jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunfeng Hu | Phonemany Ounkham | Ondřej Maršálek | Thomas E. Markland | Bala Krishnamoorthy | Aurora E. Clark
Source: ChemRxiv (preprint, 2021-01-21)
Field: Chemistry — Spectroscopy

Persistent Homology Metrics Reveal Quantum Fluctuations and Reactive Atoms in Path Integral Dynamics Nuclear quantum effects (NQEs) are known to impact a number of features associated with chemical reactivity and physicochemical properties, particularly for light atoms and at low temperatures. In the imaginary time path integral formalism, each atom is mapped onto a “ring polymer” whose spread is related to the quantum mechanical uncertainty in the particle’s position i.e. its thermal wavelength. A number of metrics have previously been used to investigate and characterize this spread and explain effects arising from quantum delocalization, zero-point energy, and tunnelling. Many of these shape metrics consider just the instantaneous structure of the ring polymers. However, given the significant interest in methods such as centroid molecular dynamics and ring polymer molecular dynamics that link the molecular dynamics of these ring polymers to real time properties, there exists significant opportunity to exploit metrics that also allow for the study of the fluctuations of the atom delocalization in time. Here we consider the ring polymer delocalization from the perspective of computational topology, specifically persistent homology, which describes the 3-dimensional arrangement of point cloud data (i.e. atomic positions). We employ the Betti sequence probability distribution to define the ensemble of shapes adopted by the ring polymer. The Wasserstein distances of Betti sequences adjacent in time are used to characterize fluctuations in shape, where the Fourier transform and associated principal components provides added information differentiating atoms with different NQEs based on their dynamic properties. We demonstrate this methodology on two representative systems, a glassy system consisting of two atom types with dramatically different de Broglie thermal wavelengths, and ab initio molecular dynamics simulation of an aqueous 4 M HCl solution where the H-atoms are differentiated based on their participation in proton transfer reactions. Keywords: path integral molecular dynamics, persistent homology, quantum delocalization, proton transfer, Wasserstein distances. Delocalized electron ; Quantum ; Physics ; Molecular dynamics ; Atom (system on chip) ; Statistical physics ; Quantum mechanics ; Chemical physics ; Computer science Molecular spectroscopy and chirality ; History and advancements in chemistry
Authors: Marco Podda | Davide Bacciu
Source: arXiv (Cornell University) (preprint, 2021-07-18)
Field: Materials Science — Materials Chemistry

GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph\n Generation The problem of labeled graph generation is gaining attention in the Deep\nLearning community. The task is challenging due to the sparse and discrete\nnature of graph spaces. Several approaches have been proposed in the\nliterature, most of which require to transform the graphs into sequences that\nencode their structure and labels and to learn the distribution of such\nsequences through an auto-regressive generative model. Among this family of\napproaches, we focus on the GraphGen model. The preprocessing phase of GraphGen\ntransforms graphs into unique edge sequences called Depth-First Search (DFS)\ncodes, such that two isomorphic graphs are assigned the same DFS code. Each\nelement of a DFS code is associated with a graph edge: specifically, it is a\nquintuple comprising one node identifier for each of the two endpoints, their\nnode labels, and the edge label. GraphGen learns to generate such sequences\nauto-regressively and models the probability of each component of the quintuple\nindependently. While effective, the independence assumption made by the model\nis too loose to capture the complex label dependencies of real-world graphs\nprecisely. By introducing a novel graph preprocessing approach, we are able to\nprocess the labeling information of both nodes and edges jointly. The\ncorresponding model, which we term GraphGen-Redux, improves upon the generative\nperformances of GraphGen in a wide range of datasets of chemical and social\ngraphs. In addition, it uses approximately 78% fewer parameters than the\nvanilla variant and requires 50% fewer epochs of training on average.\n Computer science ; Preprocessor ; Graph ; Theoretical computer science ; ENCODE ; Node (physics) ; Conditional independence ; Marginal distribution ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Software Engineering Research
Authors: Sayyed Ahmad Khadem
Source: eScholarship@McGill (McGill) (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Mathematical modeling and direct numerical simulation of the self-assembly in biological chiral lyotropic liquid crystals Lyotropic ; Liquid crystal ; Materials science ; Liquid crystalline Molecular spectroscopy and chirality ; Liquid Crystal Research Advancements ; Lipid Membrane Structure and Behavior
Authors: R. Patrick Xian | Ralph Ernstorfer | Philipp Pelz
Source: arXiv (Cornell University) (preprint, 2021-02-10)
Field: Materials Science — Materials Chemistry

