemistry ; Photochemistry ; Materials science ; Molecule ; Polymer ; Physics ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: David M. Ceperley | Scott Jensen | Yubo Yang | Hongwei Niu | Carlo Pierleoni | Markus Holzmann
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Training models using forces computed by stochastic electronic structure methods Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic errors affect the generation of effective models by analyzing the errors within a linear least squares procedure, finding that there is an advantage to having many relatively imprecise data points for constructing models. We then analyze the effect of noise on a model of many-body silicon finding that noise in some situations improves the resulting model. We then study the effect of QMC noise on two machine learning models of dense hydrogen used in a recent study of its phase diagram. The noise enable us to estimate the errors in the model. We conclude with a discussion of future research problems. Computer science ; Noise (video) ; Cover (algebra) ; Monte Carlo method ; Algorithm ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Kennet J. Rueda Espinosa | Alexei A. Kananenka | Alexander A. Rusakov
Source: ChemRxiv (preprint, 2023-07-31)
Field: Materials Science — Materials Chemistry

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization Using the example of astatine, the heaviest naturally occurring halogen whose isotope At-211 has promising medical applications, we propose a new infrastructure for large-scale computational models of heavy elements with strong relativistic effects. In particular, we focus on developing an accurate force field for At– in water based on reliable relativistic DFT calculations. To ensure such calculations’ reliability, we design novel basis sets for relativistic DFT via the polarization-consistent basis set idea’s extension to heavy elements, thus eliminating the basis-set error from DFT calculations. The resulting basis sets enable the well-grounded evaluation of relativistic DFT against “gold-standard” CCSD(T) results. We employ the evolutionary algorithm called particle swarm optimization for the force field and basis set efficient optimization. Accounting for strong relativistic effects, including spin-orbit interaction, via our redesigned infrastructure, we elucidate a noticeable dissimilarity between At– and I– in halide–water force field parameters, radial distribution functions, diffusion coefficients, and hydration energies. This works establishes the framework for the systematic development of polarization-consistent basis sets for relativistic DFT and accurate force fields for molecular dynamics simulations to be used in large-scale models of complex molecular systems with the elements from the bottom of the periodic table, including actinides and even superheavy elements. Basis set ; Basis (linear algebra) ; Force field (fiction) ; Statistical physics ; Particle swarm optimization ; Computer science ; Physics ; Density functional theory ; Computational physics ; Algorithm ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Muon and positron interactions and applications ; Advanced NMR Techniques and Applications
Authors: Kannapot Kaewsorn | Thitima Phanomsophon | Pisut Maichoon | Dharma Raj Pokhrel | Pimpen Pornchaloempong | Warawut Krusong | Panmanas Sirisomboon | Munehiro Tanaka | Takayuki Kojima
Source: Preprints.org (preprint, 2023-11-13)
Field: Chemistry — Analytical Chemistry

Modeling Methods for Evaluation of Texture Properties of Cooked Germinated Brown Rice Using the Near Infrared Spectra of Uncooked Whole Grain The models of partial least squares (PLS) regression and artificial neural network (ANN) for evaluation of texture properties of cooked germinated brown rice (GBR) using the Fourier transform near infrared (NIR) spectra of uncooked whole grain combined with data separation methods, spectral pretreatment methods were investigated in this study. The ANN was outperformed in evaluation of hardness by back extrusion test of cooked GBR using the smoothing combined with standard normal variate pretreated NIR spectra in the range of 12,500-4,000 cm−1 of 188 whole grain samples where the calibration sample set was separated from prediction set by Kennard-Stone method where the best ANN model for hardness from hidden layers of 25 and 8 iteration time provided R2, r2, RMSEC, RMSEP, Bias and RPD of 0.9987, 0.9447, 0.1021 N, 0.7699 N, 0.0216 N and 4.3 respectively. The PLS regression of 64 sample KDML GBR group and 64 sample various variety GBR group, provided models for the hardness of the former and the toughness of the latter which developed by using 7506−5446.3, 4605.4−4242.9 cm−1 which included the amylose vibration band of 6834 cm−1 while toughness model was from 9403.8-6094.3 cm−1 where included 6834 and 8316 cm−1 vibration bands of amylose which influenced the texture of cooked rice. The PLS regression models for hardness and toughness were with the r2 of 0.85 and 0.82, and the RPD of 2.9 and 2.4, respectively. The ANN model for hardness of cooked GBR should be implemented to the practical use in GBR production factories for the quality assurance and further updating using more samples and several brands to obtain the robust models. Partial least squares regression ; Brown rice ; Texture (cosmology) ; Amylose ; Mathematics ; Whole grains ; Analytical Chemistry (journal) ; Chemistry ; Food science ; Materials science ; Statistics ; Artificial intelligence ; Chromatography ; Computer science Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Food composition and properties
Authors: Maha Hany | Hatem A. Rashwan | Neveen M. Abdelmotilib
Source: Machine Learning and Applications An International Journal (article, 2023-03-30)
Field: Chemistry — Analytical Chemistry

A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques. Feature selection ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Principal component analysis ; Linear discriminant analysis ; Feature extraction ; Dimensionality reduction ; Feature (linguistics) ; Selection (genetic algorithm) ; Machine learning ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Sensory Analysis and Statistical Methods