jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shweta Mehta | Sheena Agarwal | Kavita Joshi
Source: arXiv (Cornell University) (preprint, 2018-12-12)
Field: Materials Science — Materials Chemistry

Combining DFT with ML to study size specific interactions between metal clusters and adsorbates To date, density functional theory (DFT) is one of the most accurate and yet practical theory to gain insight about materials properties. Although successful, the computational cost is the main hurdle even today. A way out is combining DFT with machine learning (ML) to reduce the computational cost without compromising accuracy. However, the success of this approach hinges on the correctness of the descriptors. In the present work, we demonstrate that, based on {\it only} interatomic distances as descriptors, our ML model predicts interaction energy between an adsorbate and Al cluster with absolute mean error (AME) $\sim$ 0.05 eV (or less) and reproduces the PES experienced by an incoming atom. Our extensive DFT calculations reveal that atoms experiencing identical environment within a cluster have identical interaction energy patterns. Further, we demonstrate that our model is not specific to Al clusters, and could be applied to clusters of different elements as well. Its application to compute PES experienced by various test atoms and molecules in the vicinity of different clusters proves the transferability of the model not just to clusters of different elements but also to various molecules. The descriptors chosen are invariant to rotation, translation, and permutation yet very simple to compute is one of the most crucial points of the present work. Cluster (spacecraft) ; Density functional theory ; Correctness ; Atom (system on chip) ; Invariant (physics) ; Transferability ; Computer science ; Translation (biology) ; Hinge ; Statistical physics ; Algorithm ; Computational chemistry ; Physics ; Chemistry ; Machine learning ; Quantum mechanics ; Classical mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Diego Guedes‐Sobrinho | Weiqi Wang | Ian Hamilton | Juarez L. F. Da Silva | Luca M. Ghiringhelli
Source: arXiv (Cornell University) (preprint, 2018-11-11)
Field: Materials Science — Materials Chemistry

(Meta-)stability and Core-Shell Dynamics of Gold Nanoclusters at Finite Temperature Gold nanoclusters have been the focus of numerous computational studies but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core-shell morphology is observed. We analyze their structure and dynamics using of state-of-the-art techniques, including unsupervised machine-learning nonlinear dimensionality reduction (sketch-map) for describing the similarities and differences among the range of sampled configurations. We find that, whereas the gold nanoclusters exhibit continuous structural rearrangement, they clearly show persistent motifs: a cationic core of one to five atoms is loosely bound to a shell which typically displays a substructure resulting from the competition between locally spherical vs planar fragments. Besides illuminating the properties of core-shell gold nanoclusters, the present study proposes a set of useful tools for understanding their nature in operando. Nanoclusters ; Substructure ; Shell (structure) ; Molecular dynamics ; Chemical physics ; Ab initio ; Nanotechnology ; Monatomic ion ; Materials science ; Superatom ; Statistical physics ; Physics ; Computational chemistry ; Chemistry ; Density functional theory ; Quantum mechanics Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Random Matrices and Applications
Authors: Seema Kesar | Sarvesh Paliwal | Pooja Mishra | Monika Chauhan
Source: Turkish Journal of Pharmaceutical Sciences (article, 2018-06-01)
Field: Chemistry — Organic Chemistry

Quantitative Structure–Activity Relationship Analysis of Selective Rho Kinase Inhibitors as Neuro-regenerator agent Introduction: Understanding the role of Rho (serine/threonine) kinases in the treatment of neurological segments, attempts have been made to find potent inhibitors of Rho enzyme by 2D quantitative structure activity relationship (QSAR) model. Materials and Methods: QSAR studies were executed on urea based scaffolds from anilines and benzylamines analogs, which were aligned for generation of chemometric based model. Multivariate statistical approaches have been applied includes linear and non-linear analysis such as multiple linear regression, partial least square and artificial neural network for the generation of model, and also an application of ADME studies was performed to ascertain the novelty and drug like properties of the intended molecules. Results: Ligand based analysis have been implemented and having excellent statistical relevance such as S-value= 0.38, F-value= 48.41, r= 0.95, r= 0.91 and rcv= 0.86. Five illuminating variables, VAMP polarization YY component (whole molecule), VAMP dipole Y component (whole molecule), VAMP dipole Z component (whole molecule), Kier ChiV6 path index (whole molecule) and Moment of inertia 2 size (whole molecule) were found and own profound influence on the potency of the compounds. Discussion and Conclusion: The values of standard statistical parameters reveal the predictive power and robustness of this model and also provided valuable insight to the significance of five descriptors. The acquired physicochemical properties (electronic, topological and steric) shows the important structural features required for activity against Rho kinase. After performing Lipinski's rule of five on urea based derivatives no molecule were violating the rule. So, these features can be effectively u n c o r r e c t e d p r o o f employed for modeling and screening of active neurological agents as novel Rho-Kinase inhibitors. Regenerative heat exchanger ; Chemistry ; Biological system ; Biophysics ; Biology ; Physics ; Thermodynamics Synthesis of Tetrazole Derivatives ; Synthesis and biological activity ; Computational Drug Discovery Methods
Authors: Andrea Pedrini | Marco Piastra | Epifanio G. Virga
Source: arXiv (Cornell University) (preprint, 2018-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lily-like twist distribution in toroidal nematics Toroidal nematics are droplets of nematic liquid crystals in the form of a circular torus. When the nematic director is subject to planar degenerate boundary conditions, the bend-only director field with vector lines along the parallels of all internal torodial shells is an equilibrium solution for all values of the elastic constants. Local stability analyses have shown that an instability is expected to occur for sufficiently small values of the twist elastic constant. It is natural to conjecture that in this regime the global equilibrium would be characterized by a maximum twist deflection on the boundary of the torus, with a twist distribution over the torus' cross-section represented by a fennel-like surface. We prove that surprisingly the stable twist distribution is instead represented by a lily-like surface. Thus the overall maximum twist deflection falls well within the torus. To cope with the complexity of the elastic free-energy functional in the fully non-linear setting, we developed an ad hoc deep-learning optimization method, which here is also duly validated and documented for it promises to be applicable to other similar problems, equally intractable analytically. Twist ; Torus ; Toroid ; Liquid crystal ; Degenerate energy levels ; Instability ; Physics ; Deflection (physics) ; Surface of revolution ; Boundary value problem ; Distribution (mathematics) ; Classical mechanics ; Surface (topology) ; Mathematics ; Geometry ; Mathematical analysis ; Condensed matter physics ; Mechanics ; Quantum mechanics Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Abdorasool Fakhraei | Maryam Abbasi Tarighat | Khosro Mohammadi | Gholamreza Abdi | Arash Rezaei
Source: Analytical and bioanalytical chemistry research (article, 2018-12-01)
Field: Chemistry — Analytical Chemistry

