jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Etienne Palos | Armando Reyes-Serrato | G. Alonso‐Núñez | J. Guerrero-Sánchez
Source: ChemRxiv (preprint, 2020-07-03)
Field: Materials Science — Materials Chemistry

Modeling the Ternary Chalcogenide Na2MoSe4 from First-Principles In the ongoing pursuit of inorganic compounds suitable for solid-state devices, transition metal chalcogenides have received heightened attention due to their physical and chemical properties. Recently, alkali-ion transition metal chalcogenides have been explored as promising candidates to be applied in optoelectronics, photovoltaics and energy storage devices. In this work, we present a comprehensive theoretical study of sodium molybdenum selenide (Na&lt;sub&gt;2&lt;/sub&gt;MoSe&lt;sub&gt;4&lt;/sub&gt;). First-principles computations were performed on a set of hypothetical crystal structures to determine the ground state and electronic properties of Na&lt;sub&gt;2&lt;/sub&gt;MoSe&lt;sub&gt;4&lt;/sub&gt;. We find that the equilibrium structure of Na&lt;sub&gt;2&lt;/sub&gt;MoSe&lt;sub&gt;4&lt;/sub&gt; is a simple orthorhombic (&lt;i&gt;oP&lt;/i&gt;) lattice, with space group Pnma, as evidenced by thermodynamics. Electronic structure computations reveal that three phases are semiconducting, while one (&lt;i&gt;cF&lt;/i&gt;) is metallic. Relativistic effects and Coulomb interaction of localized electrons were assessed for the &lt;i&gt;oP&lt;/i&gt; phase, and found to have a negligible influence on the band strucutre. Finally, meta-GGA computations were performed to model the band structure of primitive orthorhombic Na&lt;sub&gt;2&lt;/sub&gt;MoSe&lt;sub&gt;4&lt;/sub&gt; at a predictive level. We employ the Tran-Blaha modified Becke-Johnson potential to demonstrate that &lt;i&gt;oP&lt;/i&gt; Na2MoSe4 is a semiconductor with a direct bandgap of 0.53 eV at the &lt;b&gt;Γ&lt;/b&gt; point. Our results provide a foundation for future studies concerned with the modeling of inorganic and hybrid organic-inorganic materials chemically analogous to Na&lt;sub&gt;2&lt;/sub&gt;MoSe&lt;sub&gt;4&lt;/sub&gt;.&lt;br&gt; Orthorhombic crystal system ; Selenide ; Ternary operation ; Chalcogenide ; Materials science ; Chalcogen ; Electronic structure ; Crystallography ; Crystal structure ; Chemistry ; Computational chemistry ; Optoelectronics ; Selenium ; Metallurgy 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Mariia Karabin
Source: TigerPrints (Clemson University) (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Applying Statistical Mechanics to Improve Computational Sampling Algorithms and Interatomic Potentials In this dissertation the application of statistical mechanics is presented to improve classical simulated annealing and machine learning-based interatomic potentials.\nClassical simulated annealing is known to be among the most robust global optimization methods. Therefore, many variations of this method have been developed over the last few decades. This dissertation introduces simulated annealing with adaptive cooling and shows its efficiency with respect to the classical simulated annealing. Adaptive cooling simulated annealing makes use of the on-the-fly evaluation of the sta- tistical mechanical properties to adaptively adjust the cooling rate. In this case, the cooling rate is adaptively adjusted based on the instantaneous evaluations of the heat capacities, with the possible future extension to the density of states. Results are presented for Lennard-Jones clusters optimized by adaptive cooling sim- ulated annealing and the classical simulated annealing. The adaptive cooling approach proved to be more efficient than the classical simulated annealing.\nStatistical mechanics was also used to improve the quality and transferability of machine learning- based interatomic potentials. Machine learning (ML)-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which is a highly labor-intensive activity. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets[1]. Algorithm ; Statistical mechanics ; Computer science ; Statistical physics ; Computational mechanics ; Sampling (signal processing) ; Physics ; Finite element method ; Thermodynamics Electrostatics and Colloid Interactions ; Neural Networks and Applications ; Gaussian Processes and Bayesian Inference
Authors: Jaza Anwar Sayyed | Ansari Ammara Firdaus | Ansari Novman Nabeel
Source: Turkish Online Journal of Qualitative Inquiry (review, 2020-08-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review On The Thermal Conductivity Of Materials: Mechanisms, Measurement Techniques, And Applications With an emphasis on atomic structure, bonding, defect concentration, and microstructural features, this article investigates the variables affecting heat conductivity in metals, ceramics, polymers, and composites. The features of heat transfer, such as temperature, pressure, and material phase changes, are also examined. In metals, thermal conductivity is controlled by electron mobility, but in ceramics and polymers, it is determined by phonon interactions and molecular structure. Additionally covered in the review are experimental and computational approaches to measuring and forecasting thermal conductivity, such as transient or non-steady-state technologies like laser flash and transient hot-wire methods, as well as steady-state approaches like guarded hot plate and heat flow meter measurements. The benefits and drawbacks of each strategy, as well as how well they work with various materials, are also covered in the article. Heat conductivity has been improved using novel materials engineering approaches, including graphene, nanoparticles, nanowires, and composites containing phase-change components or high-conductivity fillers. Electronic heat sinks, thermal interface materials, high-performance computer systems, thermal barrier coatings, cryogenic storage, and energy-efficient architectural designs are some examples of the practical uses for materials with high and low heat conductivity. This research contributes significantly to clean energy, sustainable infrastructure, energy-efficient buildings, and climate resilience, aligning with global sustainability efforts. Thermal conductivity ; Materials science ; Composite material Thermal properties of materials ; Heat Transfer and Optimization
Authors: Sii Hong Lau | Peng Yu | Liye Chen | Christina B. Madsen-Duggan | Michael E. Williams | Brad P. Carrow
Source: ChemRxiv (preprint, 2020-09-04)
Field: Chemistry — Organic Chemistry

Aryl Amination with Soluble Weak Base Enabled by a Water-Assisted Mechanism Amination of aryl halides has become one of the most commonly practiced C–N bond-forming reactions in pharmaceutical and laboratory synthesis. The widespread use of strong or poorly soluble inorganic bases for amine activation nevertheless complicates the compatibility of this important reaction class with sensitive substrates as well as applications in flow and automated synthesis, to name a few. We report a palladium-catalyzed C–N coupling using Et3N as a weak, soluble base, which allows a broad substrate scope that includes bromo- and chloro(hetero)arenes, primary anilines, secondary amines, and amide type nucleophiles together with tolerance for a range of base-sensitive functional groups. Mechanistic data have established a unique pathway for these reactions in which water serves multiple beneficial roles. In particular, ionization of a neutral catalytic intermediate via halide displacement by H2O generates, after proton loss, a coordinatively-unsaturated Pd–OH species that can bind amine substrate triggering intramolecular N–H heterolysis. This water-assisted pathway operates efficiently with even weak terminal bases, such as Et3N. The use of a simple, commercially available ligand, PAd3, is key to this water-assisted mechanism by promoting coordinative unsaturation in catalytic intermediates responsible for the heterolytic activation of strong element-hydrogen bonds, which enables broad compatibility of carbon-heteroatom cross-coupling reactions with sensitive substrates and functionality. Heterolysis ; Chemistry ; Amination ; Aryl ; Nucleophile ; Catalysis ; Combinatorial chemistry ; Intramolecular force ; Electrophile ; Amine gas treating ; Palladium ; Photochemistry ; Organic chemistry ; Alkyl Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: M Schmidt | C Grill | R Huber | C Jirauschek | A Rudenkov | V Kisel | A Yasukevich | K Hovhannesyan | A Petrosyan | N Rubtsova | A Kovalyov | V Preobrazhenskii | N Kondratyuk | D Homan | N Kuleshov | S Aleshkina | M Likhachev | Prokhorov | O Antipov | B Denker | V Dorofeev | B Galagan | V Koltashev | S Motorin | S Sverchkov | V Plotnichenko | E Erushin | A Boyko | N Kostyukova | I Sherstov | D Kolker | M Churbanov | G Snopatin | M Sukhanov | A Velmushov | K Gorbachenya | R Deineka | E Vilejshikova | V Maltsev | D Mitina | E Volkova | N Leonyuk | Makarov | Yu Katsev | V Stupnikov | V Voropaev | D Batov | A Voronets | D Vlasov | A Donodin | M Tarabrin | V Lazarev | A Khegai | M Yashkov | V Karasik | A Dostovalov | A Wolf | M Skvortsov | K Proskurina | S Babin | L Kotov | V Temyanko | M Bubnov | D Lipatov | N Peyghambarian | A Budarnykh | I Lobach | S Kablukov | P Loiko | E Kifle | C Romero | J Rodrguez Vzquez De Aldana | Tae Park | Ji Bae | F Rotermund | P Camy | U Griebner | V Petrov | M Aguil | F Daz | X Mateos | M Ponarina | A Okhrimchuk | M Rybin | T Dolmatov | V Bukin | P Obraztsov | V Karpov | W Clements | V Balashov | E Cheshev | A Kanaev | A Kaminskii | Yu Kopylov | A Koromyslov | S Kozlova | K Lopukhin | I Tupitsyn | L Zakharov | A Inyushkin
Source:  (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Table of Contents made by thermal diffusion bonding of dissimilar materials Yb:YAG and sapphire Sapphire ; Table (database) ; Materials science ; Diffusion bonding ; Diffusion ; Optics ; Computer science ; Composite material ; Database ; Thermodynamics ; Laser ; Physics Luminescence Properties of Advanced Materials ; Solid State Laser Technologies ; Phase-change materials and chalcogenides
Authors: Brian Shevitski | Christopher T. Chen | Christoph Kastl | Tevye Kuykendall | Adam Schwartzberg | Shaul Aloni | Alex Zettl
Source: Scientific Reports (preprint, 2020-07-14)
Field: Materials Science — Materials Chemistry

Characterizing transition-metal dichalcogenide thin-films using hyperspectral imaging and machine learning Atomically thin polycrystalline transition-metal dichalcogenides (TMDs) are relevant to both fundamental science investigation and applications. TMD thin-films present uniquely difficult challenges to effective nanoscale crystalline characterization. Here we present a method to quickly characterize the nanocrystalline grain structure and texture of monolayer WS<sub>2</sub> films using scanning nanobeam electron diffraction coupled with multivariate statistical analysis of the resulting data. Our analysis pipeline is highly generalizable and is a useful alternative to the time consuming, complex, and system-dependent methodology traditionally used to analyze spatially resolved electron diffraction measurements. Materials science ; Nanocrystalline material ; Hyperspectral imaging ; Crystallite ; Thin film ; Monolayer ; Transition metal ; Nanotechnology ; Nanoscopic scale ; Characterization (materials science) ; Diffraction ; Nanomaterials ; Texture (cosmology) ; Optoelectronics ; Computer science ; Optics ; Chemistry ; Artificial intelligence ; Metallurgy ; Image (mathematics) ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Yuchen Zhu
Source: Research Repository (Delft University of Technology) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Ionic Force Field Parameterization with Bayesian Optimization Force field is widely used to model the potential energy in atomistic simulation systems. Despite force fields have a concise mathematical form, a good set of force field parameters usually requires extra care of calibration. Besides, numerous ionic force field parameters are reported from various sources as researchers have specific target properties for their interests. Previous studies mainly used brute force optimization to find the most desired set of parameters in ionic solution. However, these methods are not efficient since the evaluation of the performance of a parameter set is time-consuming. This work used a stochastic optimization routine in machine learning to tackle the problem of black-box function optimization. This method shows excellent performance of locating the optimum regions of the black-box cost function in only a few iterations. To evaluate the performance of a set of ionic force field parameters, MD simulations are carried out in LAMMPS to compute ionic properties. The solvation free energy and ion oxygen distance are selected as the primary targets while the self-diffusion coefficient and contact ion pairs are regarded as the secondary targets. The optimum region of primary targets are found by direct optimization, then secondary targets are studied with optimized parameters of the primary targets. There have been found discrepancies between the optimum regions of different targeted properties. The dependence studies of individual ionic force field parameters ($\\epsilon, \\sigma, q$) are analyzed and parameterization trends are found out. Base on these trends, the final calibration model is proposed. Force field (fiction) ; Ionic bonding ; Computer science ; Calibration ; Field (mathematics) ; Work (physics) ; Set (abstract data type) ; Statistical physics ; Algorithm ; Mathematical optimization ; Ion ; Chemistry ; Physics ; Mathematics ; Thermodynamics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Electrostatics and Colloid Interactions
Authors: Kaneko Hiromasa
Source: Institutional Repositories DataBase (IRDB) (article, 2020-12-08)
Field: Chemistry — Analytical Chemistry

Support vector regression that takes into consideration the importance of explanatory variables Support vector regression (SVR) is able to consider the nonlinear relationship between explanatory variables X and a target variable y to build a regression model with high predictive accuracy. Additionally, y values predicted with SVR models for new samples can exceed the actual y values in training data. However, because the Gaussian kernel, which is a kernel function generally used in SVR, is based on the Euclidean distance between samples, it is unable to consider the importance of X when building the regression model. Therefore, in this study, the focus was on the importance of X that can be calculated by random forests (RF), and a novel SVR method, called variable importance-considering support vector regression (VI-SVR), was proposed based on this importance. Because X is weighted based on importance, the greater the importance of X, the greater its contribution to the predicted value. Analysis using the spectral, quantitative structure–property relationship (QSPR), and quantitative structure–activity relationship (QSAR) datasets confirmed that the predictive accuracy of VI-SVR was better than that of SVR. VI-SVR Python code is available at https://github.com/hkaneko1985/dcekit. Support vector machine ; Regression analysis ; Regression ; Proper linear model ; Linear regression ; Regression diagnostic ; Variable (mathematics) ; Euclidean distance ; Cross-sectional regression Spectroscopy and Chemometric Analyses ; Machine Learning and ELM ; Hydrological Forecasting Using AI
Authors: Eduardo A. Barros de Moraes | Jorge L. Suzuki | Mohsen Zayernouri
Source: arXiv (Cornell University) (preprint, 2020-12-09)
Field: Materials Science — Materials Chemistry

Atomistic-to-Meso Multi-Scale Data-Driven Graph Surrogate Modeling of\n Dislocation Glide From their birth in the manufacturing process, materials inherently contain\ndefects that affect the mechanical behavior across multiple length and\ntime-scales, including vacancies, dislocations, voids and cracks.\nUnderstanding, modeling, and real-time simulation of the underlying stochastic\nmicro-structure defect evolution is therefore vital towards multi-scale\ncoupling and propagating numerous sources of uncertainty from atomistic to\neventually aging continuum mechanics. We develop a graph-based surrogate model\nof dislocation glide for computation of dislocation mobility. We model an edge\ndislocation as a random walker, jumping between neighboring nodes of a graph\nfollowing a Poisson stochastic process. The network representation functions as\na coarse-graining of a molecular dynamics simulation that provides dislocation\ntrajectories for an empirical computation of jump rates. With this\nconstruction, we recover the original atomistic mobility estimates, with\nremarkable computational speed-up and accuracy. Furthermore, the underlying\nstochastic process provides the statistics of dislocation mobility associated\nto the original molecular dynamics simulation, allowing an efficient\npropagation of material parameters and uncertainties across the scales.\n Statistical physics ; Computation ; Molecular dynamics ; Dislocation ; Jump ; Granularity ; Jump process ; Graph ; Computer science ; Materials science ; Algorithm ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Shirin Faraji | Johannes Ehrmaier | Maximilian F. S. J. Menger
Source: ChemRxiv (preprint, 2020-10-28)
Field: Materials Science — Materials Chemistry

PySurf - A Framework for Database Accelerated Direct Dynamics &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data-science applications in computational chemistry. To illustrate the potential of the framework, a code for nonadiabatic surface-hopping simulations based on the Landau-Zener algorithm is presented here. The results reveal the degree of sophistication, which can be achieved by the database accelerated energy-only surface-hopping simulations being competitive to commonly used semi-classical approaches.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Code (set theory) ; Sophistication ; Computer science ; Computational science ; Database ; Theoretical computer science ; Chemistry ; Programming language Machine Learning in Materials Science ; Scientific Computing and Data Management ; Quantum Computing Algorithms and Architecture
Authors: Jonathan M. Skelton | David S. D. Gunn | Sebastian Metz | Stephen C. Parker
Source: arXiv (Cornell University) (preprint, 2020-05-01)
Field: Materials Science — Materials Chemistry

Accuracy of Hybrid Functionals with Non-Self-Consistent Kohn–Sham\nOrbitals for Predicting the Properties of Semiconductors Accurately modeling the electronic structure of materials is a persistent\nchallenge to high-throughput screening. A promising means of balancing accuracy\nagainst computational cost are non-self-consistent calculations with hybrid\ndensity-functional theory, where the electronic band energies are evaluated\nusing a hybrid functional from orbitals obtained with a less demanding\n(semi-)local functional. We have quantified the performance of this technique\nfor predicting the physical properties of sixteen tetrahedral semiconductors\nwith bandgaps from 0.2-5.5 eV. Provided the base functional predicts a\nnon-metallic electronic structure, bandgaps within 5 % of the PBE0 and HSE06\ngaps can be obtained with an order of magnitude reduction in computing time.\nThe positions of the valence and conduction band extrema and the Fermi level\nare well reproduced, further enabling calculation of the band dispersion,\ndensity of states, and dielectric properties using Fermi's Golden Rule. While\nthe error in the non-self-consistent total energies is ~50 meV atom-1, the\nenergy-volume curves are reproduced accurately enough to obtain the equilibrium\nvolume and bulk modulus with minimal error. We also test the\ndielectric-dependent scPBE0 functional and obtain bandgaps and dielectric\nconstants to within 2.5 % of the self-consistent results, which amount to a\nsignificant improvement over self-consistent PBE0 for a similar computational\ncost. We identify cases where the non-self-consistent approach is expected to\nperform poorly, and demonstrate that partial self-consistency provides a\npractical and efficient workaround. Finally, we perform proof-of-concept\ncalculations on CoO and NiO to demonstrate the applicability of the technique\nto strongly-correlated open-shell transition-metal oxides.\n Hybrid functional ; Density functional theory ; Atomic orbital ; Local-density approximation ; Electronic structure ; Materials science ; Valence (chemistry) ; Statistical physics ; Condensed matter physics ; Physics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides