jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siarhei Zhuk | Alexander Wieczorek | Amit Sharma | Jyotish Patidar | Kerstin Thorwarth | Johann Michler | Sebastian Siol
Source: arXiv (Cornell University) (preprint, 2023-05-31)
Field: Materials Science — Materials Chemistry

Combinatorial Reactive Sputtering with Auger Parameter Analysis Enables Synthesis of Wurtzite Zn2TaN3 The discovery of new functional materials is one of the key challenges in materials science. Combinatorial high-throughput approaches using reactive sputtering are commonly employed to screen unexplored phase spaces. During reactive combinatorial deposition the process conditions are rarely optimized, which can lead to poor crystallinity of the thin films. In addition, sputtering at shallow deposition angles can lead to off-axis preferential orientation of the grains. This can make the results from a conventional structural phase screening ambiguous. Here we perform a combinatorial screening of the Zn-Ta-N phase space with the aim to synthesize the novel semiconductor Zn2TaN3. While the results of the XRD phase screening are inconclusive, including chemical state analysis mapping in our workflow allows us to see a very clear discontinuity in the evolution of the Ta binding environment. This is indicative of the formation of a new ternary phase. In additional experiments, we isolate the material and perform a detailed characterization confirming the formation of single phase WZ-Zn2TaN3. Besides the formation of the new ternary nitride, we map the functional properties of ZnxTa1-xN and report previously unreported clean chemical state analysis for Zn3N2, TaN and Zn2TaN3. Overall, the results of this study showcase common challenges in high-throughput materials screening and highlight the merit of employing characterization techniques sensitive towards changes in the materials' short-range order and chemical state. Sputtering ; Characterization (materials science) ; Materials science ; Combinatorial synthesis ; Ternary operation ; Wurtzite crystal structure ; Crystallinity ; Deposition (geology) ; Nanotechnology ; Computer science ; Chemistry ; Thin film ; Combinatorial chemistry ; Zinc ; Metallurgy Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Inorganic Chemistry and Materials
Authors: Shovon Talukder
Source: Journal of Sensors (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Computational and Experimental Analysis of a Triode Microfuse with a WO<sub>3</sub> Heater In this study, high‐current‐protecting multilayered thin film microfuses are designed and simulated using the MEMS‐based tool of COMSOL multiphysics software and then fabricated and tested in the laboratory. Portable electronic devices are comprised of a secondary battery or DC charge source, and due to short circuit overcurrent, fire, and explosions can ensue. A protecting device should steadily cater to phenomena like overcurrent situations to avoid hazardous circumstances. The primary purpose of this investigation is to design a heater resistor with a negative temperature coefficient (NTC) to function as a low melting point‐based alloy for the fuse element. A lead‐tin (90Pb:10Sn wt.%) alloy has been employed as the low melting point‐based fuse element, and tungsten oxide (WO 3 ) is integrated with the layer as a heater resistor due to its negative temperature coefficient of resistance characteristics. The electro‐thermo‐mechanical behavior is assessed, and a three‐dimensional structural modeling and simulation technique has been performed in both steady‐state and transient conditions with varying physical and electrical parameters. The heat required to melt the fuse depends on heater geometry, and when we applied 2 A current to the 1 : 30 length and width ratio‐based device, the heater achieved 600 K. Experimentally, nearly at 1 A current and above 4 V, the microfuse reached melting temperature and thus has been blown which provides a scope of controlling nearly 4 W of power electronic devices. Overcurrent ; Materials science ; Resistor ; Multiphysics ; Fuse (electrical) ; Current (fluid) ; Electrical engineering ; Nuclear engineering ; Optoelectronics ; Composite material ; Voltage ; Finite element method ; Engineering ; Structural engineering Transition Metal Oxide Nanomaterials ; Advanced Battery Technologies Research ; Gas Sensing Nanomaterials and Sensors
Authors: Abdalla Alghfeli | Timothy S. Fisher
Source: Research Square (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Sequential Bayesian-optimized graphene synthesis by direct solar-thermal chemical vapor deposition Abstract This work reports the use of a high-flux solar simulator that mimics the solar spectrum and a cold-wall CVD reactor to demonstrate the feasibility of utilizing a renewable energy resource in synthesizing graphene under various conditions. A parametric study of process parameters was carried out using a probabilistic Bayesian regression model and an information acquisition function to find conditions that yield high-quality product. Backscattered electron images and Raman mapping were used to assess the effects of growth conditions on graphene characteristic sizes, film quality, and uniformity. We report the synthesis of high-quality single-layer graphene (SLG) and AB-stacked bilayer graphene films in a one-step, short-time process with \(I_{D}/I_{G}\) ratios of 0.21 and 0.14, respectively. Electron diffraction analysis shows peak intensities that resemble SLG and AB-bilayer graphene with up to 5 and 20 \(\mathrm{\mu}\) m grain sizes, respectively. The optical transmissivities of SLG and AB-bilayer graphene fall between 0.959-0.977 and 0.929-0.953, whereas the sheet resistances measured by a 4-point probe with 1 mm spacing are 15.5 \(\pm\) 4.6 and 3.4$\pm$1.5 k \(\Omega\) /sq, respectively. Further scale-up of the optimized graphene growth area was achieved by flattening the insolation profile, leading to spatial uniformity up to 15 mm in radius. Direct solar capture for CVD synthesis enable a practical and sustainable option for synthesizing graphene films applicable for photonic and electronic applications. Graphene ; Materials science ; Bilayer graphene ; Chemical vapor deposition ; Raman spectroscopy ; Optoelectronics ; Nanotechnology ; Optics ; Physics Graphene research and applications ; Radiation Effects in Electronics ; Thermal properties of materials
Authors: Sharon Pinus | Jérôme Genzling | Mihai Burai Patrascu | Nicolas Moitessier
Source: ChemRxiv (preprint, 2023-12-13)
Field: Materials Science — Materials Chemistry

Catalyzing Change: The Power of Computational Asymmetric Catalysis Computational asymmetric catalysis has seen an impressive rise in the last twenty years, thanks to advancements in algorithm and method development for predicting catalyst enantioselectivity. These methods/algorithms describe reactions that can be categorized into two groups: reactions where 1) knowledge of the mechanism is not required and where leveraging experimental data to establish correlations between reaction descriptors and enantioselectivity is imperative, and 2) the mechanism (or transition state (TS) for the enantioselective step) is known and used to determine catalyst stereoselectivity by modeling the diastereomeric TSs. Although these methods have reached an important level of proficiency for enantioselectivity prediction, this field remains largely obscured for experimental chemists. In this review, we aim to shed light on models, methods, and applications used in asymmetric synthesis, with accessible language suited for experimental chemists. Our hope is that these methods will ultimately be adopted by synthetic chemists for the design of novel catalysts. Enantioselective synthesis ; Diastereomer ; Mechanism (biology) ; Computer science ; Catalysis ; Stereoselectivity ; Expressive power ; Field (mathematics) ; Biochemical engineering ; Chemistry ; Organocatalysis ; Combinatorial chemistry ; Nanotechnology ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Organic chemistry ; Materials science ; Engineering ; Physics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Samuel P. Gleason | Deyu Lu | Jim Ciston
Source: arXiv (Cornell University) (preprint, 2023-09-08)
Field: Materials Science — Materials Chemistry

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.85 and a root mean square valence error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials. X-ray absorption spectroscopy ; Valence (chemistry) ; Spectral line ; Absorption spectroscopy ; Spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Physics ; Optics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications
Authors: Szabolcs Góger | Leonardo Medrano Sandonas | Carolin Müller | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

Data-Driven Tailoring of Molecular Dipole Polarizability and Frontier Orbital Energies in Chemical Compound Space Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and the dipole polarizability. Based on the recently introduced QM7-X dataset, augmented with accurate molecular polarizability calculations as well as analysis of functional group compositions, we show that polarizability and HOMO-LUMO gap are uncorrelated when considering sufficiently extended subsets of the chemical compound space. The relation between these two properties is further analyzed on specific examples of molecules with similar composition as well as homooligomers. Remarkably, the freedom brought by the lack of correlation between molecular polarizability and HOMO-LUMO gap enables the design of novel materials, as we demonstrate on the example of organic photodetector candidates. Polarizability ; Dipole ; HOMO/LUMO ; Molecular orbital ; Chemical physics ; Molecule ; Atomic orbital ; Chemistry ; Band gap ; Computational chemistry ; Physics ; Quantum mechanics ; Electron Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Astrid Klipfel | Yaël Fregier | Adlane Sayede | Zied Bouraoui
Source: arXiv (Cornell University) (preprint, 2023-12-20)
Field: Materials Science — Materials Chemistry

Vector Field Oriented Diffusion Model for Crystal Material Generation Discovering crystal structures with specific chemical properties has become an increasingly important focus in material science. However, current models are limited in their ability to generate new crystal lattices, as they only consider atomic positions or chemical composition. To address this issue, we propose a probabilistic diffusion model that utilizes a geometrically equivariant GNN to consider atomic positions and crystal lattices jointly. To evaluate the effectiveness of our model, we introduce a new generation metric inspired by Frechet Inception Distance, but based on GNN energy prediction rather than InceptionV3 used in computer vision. In addition to commonly used metrics like validity, which assesses the plausibility of a structure, this new metric offers a more comprehensive evaluation of our model's capabilities. Our experiments on existing benchmarks show the significance of our diffusion model. We also show that our method can effectively learn meaningful representations. Metric (unit) ; Computer science ; Probabilistic logic ; Diffusion ; Focus (optics) ; Crystal (programming language) ; Field (mathematics) ; Algorithm ; Equivariant map ; Theoretical computer science ; Statistical physics ; Data mining ; Artificial intelligence ; Mathematics ; Physics ; Pure mathematics ; Thermodynamics ; Engineering ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Text and Document Classification Technologies
Authors: Levan Chkhartishvili
Source: Characterization and Application of Nanomaterials (article, 2023-02-17)
Field: Materials Science — Materials Chemistry

Construction of semiclassical interatomic B–B pair potential to characterize all-boron nanomaterials The semiclassical boron–boron interatomic pair potential is constructed in an integral form allowing its converting into the analytical one. It is an ab initio B–B potential free of any semiempirical adjusting parameters, which would serve as an effective tool for the theoretical characterization of all-boron and boron-rich nanomaterials. Semiclassical physics ; Boron ; Nanomaterials ; Interatomic potential ; Characterization (materials science) ; Ab initio ; Ab initio quantum chemistry methods ; Materials science ; Chemical physics ; Computational chemistry ; Nanotechnology ; Molecular physics ; Chemistry ; Atomic physics ; Physics ; Molecular dynamics ; Quantum mechanics ; Molecule ; Organic chemistry Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Emina Yamada | Hirotoshi Sakamoto | H. Matsui | Tomoya Uruga | Kunihisa Sugimoto | Minh-Quyet Ha | Hieu‐Chi Dam | Ryotaro Matsuda | Mizuki Tada
Source: ChemRxiv (preprint, 2023-12-28)
Field: Chemistry — Inorganic Chemistry

Three-Dimensional Visualization of Adsorption Distribution in a Single Crystalline Particle of a Metal-Organic Framework Many unique adsorption properties of metal-organic frameworks (MOFs) have been revealed by diffraction crystallography, visualizing their vacant/guest-loaded crystal structures at the molecular scale. However, it has been challenging to see the spatial distribution of the adsorption behaviors throughout a single MOF particle in a transient equilibrium state. Here, we report three-dimensional (3D) visualization of molecular adsorption behaviors in a single crystalline particle of a MOF by operando X-ray absorption fine structure spectroscopy combined with computed tomography for the first time. The 3D maps of water-coordinated Co sites in a 100-µm-scale MOF-74-Co crystal were obtained with 1 µm spatial resolution under sever-al water vapor pressures. Through the visualization of the water vapor adsorption process, the 3D spectroimaging revealed the mechanism and spatial heterogeneity of guest adsorption inside a single particle of a crystalline MOF. Adsorption ; Particle (ecology) ; Metal-organic framework ; Materials science ; Visualization ; Chemical physics ; Single crystal ; Water vapor ; Crystallography ; Absorption (acoustics) ; Chemical engineering ; Nanotechnology ; Chemistry ; Physical chemistry ; Organic chemistry ; Computer science ; Geology Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Rui Wang | Brandon C. Bukowski | Jiaxin Duan | Kun Zhang | Randall Q. Snurr | Joseph T. Hupp
Source: ChemRxiv (preprint, 2023-06-21)
Field: Chemistry — Inorganic Chemistry

Geometry and Chemistry: Influence of Pore Functionalization on Molecular Transport and Diffusion in Solvent-Filled Zirconium Metal–Organic Frameworks Post-synthetic modification (PSM) of metal–organic frameworks (MOFs) enables incorporation of diverse func-tionalities in pores for chemical separations, drug delivery, and heterogeneous catalysis. However, the effect of PSM on molecular transport, which is essential for most applications of MOFs, is rarely studied. In this paper, we used the perfluoroalkane-functionalized Zr-MOF NU-1008 as a platform to systematically interrogate transport processes and mechanisms in solvated pores. We anchored perfluoroalkanes onto NU-1008 nodes by solvent-assisted ligand incorporation (SALI-n, with n = 3, 5, 7, 9 denoting the number of fluorinated carbons). Transport of a luminescent molecule, BODIPY, through individual crystallites of four versions of methanol-filled SALI-n was monitored by confocal fluorescence microscopy as a function of time and location. In com-parison with the parent NU-1008, the diffusivity of the probe molecules within SALI-n declined by 2 to 7-fold depending on chain length and loading, presumably due to steric effects or adsorptive interactions with per-fluoroalkyl chains. Atomistic simulations were performed to uncover the microscopic behavior of BODIPY dif-fusion in SALI-n. BODIPY molecules, which preferably interact with linkers, were pushed to the interior of the channels as chain length increased, resulting in solvated diffusion and minor differences in the short-time mobility of BODIPY in SALI-n. This suggested that the observed decline of transport diffusivity in SALI-n mainly stemmed from the steric effect from these flexible chains. We anticipate that this proof-of-concept will assist in understanding how pore functionalization can physically and chemically affect mass transport in MOFs and will be useful in further guiding the design of PSM to realize the optimal performance of MOFs for various applications. BODIPY ; Steric effects ; Chemistry ; Surface modification ; Molecule ; Metal-organic framework ; Diffusion ; Ligand (biochemistry) ; Thermal diffusivity ; Chemical engineering ; Fluorescence ; Physical chemistry ; Organic chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Supramolecular Chemistry and Complexes
Authors: Nicolas Castel | Dune André | Connor Edwards | Jack D. Evans | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-10-19)
Field: Materials Science — Materials Chemistry

Machine-Learnt Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks The detailed understanding of the microscopic structure of amorphous phases of metal-organic frameworks (MOFs) remains a widely open question: characterization of these systems is very difficult, both from the experimental and computational point of view. In molecular simulations, approaches have been proposed that rely either on reactive force field, that lack chemical accuracy, or first-principles calculations, that are too computationally expensive. Here, we have found an innovative solution to these problems by training a machine-learnt potential for the description of disordered phases of a zeolitic imidazolate framework (ZIF). We then used it to produce high-quality atomistic models of ZIF glasses, with accuracy close to density functional theory (DFT) but at far lower computational cost. Zeolitic imidazolate framework ; Amorphous solid ; Imidazolate ; Force field (fiction) ; Molecular dynamics ; Density functional theory ; Computer science ; Materials science ; Characterization (materials science) ; Field (mathematics) ; Metal-organic framework ; Nanotechnology ; Computational chemistry ; Chemistry ; Artificial intelligence ; Physical chemistry ; Mathematics ; Crystallography ; Inorganic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Maureen Kitheka | Yan Jing | Yan Yao | Puja Goyal
Source: ChemRxiv (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

EFFICIENT ORGANIC MOLECULAR CRYSTAL STRUCTURE PREDICTION USING THE DENSITY FUNCTIONAL TIGHT-BINDING METHOD Over the years, crystal structure prediction (CSP) has thrived as an area of research, spanning various scientific disciplines, and having significant applications in industries such as pharmaceuticals and agrochemicals. Within the field of batteries, redox-active organic materials (ROMs) such as quinones have received increased attention as promising electrode materials for rechargeable batteries. However, experimental determination of the crystal structure of intermediate species formed during the discharge/charge cycle can often be challenging. Incomplete X-ray diffraction patterns can also lead to difficulties in crystal structure determination for ROMs used in batteries. Use of a semiempirical electronic structure method for CSP helps to avoid force field reparameterization for different species, sometimes with complex electronic structure, formed during battery operation. It also helps to significantly lower the computational cost compared to the widely used density functional theory (DFT). The goal of this study is to systematically investigate the ability of a DFT-based semiempirical method, third-order density functional tight-binding (DFTB3), to predict the most stable crystal structures of organic molecules with different kinds of intermolecular interactions ranging from hydrogen bonding to -stacking. Here, DFTB3 in conjunction with the particle swarm optimization (PSO) algorithm, as implemented in the CALYPSO software, is also used to predict the most stable crystal structures of selected quinones relevant to organic batteries. Our findings emphasize the potential of CALYPSO/DFTB as a promising approach for CSP of different classes of organic molecules, including quinones. Additionally, they establish the foundation for future CSP studies of other organic molecules utilized in rechargeable batteries. Crystal structure prediction ; Density functional theory ; Stacking ; Crystal (programming language) ; Molecule ; Crystal structure ; Crystal engineering ; Materials science ; Electronic structure ; Intermolecular force ; Force field (fiction) ; Chemical physics ; Computer science ; Computational chemistry ; Chemistry ; Hydrogen bond ; Crystallography ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: P. A. Sechenykh
Source:  (article, 2023-04-20)
Field: Materials Science — Materials Chemistry

MODELING THE CRYSTAL STRUCTURE OF SPINELS USING A RELATIONAL DATA MODEL The work presents the calculation of the metric parameters of crystalline compounds with a given chemical formula and space symmetry group. During the simulation, the developed relational model for storing input and output data was used. Compounds realized in the “spinel” structural type are considered Metric (unit) ; Relational model ; Spinel ; Data modeling ; Computer science ; Crystal structure ; Work (physics) ; Relational database ; Space (punctuation) ; Symmetry (geometry) ; Group (periodic table) ; Theoretical computer science ; Algorithm ; Data mining ; Materials science ; Crystallography ; Chemistry ; Mathematics ; Thermodynamics ; Physics ; Database ; Engineering ; Geometry ; Organic chemistry ; Metallurgy Machine Learning in Materials Science ; Pigment Synthesis and Properties ; Geochemistry and Geologic Mapping