Scalable multicomponent spectral analysis for high-throughput data annotation Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. To automate the annotation process for spectroscopic and diffraction data collected in counts of hundreds to thousands, we present a systematic approach compatible with high-performance computing infrastructures using the MapReduce model and task-based parallelization. We implement the approach in software and demonstrate linear computational scaling with respect to spectral components using multidimensional experimental materials characterization datasets from photoemission spectroscopy and powder electron diffraction as benchmarks. Our approach enables efficient generation of high-quality data annotation and online spectral analysis and is applicable to a variety of analytical techniques in materials science and chemistry as a building block for closed-loop experimental systems. Computer science ; Scalability ; Throughput ; Computational science ; Annotation ; Software ; Parametric statistics ; Block (permutation group theory) ; Task (project management) ; Characterization (materials science) ; Process (computing) ; Data mining ; Computer engineering ; Materials science ; Artificial intelligence ; Database ; Nanotechnology ; Programming language ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Electrochemical Analysis and Applications
Authors: Ravi Babu Sharma | J. Ganesh Kumar
Source: International Journal of Advanced Engineering Research and Science (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Design &amp; Development of a Computer Controlled Tensile Strength Testing Machine for Testing of Strings &amp; Fabrics Tensile testing is a primary material science test in which a sample is tension-proof until it is failure. Test results are frequently used to select an application material, to verify quality and to forecast how the material reacts with other forces. The features assessed directly by a tensile test include maximum tensile strength, maximum length and a reduction of the area. From these measures the following characteristics can also be determined: Young modulus, the ratio of Poisson, yield strength and stress resistance. Testing machine is used for the development and maintenance of better information on known materials or the development of new materials. There are many test machines, but the most common of these is the one that tests tensions, compression and bending of the materials. For the test specimen to be tested, the engine must have the proper characteristics. Three major parameters exist: strength, speed, accuracy and precision. The test machine is mainly used in the creation of the stress line diagram. The computer algorithm may be applied to compute the yield strength, tensile force, youth module or total elongation once the diagram has been generated. The author has designed & fabricated a model for demonstration of the techniques employed using viper motor (DC motor) for force exertion, load cell & instrumentation amplifier of reading force, ultrasonic distance sensor for measurement of displacement. Simultaneously a tripod mounted webcam is employed to capture real time video & image processing & employed to compute displacement on the basis of motor movement. Ultimate tensile strength ; Universal testing machine ; Tensile testing ; Structural engineering ; Computer science ; Mechanical engineering ; Engineering ; Composite material ; Materials science Textile materials and evaluations
Authors: Long Wang | Xingyuan Huang | Haifeng Liang
Source: e-Polymers (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Experimental study on the dissolution of supercritical CO <sub>2</sub> in PS under different agitators Abstract In the continuous molding of microporous plastics, the polymer/CO 2 homogeneous body needs to be formed in a very short time, which affects the subsequent bubble nucleation, bubble pore distribution, and growth, and is the key to the molding. It is known that the formation time of homogeneous body is shortened during the continuous molding of microporous plastics because of the agitator’s effect. However, different agitators have different effects on the dissolution rate. So, it is necessary to study not only the dissolution of gas in polymer melt under static condition but also the dissolution under the action of the agitator. In this article, the solubility and dissolution rate of supercritical CO 2 in polymer melt PS at different temperatures and pressures were experimentally investigated under conical and screw agitators, and the numerical solution was also carried out. Dissolution ; Agitator ; Materials science ; Supercritical fluid ; Microporous material ; Bubble ; Polymer ; Composite material ; Nucleation ; Molding (decorative) ; Homogeneous ; Plastics extrusion ; Wetting ; Chemical engineering ; Chemistry ; Thermodynamics ; Mechanics ; Viscosity Polymer Foaming and Composites ; Injection Molding Process and Properties ; Phase Equilibria and Thermodynamics
Authors: Megan McCarthy | Hui Zheng | Diran Apelian | William J. Bowman | Horst Hahn | Jian Luo | Shyue Ping Ong | Xiaoqing Pan | Timothy J. Rupert
Source: arXiv (Cornell University) (preprint, 2021-06-17)
Field: Materials Science — Materials Chemistry

