jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter Fjodorow | Pitt Allmendinger | Raphael Horvath | Jürgen Herzler | Florian Eigenmann | Markus Geiser | Mustapha Fikri | Christof Schulz
Source: Applied Physics B (article, 2020-11-08)
Field: Chemistry — Spectroscopy

Monitoring formaldehyde in a shock tube with a fast dual-comb spectrometer operating in the spectral range of 1740–1790 cm–1 Abstract A dual-frequency-comb spectrometer based on two quantum-cascade lasers is applied to kinetics studies of formaldehyde (HCHO) in a shock tube. Multispectral absorption measurements are carried out in a broad spectral range of 1740–1790 cm –1 at temperatures of 800–1500 K and pressures of 2–3 bar. The formation of HCHO from thermal decomposition of 1,3,5-trioxane (C 3 H 6 O 3 , 0.9% diluted in argon) and the subsequent oxidation of formaldehyde is monitored with a time resolution of 4 µs. The rate coefficient of the decomposition of C 3 H 6 O 3 (i.e., HCHO formation) is found to be k 1 = 6.0 × 10 15 exp(− 205.58 kJ mol −1 / RT ) s –1 . For the oxidation studies, mixtures of 0.36% C 3 H 6 O 3 and 1% O 2 in argon are used. The information of all laser lines, along with the consideration of individual signal variance of each line, is utilized for kinetic and spectral analysis. The experimental kinetic profiles of HCHO are compared with simulations based on the mechanisms of Zhou et al. (Combust Flame, 197:423–438, 2018) and Cai and Pitsch (Combust Flame, 162:1623–1637, 2015). Formaldehyde ; Argon ; Shock tube ; Analytical Chemistry (journal) ; Materials science ; Spectrometer ; Radiative transfer ; Kinetic energy ; Optics ; Atomic physics ; Shock wave ; Chemistry ; Physics ; Thermodynamics ; Organic chemistry Spectroscopy and Laser Applications ; Atmospheric chemistry and aerosols ; Atmospheric Ozone and Climate
Authors: István Csarnovics | M. Vereš | Petr Němec | S. Molnár | S. Kökényesi
Source: Journal of Non-Crystalline Solids (article, 2020-12-06)
Field: Materials Science — Materials Chemistry

Surface plasmon enhanced light-induced changes in Ge-Se amorphous chalcogenide – gold nanostructures In this paper, the plasmon field enhanced light-induced changes are investigated in the amorphous Ge24Se76 layer incorporating Au nanoparticles. The presence of gold nanoobjects in amorphous chalcogenide/gold heterostructure influences the optical and structural properties, as well as volume changes of the material. It also enhances the Raman signal of the structure through surface enhancement, which allows clearer and better investigation of the glass structure and understanding of the mechanisms of surface plasmon stimulated transformations. In addition, it also enhances the recording of the optical grating pattern into the layer. Chalcogenide ; Materials science ; Amorphous solid ; Plasmon ; Surface plasmon ; Raman spectroscopy ; Colloidal gold ; Nanostructure ; Optoelectronics ; Localized surface plasmon ; Layer (electronics) ; Surface plasmon resonance ; Nanotechnology ; Nanoparticle ; Optics ; Chemistry ; Crystallography Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; melanin and skin pigmentation
Authors: Nasim Al Islam | Sangjo Choi
Source: Scientific Reports (article, 2020-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compact folded dipole metasurface for high anomalous reflection angles with low harmonic levels A dense unit cell array used in a metasurface for a high reflection angle (θ<sub>r</sub> > 50°) leads to high coupling among the unit cells; thus, parasitic reflections are unavoidable. The up-do-date patch-based metasurfaces for high reflection angles were electrically large (> 80 λ<sup>2</sup>), but for a practical point of view, a more compact metasurface design is needed. As a solution for these issues, we use the folded dipole-based unit cells with closed-loop currents for low near-field coupling and design compact metasurfaces (~ 40 λ<sup>2</sup>) for high reflection angles (θ<sub>r</sub> = 56° and 70°) at 10 GHz. The folded dipole unit cells are arranged according to the recently developed non-linear phase boundary condition for low harmonic reflections. As a counterpart, we also designed a metasurface using conventional patch-shaped unit cells with the same reflection phases (θ<sub>r</sub> = 70°). In experiments, the folded dipole metasurface shows lower harmonic levels (θ<sub>r</sub> = 0° and - 70°) and a comparable anomalous reflection (θ<sub>r</sub> = 70°) versus the patch-shaped metasurface. The time-domain analysis demonstrates that the low harmonic levels from the folded dipole metasurface are due to low scattering from the guided waves and the edge scattering. The proposed compact folded dipole-based metasurface with low undesired harmonics can be used as a practical reflect-array for millimeter-wave communication links. Reflection (computer programming) ; Dipole ; Scattering ; Optics ; Physics ; Harmonic ; Coupling (piping) ; Harmonics ; Materials science ; Acoustics ; Computer science ; Voltage Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Marcus D. Hanwell | Chris Harris | Alessandro Genova | Mojtaba Haghighatlari | Muammar El Khatib | Patrick Avery | Johannes Hachmann | Wibe A. de Jong
Source: International Journal of Quantum Chemistry (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Open Chemistry, <scp>JupyterLab</scp>, <scp>REST</scp>, and quantum chemistry Abstract Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open‐source framework that offers an end‐to‐end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open‐source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state‐of‐the‐art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data. Leverage (statistics) ; Computer science ; World Wide Web ; Visualization ; Quantum chemistry ; The Internet ; Chemistry ; Data science ; Nanotechnology ; Data mining ; Artificial intelligence ; Molecule ; Materials science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Various Chemistry Research Topics
Authors: In Ho Cho | Qiang Li | R. Biswas | Jaeyoun Kim
Source: Communications Physics (article, 2020-05-08)
Field: Materials Science — Materials Chemistry

A framework for glass-box physics rule learner and its application to nano-scale phenomena Abstract Attempts to use machine learning to discover hidden physical rules are in their infancy, and such attempts confront more challenges when experiments involve multifaceted measurements over three-dimensional objects. Here we propose a framework that can infuse scientists’ basic knowledge into a glass-box rule learner to extract hidden physical rules behind complex physics phenomena. A “convolved information index” is proposed to handle physical measurements over three-dimensional nano-scale specimens, and the multi-layered convolutions are “externalized” over multiple depths at the information level, not in the opaque networks. A transparent, flexible link function is proposed as a mathematical expression generator, thereby pursuing “glass-box” prediction. Consistent evolution is realized by integrating a Bayesian update and evolutionary algorithms. The framework is applied to nano-scale contact electrification phenomena, and results show promising performances in unraveling transparent expressions of a hidden physical rule. The proposed approach will catalyze a synergistic machine learning-physics partnership. Scale (ratio) ; Generator (circuit theory) ; Computer science ; Artificial intelligence ; Function (biology) ; Machine learning ; Physics ; Power (physics) Machine Learning in Materials Science ; Neural Networks and Applications ; Neural Networks and Reservoir Computing
Authors: Aditya R. Thawani | Ryan‐Rhys Griffiths | Arian R. Jamasb | Anthony Bourached | Penelope Jones | William McCorkindale | Alexander A. Aldrick | Alpha A. Lee
Source: ChemRxiv (preprint, 2020-07-06)
Field: Materials Science — Materials Chemistry

The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry The space of synthesizable molecules is greater than $10^{60}$, meaning only a vanishingly small fraction of these molecules have ever been realized in the lab. In order to prioritize which regions of this space to explore next, synthetic chemists need access to accurate molecular property predictions. While great advances in molecular machine learning have been made, there is a dearth of benchmarks featuring properties that are useful for the synthetic chemist. Focussing directly on the needs of the synthetic chemist, we introduce the Photoswitch Dataset, a new benchmark for molecular machine learning where improvements in model performance can be immediately observed in the throughput of promising molecules synthesized in the lab. Photoswitches are a versatile class of molecule for medical and renewable energy applications where a molecule's efficacy is governed by its electronic transition wavelengths. We demonstrate superior performance in predicting these wavelengths compared to both time-dependent density functional theory (TD-DFT), the incumbent first principles quantum mechanical approach, as well as a panel of human experts. Our baseline models are currently being deployed in the lab as part of the decision process for candidate synthesis. It is our hope that this benchmark can drive real discoveries in photoswitch chemistry and that future benchmarks can be introduced to pivot learning algorithm development to benefit more expansive areas of synthetic chemistry. Photoswitch ; Chemical space ; Benchmark (surveying) ; Computer science ; Expansive ; Artificial intelligence ; Nanotechnology ; Machine learning ; Chemistry ; Drug discovery ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Photochromic and Fluorescence Chemistry
Authors: Yongqiang Wang | Changhui Feng | Yu Luo
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The Development of Electrical Tree Discharge in Epoxy Resin Impregnated Paper Insulation An electrical tree discharge model of multilayer insulation paper was prepared, and a voltage of 20 kV was used for electrical tree cultivation and initiation tests. Based on the microscopic image information and partial discharge (PD) information during the aging of the electrical tree, the development of electrical tree discharges between layers of paper comprising epoxy resin-impregnated paper insulation was studied. The results show that, by analysing PD information such as the discharge volume phase spectrum and the discharge repetition rate phase spectrum, the development of the electrical tree branch progresses in four stages: initial, growth, stagnation, and bursting. During the growth and stagnation stages, the main discharge paths of the electrical tree are mainly formed, and there are many filamentous fibres on the path edge. During the burst stage, more branches will be formed on the electrical tree, which will cause the greatest damage to the insulation. With the extension of the electrical tree path, the discharge magnitude and discharge times also increase. There are specific discharge patterns in different growth stages of the electrical tree, and the number of discharge events in each positive half-cycle is significantly greater than that in a negative half-cycle. Combining the microscopic image information and PD information during the aging of such an electrical tree, a theoretical basis for evaluating insulation state by PD monitoring is realised. The long-chain molecules of cellulose and epoxy resin break bonds and oxidise, which decompose into a large number of free radicals, monosaccharides, and other low-molecular-weight products. Epoxy ; Partial discharge ; Materials science ; Composite material ; Tree (set theory) ; Electrical breakdown ; Electrical treeing ; Voltage ; Electrical engineering ; Optoelectronics ; Dielectric ; Mathematics High voltage insulation and dielectric phenomena ; Embedded Systems and FPGA Design ; Power Transformer Diagnostics and Insulation
Authors: Jyothi Bhandari | Neha Kanswami | Lakshmi PK
Source: Journal of Young Pharmacists (article, 2020-08-10)
Field: Materials Science — Materials Chemistry

Nano Co-crystal Engineering Technique to Enhance the Solubility of Ezetimibe Published on:July 2020 Journal of Young Pharmacists , 2020; 12(2s):s10-s15 Original Article | doi:10.5530/jyp.2020.12s.40 Authors: Jyothi Bhandari1, Neha Kanswami2, Lakshmi P.K2* 1Department of Pharmaceutics, St. Paul College of Pharmacy, Hyderabad, Telangana, INDIA. 2Department of Pharmaceutics, G. Pulla Reddy College of Pharmacy, Mehdipatnam, Hyderabad, Telangana, INDIA. Abstract: Background: Co-crystals have been highly promising for tailoring physicochemical properties Ezetimibe ; Nano- ; Solubility ; Materials science ; Crystal (programming language) ; Nanotechnology ; Chemical engineering ; Chemistry ; Medicine ; Organic chemistry ; Engineering ; Computer science ; Composite material ; Internal medicine ; Cholesterol Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Mingshu Zhao | Xiaoyan Cao | Kai Zhou | Yao Fu | Xutao Li | Li Wan
Source: Sensors (article, 2020-11-20)
Field: Materials Science — Materials Chemistry

Flexible Sensor Array Based on Transient Earth Voltage for Online Partial Discharge Monitoring of Cable Termination Cable termination is a weak point in an underground cable system. The transient earth voltage (TEV) method is an effective and nonintrusive method for estimating the insulation condition of cable termination. However, the practical application of TEV detection is mainly focused on switchgears, generators, and transformers with a flat and conductive shell. A flexible sensor array based on the TEV method is presented for online partial discharge (OLPD) monitoring of the cable termination. Each sensing element is designed with a dual-capacitor structure made of flexible polymer material to obtain better and more stable sensitivity. Based on the electromagnetic (EM) wave propagation theory, the partial discharge (PD) propagation model in the cable termination is built to analyze and verify the rationality and validity of the sensor unit. Some influencing factors are discussed regarding the response characteristics of sensors. Finally, the performance of the sensor array is verified by simulations and experiments. Besides, an OLPD monitoring system is introduced. The monitoring system is composed of the on-site monitoring device and the remote monitoring host. The two parts of the system exchange the data through wireless networks using a wireless communication module. The experiment results show that the monitoring device could supply the PD condition monitoring demand for cable termination. Partial discharge ; Switchgear ; Transient (computer programming) ; Transformer ; Electrical engineering ; Wireless ; Continuous monitoring ; Voltage ; Engineering ; Electronic engineering ; Computer science ; Telecommunications High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Lightning and Electromagnetic Phenomena
Authors: Zhudan Chen | Dazi Li | Haixiao Wan | Minghui Liu | Jun Liu
Source: Molecular Simulation (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning methods for polymer nanocomposites data via molecular dynamics simulation Molecular dynamics (MD) simulation has been an invaluable tool for polymer nanocomposites (PNCs) research. MD simulation investigations provide massive data about the movements of PNCs beads on the micro-level. However, there are many challenges involved in correctly understanding and predicting the performance and properties of molecules from these massive MD simulation data. Traditional data-driven techniques are limited due to computational expense, required accuracy, and the availability of high-dimensional PNCs structures. Machine learning can give an effort to process massive data, but not all machine learning methods are available for all occasions; domain knowledge or manual intervention is required. Here, we aim to investigate unsupervised machine learning algorithms to discover clustering and shaping movements of PNCs beads under tension. Cluster Envelope Shaping Method (CESM) was proposed to analyze the clustering of beads and the shaping of clusters based on MD simulation data. The matrix-free PNC based elastomer simulation and Single-Chain Polymer Nanoparticles simulation, which demonstrate two types of deformations, verified the validity of this method. Cluster analysis ; Computer science ; Molecular dynamics ; Unsupervised learning ; Artificial intelligence ; Cluster (spacecraft) ; Process (computing) ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Kyohei Hanaoka
Source: ACS Omega (article, 2020-08-10)
Field: Materials Science — Materials Chemistry

Deep Neural Networks for Multicomponent Molecular Systems Deep neural networks (DNNs) represent promising approaches to molecular machine learning (ML). However, their applicability remains limited to single-component materials and a general DNN model capable of handling various multicomponent molecular systems with composition data is still elusive, while current ML approaches for multicomponent molecular systems are still molecular descriptor-based. Here, a general DNN architecture extending existing molecular DNN models to multicomponent systems called MEIA is proposed. Case studies showed that the MEIA architecture could extend two exiting molecular DNN models to multicomponent systems with the same procedure, and that the obtained models that could learn both the molecular structure and composition information with equal or better accuracies compared to a well-used molecular descriptor-based model in the best model for each case study. Furthermore, the case studies also showed that, for ML tasks where the molecular structure information plays a minor role, the performance improvements by DNN models were small; while for ML tasks where the molecular structure information plays a major role, the performance improvements by DNN models were large, and DNN models showed notable predictive accuracies for an extremely sparse dataset, which cannot be modeled without the molecular structure information. The enhanced predictive ability of DNN models for sparse datasets of multicomponent systems will extend the applicability of ML in the multicomponent material design. Furthermore, the general capability of MEIA to extend DNN models to multicomponent systems will provide new opportunities to utilize the progress of actively developed single-component DNNs for the modeling of multicomponent systems. Component (thermodynamics) ; Computer science ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Zeren Shui | George Karypis
Source: arXiv (Cornell University) (preprint, 2020-09-26)
Field: Materials Science — Materials Chemistry

Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules. Most of the existing methods treat molecules as molecular graphs in which atoms are modeled as nodes. They characterize each atom's chemical environment by modeling its pairwise interactions with other atoms in the molecule. Although these methods achieve a great success, limited amount of works explicitly take many-body interactions, i.e., interactions between three and more atoms, into consideration. In this paper, we introduce a novel graph representation of molecules, heterogeneous molecular graph (HMG) in which nodes and edges are of various types, to model many-body interactions. HMGs have the potential to carry complex geometric information. To leverage the rich information stored in HMGs for chemical prediction problems, we build heterogeneous molecular graph neural networks (HMGNN) on the basis of a neural message passing scheme. HMGNN incorporates global molecule representations and an attention mechanism into the prediction process. The predictions of HMGNN are invariant to translation and rotation of atom coordinates, and permutation of atom indices. Our model achieves state-of-the-art performance in 9 out of 12 tasks on the QM9 dataset. Molecular graph ; Computer science ; Theoretical computer science ; Leverage (statistics) ; Artificial neural network ; Pairwise comparison ; Graph ; Invariant (physics) ; Molecule ; Topology (electrical circuits) ; Biological system ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Combinatorics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jian Peng | Yukinori Yamamoto | Michael P. Brady | Sangkeun Lee | J. Allen Haynes | Dongwon Shin
Source: JOM (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys Creep ; Oak Ridge National Laboratory ; Ridge ; Austenite ; Materials science ; Feature (linguistics) ; Set (abstract data type) ; Alloy ; Machine learning ; Computer science ; Artificial intelligence ; Metallurgy ; Geology Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Machine Learning in Materials Science
Authors: Marianne Liu | Conrad Clement | Kathy Liu | Xuming Wang | Taylor D. Sparks
Source: Computational Materials Science (article, 2020-11-07)
Field: Materials Science — Materials Chemistry

A data science approach for advanced solid polymer electrolyte design Computer science ; Random forest ; Support vector machine ; Mean squared error ; Lasso (programming language) ; Battery (electricity) ; Regression ; Linear regression ; Machine learning ; Artificial intelligence ; Process engineering ; Statistics ; Mathematics ; Engineering ; Power (physics) ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies