jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shota Ono
Source: Physical Review Materials (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Comprehensive search for buckled honeycomb binary compounds based on noble metals (Cu, Ag, and Au) Honeycomb structure has frequently been observed in two-dimensional (2D) materials. CuAu in the buckled honeycomb (BHC) structure was synthesized recently, which is a case of a 2D intermetallic compound. Here, the dynamical stability of 2D $AX$ in the BHC structure, where $A=\mathrm{Cu}$, Ag, and Au, and $X$ is a metallic element in the Periodic Table, is systematically studied by calculating phonon dispersions from first-principles. Among 135 $AX$, 48 $AX$ are identified to be dynamically stable. In addition, we discuss (i) the relationship between the dynamical stability and the formation energy, (ii) a correlation of the dynamical stability between different constituents $A$, and (iii) trends of the structural, elastic, electronic, and magnetic properties. Furthermore, a stable phase of the B11-type AuZr is predicted based on both the result (ii) and the stability relationship between 2D and 3D structures. The stability of B11 AuZr is consistent with other predictions using the machine learning approach. The present findings should stimulate future studies exploring the physics and chemistry of 2D intermetallic compounds. Intermetallic ; Materials science ; Honeycomb ; Structural stability ; Stability (learning theory) ; Phonon ; Honeycomb structure ; Crystallography ; Metal ; Binary number ; Chemical physics ; Condensed matter physics ; Composite material ; Metallurgy ; Physics ; Structural engineering ; Chemistry ; Mathematics ; Alloy Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Graphene research and applications
Authors: Omri Abarbanel | Geoffrey Hutchison
Source:  (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

Machine Learning to Accelerate Screening for Marcus Reorganization Energies Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters. Intramolecular force ; Charge (physics) ; Marcus theory ; Work (physics) ; Density functional theory ; Computer science ; Transistor ; Range (aeronautics) ; Materials science ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Voltage ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Conducting polymers and applications
Authors: Emily C. Schueller | Yuzki M. Oey | Kyle D. Miller | Kira E. Wyckoff | Ruining Zhang | William Zhang | Stephen D. Wilson | James M. Rondinelli | Ram Seshadri
Source: Inorganic Chemistry (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

AB<sub>2</sub>X<sub>6</sub> Compounds and the Stabilization of Trirutile Oxides The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB<sub>2</sub>X<sub>6</sub> (AB<sub>2</sub>O<sub>6</sub> and AB<sub>2</sub>F<sub>6</sub>) composition space with the specific goal of finding new oxide compounds in the trirutile family, which is known for unusual one-dimensional (1D) antiferromagnetic behavior. Through machine learning methods, we develop an understanding of how geometric and bonding constraints determine the crystallization of compounds in the trirutile structure as opposed to other ternary structures in this space. In combination with density functional theory (DFT) calculations, we predict 16 previously unreported candidate trirutile oxides. We successfully prepare one of these and show it forms in the disordered rutile structure, under the preparation conditions adopted here. Chemistry ; Electronegativity ; Ternary operation ; Antiferromagnetism ; Magnetism ; Crystal structure ; Ionic bonding ; Density functional theory ; Crystallography ; Rutile ; Ionic radius ; Space (punctuation) ; Chemical physics ; Oxide ; Computational chemistry ; Condensed matter physics ; Ion ; Physics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; Inorganic Chemistry and Materials
Authors: Natália R. S. Araújo | Álvaro C.M. Duarte | Fabrício José Pacheco Pujatti | Maria Betânia de Freitas Marques | Rita C. O. Sebastião
Source: Thermochimica Acta (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Kinetic models and distribution of activation energy in complex systems using Hopfield Neural Network Artificial neural network ; Kinetic energy ; Biological system ; Activation energy ; Thermal ; Process (computing) ; Work (physics) ; Thermodynamics ; Coke ; Computer science ; Materials science ; Process engineering ; Chemistry ; Artificial intelligence ; Physical chemistry ; Physics ; Engineering ; Metallurgy Thermal and Kinetic Analysis ; Petroleum Processing and Analysis ; Iron and Steelmaking Processes
Authors: Haixin Wei | Zekai Zhao | Ray Luo
Source: Journal of Chemical Theory and Computation (article, 2021-09-13)
Field: Materials Science — Materials Chemistry

Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES in almost all situations. Further analyses show that the machine-learned SES is incredibly stable in terms of rotational variation of tested molecules. Our timing analysis shows that the machine-learned SES is roughly 2.5 times as efficient as the classical SES routine implemented in Amber/PBSA on a tested central processing unit (CPU) platform. We expect further performance gain on massively parallel platforms such as graphics processing units (GPUs) given the ease in converting the machine-learned SES to a parallel procedure. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with a 1% deviation on average. Given its level set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms. Computer science ; Field (mathematics) ; Set (abstract data type) ; Surface (topology) ; Graphics processing unit ; Massively parallel ; Computational science ; Interface (matter) ; Artificial intelligence ; Machine learning ; Parallel computing ; Mathematics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Suman Yadav | Harold R. Chamorro | Wilfredo C. Flores | Ram Krishna Mehta
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM The heat dissipation factor of conventional epoxy impregnated paper bushings is a subject of concern due to the large quantities of power in a High Voltage Direct Current (HVDC) system. The present work deals with the selection of better insulation as a replacement to the conventional resin impregnated material employing nano-hexagonal-Boron Nitride and nano-hexagonal-Boron Nitride added with nano-cellulose-fiber. The bushing of the converter transformer is designed using the Finite Element Method (FEM), and the electrothermal analysis is performed at the loaded working condition. Besides, numerous optimization schemes are also presented for adapting the structure of the thermal conductor enclosed in the inner conductor. The electrothermal performances of the above materials with the optimized structure are compared and an advanced scheme is proposed. Further, the results obtained from the designed system are employed in the form of an Artificial Neural Network to simplify the process of thermal computation characterized by the selected scheme. The internal parameters of the neural network are tuned implementing a hybrid amalgamation of Particle Swarm optimization - Grey Wolf Optimiser and the performance is compared against the actual values. The supremacy of the implemented algorithm is justified by a comparative analysis with other well-established algorithms using various statistical parameters. Bushing ; Materials science ; Finite element method ; Transformer ; Conductor ; Boron nitride ; Epoxy ; Dissipation ; Nano- ; Composite material ; Voltage ; Mechanical engineering ; Electrical engineering ; Structural engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Zachary A. VanOrman | Lea Nienhaus
Source: Matter (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Feeling blue no more: How TIPS-naphthalene enables efficient visible-to-UV upconversion Photon upconversion ; Annihilation ; Triplet state ; Photochemistry ; Singlet state ; Chemistry ; Excited state ; Materials science ; Optoelectronics ; Physics ; Atomic physics ; Doping ; Particle physics Luminescence and Fluorescent Materials ; Perovskite Materials and Applications ; Organic Light-Emitting Diodes Research
Authors: Jason R. Hall | Steven K. Kauwe | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

Sequential Machine Learning Applications of Particle Packing with Large Size Variations Sphere packing ; Ternary operation ; Atomic packing factor ; Particle (ecology) ; Monte Carlo method ; Materials science ; RADIUS ; SPHERES ; Binary number ; Dimension (graph theory) ; Work (physics) ; Statistical physics ; Computer science ; Algorithm ; Mathematics ; Physics ; Statistics ; Thermodynamics ; Composite material ; Combinatorics Thermal properties of materials ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Ravinder Bhattoo | Suresh Bishnoi | Mohd Zaki | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2021-03-22)
Field: Materials Science — Ceramics and Composites

Revealing the Compositional Control of Electrical, Mechanical, Optical, and Physical Properties of Inorganic Glasses Inorganic glasses, produced by the melt-quenching of a concoction of minerals, compounds, and elements, can possess unique optical and elastic properties along with excellent chemical, and thermal durability. Despite the ubiquitous use of glasses for critical applications such as touchscreen panels, windshields, bioactive implants, optical fibers and sensors, kitchen and laboratory glassware, thermal insulators, nuclear waste immobilization, optical lenses, and solid electrolytes, their composition-structure-property relationships remain poorly understood. Here, exploiting largescale experimental data on inorganic glasses and explainable machine learning algorithms, we develop composition-property models for twenty-five properties, which are in agreement with experimental observations. These models are further interpreted using a game-theoretic concept namely, Shapley additive explanations, to understand the role of glass components in controlling the final property. The analysis reveals that the components present in the glass, such as network formers, modifiers, and the intermediates, play distinct roles in governing each of the optical, physical, electrical, and mechanical properties of glasses. Additionally, these components exhibit interdependence, the magnitude of which is different for different properties. While the physical origins of some of these interdependencies could be attributed to known phenomena such as "boron anomaly", "mixed modifier effect", and the "Loewenstein rule", the majority of the remaining ones requires further experimental and computational analysis of the glass structure. Thus, our work paves the way for decoding the "glass genome", which can provide the recipe for discovery of novel glasses, while also shedding light into the fundamental factors governing the composition-structure-property relationships. Materials science ; Physical property ; Thermal ; Nanotechnology ; Composite material ; Physics ; Thermodynamics Glass properties and applications ; Phase-change materials and chalcogenides ; Sensory Analysis and Statistical Methods
Authors: Andreas Rauh
Source: Clean Technologies (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells The electric power characteristic of solid oxide fuel cells (SOFCs) depends on numerous influencing factors. These are the mass flow of supplied hydrogen, the temperature distribution in the interior of the fuel cell stack, the temperatures of the supplied reaction media at the anode and cathode, and—most importantly—the electric current. Describing all of these dependencies by means of analytic system models is almost impossible. Therefore, it is reasonable to identify these dependencies by means of stochastic filter techniques. One possible option is the use of Kalman filters to find locally valid approximations of the power characteristics. These can then be employed for numerous online purposes of dynamically operated fuel cells such as maximum power point tracking or the maximization of the fuel efficiency. In the latter case, it has to be ensured that the fuel cell operation is restricted to the regime of Ohmic polarization. This aspect is crucial to avoid fuel starvation phenomena which may not only lead to an inefficient system operation but also to accelerated degradation. In this paper, a Kalman filter-based, real-time implementable optimization of the fuel efficiency is proposed for SOFCs which accounts for the aforementioned feasibility constraints. Essentially, the proposed strategy consists of two phases. First, the parameters of an approximation of the electric power characteristic are estimated. The measurable arguments of this function are the hydrogen mass flow and the electric stack current. In a second stage, these inputs are optimized so that a desired stack power is attained in an optimal way. Simulation results are presented which show the robustness of the proposed technique against inaccuracies in the a-priori knowledge about the power characteristics. For a numerical validation, three different models of the electric power characteristic are considered: (i) a static neural network input/output model, (ii) a first-order dynamic system representation and (iii) the combination of a static neural network model with a low-order fractional differential equation model representing transient phases during changes between different electric operating points. Stack (abstract data type) ; Electric power ; Kalman filter ; Anode ; Extended Kalman filter ; Computer science ; Solid oxide fuel cell ; Power (physics) ; Maximization ; Control theory (sociology) ; Mathematical optimization ; Chemistry ; Mathematics ; Physics ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: Hossein Tahmasbi | Stefan Goedecker | S. Alireza Ghasemi
Source: Physical Review Materials (article, 2021-08-30)
Field: Materials Science — Materials Chemistry

Large-scale structure prediction of near-stoichiometric magnesium oxide based on a machine-learned interatomic potential: Crystalline phases and oxygen-vacancy ordering Using a fast and accurate neural network potential, we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., ${\mathrm{Mg}}_{x}{\mathrm{O}}_{1\ensuremath{-}x}$ with $0.50&lt;x&lt;0.60$. Our extensive exploration demonstrates that for bulk stoichiometric compounds, there are several new low-energy rock-salt-like structures in which Mg atoms are octahedrally six-coordinated and form trigonal prismatic motifs with different stacking sequences. Furthermore, we find a dense spectrum of novel nonstoichiometric crystal phases of ${\mathrm{Mg}}_{x}{\mathrm{O}}_{1\ensuremath{-}x}$ for each composition of $x$. These structures are mostly similar to the rock-salt structure with octahedral coordination and five-coordinated Mg atoms. Due to the removal of one oxygen atom, the energy landscape becomes more glasslike with oxygen-vacancy type structures that all lie very close to each other energetically. For the same number of magnesium and oxygen atoms, our oxygen-deficient structures are lower in energy if the vacancies are aligned along lines or planes than rock-salt structures with randomly distributed oxygen vacancies. We also found the putative global minima configurations for each composition of the nonstoichiometric suboxide structures. These structures are predominantly composed of $\mathrm{MgO}(111)$ layers of the rock-salt structure which are terminated with Mg atoms at the top and bottom and are stacked in different sequences along the $z$ direction. Like for other materials, these Magn\'eli-type phases have properties that differ considerably from their stoichiometric counterparts such as high electrical conductivity. Stoichiometry ; Vacancy defect ; Magnesium ; Oxide ; Materials science ; Scale (ratio) ; Oxygen ; Interatomic potential ; Chemical physics ; Crystallography ; Chemistry ; Computational chemistry ; Physical chemistry ; Metallurgy ; Molecular dynamics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Samar Elaraby | Sherif M. Abuelenin | Adel Moussa | Yasser M. Sabry
Source: Foundations (article, 2021-12-15)
Field: Chemistry — Spectroscopy

Deep Learning on Synthesized Sensor Characteristics and Transmission Spectra Enabling MEMS-Based Spectroscopic Gas Analysis beyond the Fourier Transform Limit Miniaturized Fourier transform infrared spectrometers serve emerging market needs in many applications such as gas analysis. The miniaturization comes at the cost of lower performance than bench-top instrumentation, especially for the spectral resolution. However, higher spectral resolution is needed for better identification of the composition of materials. This article presents a convolutional neural network (CNN) for 3X resolution enhancement of the measured infrared gas spectra using a Fourier transform infrared (FTIR) spectrometer beyond the transform limit. The proposed network extracts a set of high-dimensional features from the input spectra and constructs high-resolution outputs by nonlinear mapping. The network is trained using synthetic transmission spectra of complex gas mixtures and simulated sensor non-idealities such as baseline drifts and non-uniform signal-to-noise ratio. Ten gases that are relevant to the natural and bio gas industry are considered whose mixtures suffer from overlapped features in the mid-infrared spectral range of 2000–4000 cm−1. The network results are presented for both synthetic and experimentally measured spectra using both bench-top and miniaturized MEMS spectrometers, improving the resolution from 60 cm−1 to 20 cm−1 with a mean square error down to 2.4×10−3 in the transmission spectra. The technique supports selective spectral analysis based on miniaturized MEMS spectrometers. Spectrometer ; Spectral resolution ; Fourier transform ; Spectral line ; Fourier transform spectroscopy ; Fourier transform infrared spectroscopy ; Miniaturization ; Resolution (logic) ; Infrared ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Optics ; Chemistry ; Physics ; Nanotechnology ; Artificial intelligence Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alberto Rossi | Federico Alberini | Elisabetta Brunazzi
Source: Process Safety and Environmental Protection (article, 2021-10-30)
Field: Chemistry — Analytical Chemistry

Identification of suspension state using passive acoustic emission and machine learning in a solid–liquid mixing system Impeller ; Fast Fourier transform ; Suspension (topology) ; Frequency domain ; Acoustics ; SIGNAL (programming language) ; Materials science ; Acoustic emission ; Oscilloscope ; Computer science ; Engineering ; Algorithm ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Fluid Dynamics and Mixing ; Mineral Processing and Grinding