Emergence of near-boundary segregation zones in face-centered cubic\n multi-principal element alloys Grain boundaries have been shown to dramatically influence the behavior of\nrelatively simple materials such as monatomic metals and binary alloys. The\nincreased chemical complexity associated with multi-principal element alloys is\nhypothesized to lead to new grain boundary phenomena. To explore the\nrelationship between grain boundary structure and chemistry in these materials,\nhybrid molecular dynamics/Monte Carlo simulations of a faceted {\\Sigma}11 &lt;110&gt;\ntilt boundary, chosen to sample both high- and low-energy boundary\nconfigurations, are performed in face-centered cubic CrFeCoNiCu and CrFeCoNi\nequiatomic alloys. Unexpected enrichment of Fe is discovered in the\nface-centered cubic regions adjacent to the interface and found to be\ncorrelated with a structurally-distinct region of reduced atomic volume.\nComparison with the boundary of the same type in monatomic Cu demonstrates that\naltered near-boundary regions exist in simpler systems as well, with the\nchemical complexity of the multi-principal element alloys highlighting its\nexistence and importance.\n Monatomic ion ; Grain boundary ; Cubic crystal system ; Materials science ; Molecular dynamics ; Boundary (topology) ; Boundary element method ; Condensed matter physics ; Crystallography ; Metallurgy ; Finite element method ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Microstructure Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Sergey A. Astakhov | V. I. Biryukov
Source: Periodicals of Engineering and Natural Sciences (PEN) (article, 2021-09-30)
Field: Materials Science — General Materials Science

Algorithm for computational and experimental determination of the acoustic characteristics of solid propulsion engine Combustion instability in rocket engines is accompanied by large-amplitude pressure fluctuations in the combustion chamber and (or) intense vibration of the load-bearing structural elements. Unstable operation leads to a decrease in thrust, specific impulse, a decrease in engine life, a violation of the normal operation of vibration-sensitive flight control system equipment. The study considers a method for determining the frequencies of the first and second tone natural resonances of the longitudinal mode of acoustic vibrations in the combustion chambers of solid propulsion engines. The gas path of the combustion chamber is divided into homogeneous sections, for which solutions of the wave equation are presented. To determine the natural frequencies and the distribution of vibrational pressures and velocities, the method of "stitching" acoustic fields at the boundaries of cavities was applied. In the course of the study, to determine the decrement from natural perturbations, the following methods were used: spectral, correlation, amplitude, and instantaneous period method. In the study, vibrations of a certain frequency in a given range were excited using an electrodynamic emitter. To obtain the acoustic characteristics of the camera at various moments of engine operation, experiments were carried out with several charge models having different sizes. The solution of the wave equation in the form of standing waves was considered. In addition, the study results present a description of the experimental setup, in particular, the distribution of the oscillatory pressure in the chamber gas cavity (according to the calculated data). In the course of the study, an algorithm was developed for calculating and experimentally determining the acoustic characteristics of the combustion chambers of solid propulsion engines. Propulsion ; Computer science ; Aerospace engineering ; Acoustics ; Mechanical engineering ; Materials science ; Physics ; Automotive engineering ; Engineering Material Properties and Applications ; Vehicle Noise and Vibration Control ; Rocket and propulsion systems research
Authors: John E. Carpenter | Michael Gruenwald
Source:  (preprint, 2021-07-16)
Field: Materials Science — Materials Chemistry

Pre-Nucleation Clusters Predict Crystal Structures in Models of Chiral Molecules Kinetics can play an important role in the crystallization of molecules and give rise to polymorphism, the prevalent ability of molecules to form more than one crystal structure. Current computational methods of crystal structure prediction, however, focus almost exclusively on identifying the thermodynamically stable polymorph. Kinetic factors of nucleation and growth are often neglected because the underlying microscopic processes are thought to be complex and accurate rate calculations are numerically cumbersome. In this work, we use molecular dynamics computer simulations to study a simple molecular model that reproduces the crystallization behavior of real chiral molecules, including the formation of enantiopure and racemic crystals, as well as polymorphism. We show that in many cases, the crystal that robustly forms in simulations is not the one with the lowest free energy. We demonstrate that at high supersaturation the prevailing polymorph can be accurately predicted by considering the similarities between prevalent oligomeric species in solution and molecular motifs in the crystal structure. For the case of racemic mixtures, we even find that knowledge of crystal free energies is not necessary and kinetic considerations are sufficient to determine if the system will undergo spontaneous chiral separation. Our results suggest conceptually simple ways of improving current crystal structure prediction methods. Enantiopure drug ; Nucleation ; Crystallization ; Molecule ; Crystal structure prediction ; Crystal (programming language) ; Chemical physics ; Polymorphism (computer science) ; Molecular dynamics ; Supersaturation ; Kinetic energy ; Work (physics) ; Protein crystallization ; Materials science ; Crystal structure ; Chemistry ; Statistical physics ; Computational chemistry ; Crystallography ; Thermodynamics ; Physics ; Catalysis ; Computer science ; Organic chemistry ; Classical mechanics Crystallization and Solubility Studies ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Hung M. Le | Sushant Kumar | Nathan May | Ernesto Martínez-Baez | Ravishankar Sundararaman | Bala Krishnamoorthy | Aurora E. Clark
Source: ChemRxiv (preprint, 2021-09-21)
Field: Materials Science — Materials Chemistry

Behavior of Linear and Nonlinear DimensionalityReduction for Collective Variable Identification ofSmall Molecule Solution-Phase Reactions Identifying collective variables for chemical reactions is essential to reduce the 3$N$ dimensional energy landscape into lower dimensional basins and barriers of interest. However in condensed phase processes, the non-meaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis, although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., UMAP, that have become more popular recently. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in solution, and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. The first reaction consists of the dynamic exchange of a cation about a polyhydroxy anion that is facilitated by waters of solvation, while the second reaction consists of a nucleophilic attack of water upon ethylene to initiate cis/trans isomerization. When applied to raw data, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables in solution. Chemistry ; Solvation ; Dimensionality reduction ; Curse of dimensionality ; Interpretability ; Isomerization ; Solvent ; Principal component analysis ; Computational chemistry ; Energy landscape ; Biological system ; Computer science ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Gulnar Kim | Alexandr Demyanenko | Alexey Savostin | Kainizhamal Iklassova
Source: Eastern-European Journal of Enterprise Technologies (article, 2021-04-30)
Field: Chemistry — Analytical Chemistry

Devising a method for recognizing the causes of deviations in the development of the plant Aloe arborescens L. using machine learning capabilities This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields. The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set. The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively. The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values Artificial intelligence ; Convolutional neural network ; Quality (philosophy) ; Computer science ; Reliability (semiconductor) ; Process (computing) ; Sample (material) ; Pattern recognition (psychology) ; Machine learning ; Chemistry Spectroscopy and Chemometric Analyses
Authors: Julia Stille | Jevgenijs Tjutrins | Guanyu Wang | Felipe A. Venegas | Christopher Hennecker | Andres Mauricio Rueda | Itai Sharon | Caitlin E. Miron | Sharon Pinus | Anne Labarre | Jessica Plescia | Mihai Burai Patrascu | Xiaocong Zhang | Alexander S. Wahba | Danielle Vlaho | Mitchell Huot | T.M. Schmeing | Anthony Mittermaier | Nicolas Moitessier
Source: ChemRxiv (preprint, 2021-04-01)
Field: Chemistry — Organic Chemistry

Design, Synthesis and Biological Evaluation of Novel SARS-CoV-2 3CLpro Covalent Inhibitors Severe diseases such as the ongoing COVID-19 pandemic, as well as the previous SARS and MERS outbreaks, are the result of coronavirus infections and have demonstrated the urgent need for antiviral drugs to combat these deadly viruses. Due to its essential role in viral replication and function, 3CLpro has been identified as a promising target for the development of antiviral drugs. Previously reported SARS-CoV 3CLpro non-covalent inhibitors were used as a starting point for the development of covalent inhibitors of SARS-CoV-2 3CLpro. We report herein our efforts in design and synthesis which led to submicromolar covalent inhibitors when the enzymatic activity of the viral protease was used as a screening platform. Covalent bond ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Coronavirus disease 2019 (COVID-19) ; Virology ; Coronavirus ; Viral replication ; 2019-20 coronavirus outbreak ; Drug development ; Pandemic ; Function (biology) ; Chemistry ; Biology ; Outbreak ; Virus ; Pharmacology ; Medicine ; Drug ; Genetics Click Chemistry and Applications ; Computational Drug Discovery Methods ; Cancer therapeutics and mechanisms