Chemometrics-enhanced Kinetic Spectrophotometric Method for Simultaneous Determination of Ag+, Cu2+ and Ni2+ Ions in Some Medicinal Plants by Dimethyl 2,2’-(ethan-1,2-diylbis1)bis(cyclopent-1-ene-1-carbodithioate) The metal ions constituents in plants reliable for determination of their medicinal, nutritional and toxicity properties. A novel, simple and accurate spectrophotometric method was developed for the simultaneous determination of Ag+, Cu2+ and Ni2+ ions in different medicinal plants without prior separation steps. This method was based on the different kinetic characteristics between the reactions of analytes with newly synthesized Schiff base as dimethyl 2,2’,-(ethan-1,2-diylbis1)bis(cyclopent-1-ene-1-carbodithioate(DEBC). All experimental conditions include, DEBC concentration, effects of pH and temperature was optimized. Also, orthogonal array design was applied for the construction of concentration. The differential kinetic spectra were monitored and recorded at 397 nm. The reaction orders were estimated with respect to complex mixture of analytes with DEBC. Limit of detections values were 0.013, 0.078 and 0.003 mg L-1 and limit of quantifications values were 0.043, 0.250 and 0.011 for, Cu2+, Ni2+ and Ag+, respectively. The recorded data were processed by principal component analysis- back propagation neural networks (PCBPNNs). A set of synthetic mixtures of metal ions was evaluated and the obtained results by PCBPNNs were discussed. The simultaneous analysis was also performed without compressed data and was compared with those obtained by PCBPNNs. The proposed method was successfully applied to the simultaneous determination of metal ions in medicinal plants samples. The results of proposed method were compared by those obtained on the same samples by Graphite furnace atomic absorption spectrometry. Metal ions in aqueous solution ; Detection limit ; Chemistry ; Analyte ; Chemometrics ; Graphite furnace atomic absorption ; Analytical Chemistry (journal) ; Ion ; Metal ; Kinetic energy ; Nuclear chemistry ; Chromatography ; Organic chemistry Analytical Methods in Pharmaceuticals ; Inorganic and Organometallic Chemistry ; Electrochemical Analysis and Applications
Authors: Hou Jing Chan
Source:  (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Cable Fault Recognition Using Particle Swarm Optimization Partial Discharge(PD), a type of breakdown that due to the defect that occurs in the insulation material of the power system. During the occurrence of PD, it may lead to serious insulation damage and decrease the life span of the high voltage equipment. Hence, PD monitoring is required in order to minimize the chance of PD lead to serious impact to the power system In this final dissertation, a research work to develop a hybrid cable fault recognition system had been done. The classifiers include Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference System (ANFIS) and being optimized by modified Chaotic Inertia Weight Particle Swarm Optimization with Time Varying Acceleration Coefficient(CIW-PSO-TVAC) to improve its performance under noise-free and noise-contaminated condition. The result obtained for optimized and unoptimized ANN, SVM and ANFIS under noise-free and noise contaminated condition were compared side by side. Based on the result shown, optimized ANN outperform compare to SVM and ANFIS for noise-free and noise-contaminated condition which show the highest classification accuracy and noise tolerance level. Hence, this method can be proposed as one of the method to improve the performance of classifiers for PD pattern classification under noise-free and noise-contaminated condition. Particle swarm optimization ; Noise (video) ; Adaptive neuro fuzzy inference system ; Support vector machine ; Artificial neural network ; Fault (geology) ; Artificial intelligence ; Control theory (sociology) ; Computer science ; Engineering ; Pattern recognition (psychology) ; Machine learning ; Fuzzy logic ; Fuzzy control system High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Anomaly Detection Techniques and Applications
Authors: Shweta Mehta | Sheena Agarwal | Kavita Joshi
Source: arXiv (Cornell University) (preprint, 2018-12-12)
Field: Materials Science — Materials Chemistry

Catching the essence of Hohenberg-Kohn's first theorem with machine learning To date, density functional theory (DFT) is one of the most accurate and yet practical theory to gain insight about materials properties. Although successful, the computational cost is the main hurdle even today. In recent years, there has been a trend in combining DFT with machine learning (ML) to reduce the computational cost without compromising accuracy. In this work, we have combined DFT with ML to predict the potential energy surface experienced by an adsorbate. Towards this, interaction energy of an adsorbate with Al clusters is predicted which is the first step towards understanding size specific reactivity of clusters. Our detailed and extensive DFT calculations reveal that atoms experiencing identical environment within a cluster have identical interaction energy patterns. Further based on these DFT calculations, ML techniques are implemented to predict interaction energy between a test atom and cluster. We demonstrate that, based on {\it only} interatomic distances as descriptors, our ML model predicts interaction energy between an adsorbate and cluster with absolute mean error (AME) $\sim$ 0.05 eV (or less) and could reproduce the PES experienced by an incoming atom. Our extensive DFT calculations provide the rationale of achieving improved accuracy and a proof of concept that our ML model picks up the essence of Hohenberg-Kohn's first theorem. Finally, this work will help us to understand the size specific reactivity of clusters better. Density functional theory ; Cluster (spacecraft) ; Atom (system on chip) ; Work (physics) ; Reactivity (psychology) ; Coupled cluster ; Energy (signal processing) ; Interaction energy ; Statistical physics ; Computer science ; Computational chemistry ; Theoretical physics ; Molecule ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Advanced Chemical Physics Studies
Authors: Han Suk Ryu | Dongjian Han | Kyoung‐Hwa Lee | Ki Hoon Kim
Source: Annals of Oncology (article, 2018-10-01)
Field: Chemistry — Spectroscopy

Proteomic-based machine learning computational analysis discovered biomarkers of aberrant vesicle-exosomal trafficking to determine chemotherapeutic responses in breast cancer Exosome ; Biomarker ; Breast cancer ; Cancer ; Cancer research ; Proteomics ; Biopsy ; Companion diagnostic ; Immunohistochemistry ; Medicine ; Microvesicles ; Biology ; Bioinformatics ; Computational biology ; Pathology ; microRNA ; Internal medicine ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Extracellular vesicles in disease ; Advanced Biosensing Techniques and Applications
Authors: Heng Soon Lee
Source:  (article, 2018-01-05)
Field: Materials Science — Materials Chemistry

Cable Fault Recognition Using Image Analysis Of Phase Resolved Partial Discharge Pattern Partial Discharge (PD) source classification is vital to diagnose the insulation quality to prevent any cable joint insulation breakdown that could cause major loss to power companies. There are many research works done on PD pattern recognition but it is usually done in noise free condition and through literature review, once noise is introduced the accuracy is not promising. Throughout literature review, several image analysis methods like fractal dimensions and lacunarity and wavelet shape decomposition and others algorithm were used but their work can still be improved as the accuracy when the PD data is contaminated dropped significantly to unusable levels. PD measured from five cross-linked polyethylene (XLPE) cables with 5 different defects including insulation incision defect, axial direction shift defect, semiconductor layer tip defect and air gap defect and metal particle on XLPE defect were used as input PD data in this research work. To replicate the real environment, noise was added into the PD data. Noise duration, noise amplitude and the combination of the previous two were the three type of noise condition used here. New feature extraction method using image analysis of phase resolved partial discharge pattern to extract features for PD classification was performed to test its noise tolerance rate. In this work, the images used as input for feature extraction were generated from the PD data which are the pulse count and pulse height distribution. The performance of conventional statistical features was compared with the new image features. The features generated were undergone classification using two different artificial intelligence classifiers, which are the Support Vector Machine (SVM) and Artificial Neural Network (ANN). The performance of ANN and SVM were compared within same feature type, then the best results were compared with the best results from another type of feature. It was found that among ANN and SVM, ANN provides better results than SVM in each type of feature with respective noise condition. As for the comparison between statistical and proposed image feature, the image features displayed a better performance. Partial discharge ; Noise (video) ; Lacunarity ; Artificial intelligence ; Pattern recognition (psychology) ; Wavelet ; Computer science ; Fractal ; Engineering ; Fractal dimension ; Voltage ; Image (mathematics) ; Mathematics ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization
Authors: Meryl Spencer
Source: Deep Blue (University of Michigan) (article, 2018-01-01)
Field: Materials Science — Biomaterials

The Role of Topology and Topological Changes in the Mechanical Properties of Epithelia Epithelia, quasi-two-dimensional sheets of cells, are important in molding organs into their correct final shape and size during animal development. Epithelia are active materials that are capable of both generating and reacting to mechanical forces, in a manner that depends on the organization of their cells. Cells in an epithelium may divide, exchange neighbors, or otherwise remodel their packing topology, thereby creating a complex feedback loop between tissue topology and mechanical forces. A full theory of the interplay between mechanical forces and cellular arrangement has not yet been developed. Here we work towards developing such a theory using a vertex model framework, which represents complex biological processes as an active network of cell-cell interactions. We consider several specific problems: We carefully derive the forces acting on vertices, places where three or more cells meet, with special attention to fourfold vertices. This work results in a mathematical proof of the criterion for stabilizing fourfold vertices, which places theoretical limits on the types of tissues that can support stable fourfold vertices. Continuous supra-cellular actomyosin cables are capable of generating large forces to either resist external stress or drive cell motion. These cables have been extensively studied in isolation, but there has been little work on the effect of multiple parallel cables on tissue mechanics. Here we show that these cables prevent cells from becoming elongated or misshapen under large stress anisotropies and can only arise in certain favorable topologies. We develop two measures of the favorability of a disordered packing to forming cables, a quality we call cableness, and show that passive cell flow reduces cableness whereas oriented cell divisions increase cableness. A large anisotropic stress is applied to the Drosophila pupal notum for a few hours during its development, at which time it develops internal apical actomyosin fibers. We present a toy model incorporating these fibers into the network of cell-cell interactions, based on the assumption that these fibers form in order to resist the applied stress, and validate predictions of the model against experimental data. We also summarize the computational methods that are the foundation of our scientific results. We present the design philosophy for our highly modular vertex model, as well as the algorithms we developed to correctly implement T1 transitions. We also discuss our use of automated image analysis techniques in the context of fluorescent imaging, including both morphological operations and machine learning algorithms. Topology (electrical circuits) ; Mathematics ; Combinatorics Supramolecular Self-Assembly in Materials
Authors: XING Suxia(邢素霞) | Wang JiuQing | CHEN Si(陈思) | WANG Rui(王睿)
Source: SHILAP Revista de lepidopterología (article, 2018-07-01)
Field: Chemistry — Analytical Chemistry

Study on Chicken Quality Classification Method Based on K-Means-RBF(基于K-means-RBF的鸡肉品质分类方法研究) The chicken is easy to get spoilage during the storage and transportation. The hyperspectral imaging technology with the maps and spectral combination features was applied in this study, which could extract the spectral characteristics, texture and color characteristics to realize the rapid classification from the intrinsic quality and external qualities of the chicken. First of all, according to the physical and chemical analysis, 62 chicken breast samples were divided into four categories which are safe, edible, not recommended, and inedible. Secondly, using 42 labeled samples as the training set, texture, color, and spectral features were taken as the input of K-means-RBF neural network to identify the K-means initial classification center, train the RBF neural network, and construct the K-means-RBF chicken quality classification model. K-means-RBF chicken quality classification model was tested using the remaining 20 samples as test sets. The test results showed that the accuracy of the classification of 20 test samples was 100%. However, the classification accuracies of texture feature, color feature and texture color were 85%, 80%, and 95%, respectively. The chicken quality classification has successfully utilized the feature of hyperspectral imaging “map and spectral combination”, and the comprehensive detection of chicken has been successfully achieved. The results show that the K-means-RBF fusion method is effective in the hyperspectral data analysis, and the limitation of single feature in classification.(鸡肉在贮藏和运输过程中容易腐败变质,利用高光谱成像技术的图、谱合一特点,同时获取鸡肉的光谱特征和纹理、颜色特征,通过鸡肉的内在特征与外在特征综合实现鸡肉品质快速分类。制备62份鸡胸肉样品,通过理化分析分为放心食用、可食用、不建议食用和不可食用4类；以已知分类结果的42个样品作为训练集,将纹理、颜色、光谱特征作为K-means-RBF神经网络的输入,确定K-means初始分类中心、训练RBF神经网络,构建K-means-RBF鸡肉品质分类模型,并利用剩余20个样品作为测试集,对K-means-RBF鸡肉品质分类模型进行测试。测试结果显示,通过训练后的K-means-RBF神经网络对20个测试集样品的分类正确率达到100%；而分别采用纹理、颜色和纹理颜色综合特征作为输入建立的分类器,正确率分别为85%、80%、95%。鸡肉品质分类成功利用了高光谱成像技术“图谱合一”的特点,实现了鸡肉品质的综合检测,验证了K-means-RBF融合方法在高光谱数据分析中的有效性,及单一特征在分类中的局限性。) Hyperspectral imaging ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial neural network ; Texture (cosmology) ; Quality (philosophy) ; Feature extraction Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis