jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael J. Sahre | Guido Falk von Rudorff | Philipp Marquetand | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Transferability of atomic energies from alchemical decomposition Additional data to the paper "Transferability of atomic energies from alchemical decomposition" https://doi.org/10.48550/arXiv.2311.04784 Atomic energies of selected QM9 compounds calculated with different decomposition schemes (alchemy, IQA and IBO/IAO). Code to generate the input-file and the rescaled pseudopotential files for DFT calculations with fractional core charges with the CPMD program and to calculate alchemical atomic energies from the CPMD output. Decomposition ; Transferability ; Molecule ; Sensitivity (control systems) ; Atomic physics ; Chemistry ; Representation (politics) ; Statistical physics ; Computational chemistry ; Chemical physics ; Physics ; Mathematics ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Zhengwei Cao | Feng Zhang | Jiaqi Liu | Cuihe Fan | Yukun Wu | Yuying Hao | Mingwei Guo | Weixin Wu | Wenjing Wu | Ruizhe Wang | Liming Huang | Qinwei Gao | M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | H Kondoh | J Zhang | C Adamo | M Devries | C Diaz | S George | D Ghosh | K Holmberg | G Meijer | G Patwari | M Rodgers | S Sampath | M Suhm | A Troisi | Jianbo Lin | ,* Ryo Tamura | * Yasunori Futamura | Tetsuya Sakurai | Tsuyoshi Miyazaki | Daniela Torres-Diaz | Lionel Amiaud | Anne Lafosse | Oddur Ingo lfsson | A Saidi | * Mabrouki | J Dhahri | E Dhahri | K Khirouni | B Joshua | S Tse | James Grant | Jonathan Skelton | Lisa Gillie | Runliang Zhu | Giovanni Pesce | Richard Ball | Stephen Parker | * | Marco Molinari | Al Nasser | Luis Martinez-Crespo | Simon Webb | Robert Dryfe ; Zhen | Boxiao Tang | Wei Shao | Peifeng Wu | ; Su | * Silvia Angelova | Valya Nikoleta Kircheva | Stefan Nikolova | Todor Dobrev | Dudev | Ziqi Han | Chun-Sheng Liu | Xiaohong Zheng | Lei Zhang ; Shao | Xin Jiang | Xiaoqing Zeng | ; Yangyi Zhang | Lixiang Wang | Jiaming Chen | Dongke Li | Jingjie ; Dawid Lupa | Piotr Bonarek | Dmitrii Fedorov | Maria Morga | Markus Linder | Jodie Lutkenhaus
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 17705 DOI: 10.1039/D3CP90148C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; Chemistry ; Computer security ; Psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Taohan Lin | Thomas J. Webster
Source: Journal of Emerging Investigators (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Pressure and temperature influence the efficacy of metal-organic frameworks for carbon capture and conversion Carbon dioxide (CO2) is a major contributor to global warming, which has led to irreversible environmental and socio-economic damage. Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO2 to other useful carbon products, such as ethanol, methanol, and cyclic carbonates. However, the large-scale production of MOFs for industrial use is limited because of their high porosity and customizability making their development, synthesis, and use expensive. The purpose of our research was to use computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO2. We used Python programming to examine current data presented in the literature, encompassing various MOFs with a range of pore sizes and different substrates to determine the optimal conditions for MOFs to capture and convert CO2 into useful products. Our results showed that pressure was significantly correlated in convex quadratic models with the selective capture of carbon and temperature was significantly correlated in a concave quadratic model with carbon conversion. Optimal reaction conditions were approximately 80°C (R² = 0.376) and 0-1 atm or 11-12 atm of pressure (R² = 0.210). In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO2 emissions. Greenhouse gas ; Metal-organic framework ; Pyrolysis ; Carbon dioxide ; Methane ; Carbon capture and storage (timeline) ; Carbon fibers ; Methanol ; Environmental science ; Process engineering ; Materials science ; Computer science ; Climate change ; Chemical engineering ; Chemistry ; Composite number ; Organic chemistry ; Adsorption ; Engineering ; Algorithm Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Carbon Dioxide Capture Technologies
Authors: A. G. Nugmanova | A. I. Gorshkova | Alexey V. Yagodin | А. А. Аверин | Maria A. Kalinina
Source: Коллоидный журнал (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Noncovalent Stabilization of Water-Soluble Zinc Phthalocyaninate in Graphene Oxide Hydrosol The possibility of stabilization of zinc(II) 2,3,9,10,16,17,23,24-octa[(3,5-sodium biscarboxylate)phenoxy] phthalocyaninate (ZnPc16) by its hybridization with the surface of graphene oxide (GO) sheets via van der Waals or coordination bonds with functional groups of the carbon matrix in the GO hydrosols has been investigated. A combination of physicochemical analysis methods (scanning electron microscopy, fluorescence microscopy, powder X-ray diffraction, and Raman spectroscopy) has been employed to confirm the integration of ZnPc16 with GO nanosheets and to study the morphology and structure of the obtained hybrid materials. Using electronic absorption spectroscopy, it has been found that, regardless of the hybridization method, the binding of the macrocycles to the inorganic particles increases the stability of ZnPc16 in an aqueous medium being irradiated with visible light. The analysis of spectral kinetic data has shown that, in contrast to the system obtained by direct integration of ZnPc16 and GO, the hybrid material formed by coordination bonding of the components via zinc acetate (Zn(OAc)2) as a binding metal cluster is able to exhibit photocatalytic properties in oxidative photodegradation of some model organic pollutant substrates (rhodamine 6G, 1,5-dihydroxynaphthalene, and 1,4-nitrophenol). The proposed colloid-chemical approach to the stabilization of photoactive water-soluble phthalocyaninates makes it possible to increase their resistance to photoinduced self-oxidation and can be adapted for various derivatives of tetrapyrrole compounds possessing photosensitizing properties. HYDROSOL ; Graphene ; Zinc ; Oxide ; Chemistry ; Materials science ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Engineering Porphyrin and Phthalocyanine Chemistry ; Supramolecular Self-Assembly in Materials ; Conducting polymers and applications
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 11005 DOI: 10.1039/D3CP90096G This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; Physics ; Chemistry ; Computer security ; Psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Eduardo Mayo Yanes | Sabyasachi Chakraborty | Renana Gershoni‐Poranne
Source: ChemRxiv (preprint, 2023-07-26)
Field: Materials Science — Materials Chemistry

The COMPAS Project: A Computational Database ofPolycyclic Aromatic Systems. Phase 2:cata-condensed Hetero-Polycyclic Aromatic Systems Polycyclic aromatic systems are highly important to numerous applications, especially to organic electronics and optoelectronics. High-throughput screening and generative models can help to identify new molecules that can advance these technologies but require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available data set of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date. Our data set contains ~500k molecules comprising 11 types of aromatic and antiaromatic building blocks calculated at the GFN1-xTB level and is representative of a highly diverse chemical space. The methodologies used to enumerate and compute the various structures and their electronic properties (including HOMO-LUMO gap, vertical and adiabatic ionization potential, and electron affinity) are detailed. Additionally, we benchmark the values against a ~50k data set calculated at the CAM-B3LYP-D3BJ/def2-SVP level and develop a fitting scheme to correct the xTB values to higher accuracy. These new data sets represent the second installment in the COMputational database of Polycyclic Aromatic Systems (COMPAS) Project. Aromaticity ; Chemical space ; Benchmark (surveying) ; Molecule ; Polycyclic aromatic hydrocarbon ; Computer science ; Computational chemistry ; Electron affinity (data page) ; Chemistry ; Set (abstract data type) ; Antiaromaticity ; Drug discovery ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Synthesis and Properties of Aromatic Compounds
Authors: Andi Mayasari Usman | A. Dahiru
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-05-17)
Field: Chemistry — Organic Chemistry

Optimized Photocatalytic Degradation of 4- Nitrophenol over N,S-codoped ZnO Photocatalyst In this study, a well defined photoactive N,Scodoped ZnO catalyst was synthesized through by a lowtemperature alkali co-precipitation of zinc acetate dihydrate precursor in presence of ammonium sulfate as dopants’ source. The catalyst was characterized by Xray diffraction (XRD), scanning electronmicroscopy (SEM), energy dispersive x-ray (EDX), UV-visible analysis and FT-IR spectroscopy. The diffraction peaks of undoped and N,S-codoped lines materials are in accordance with the wurtzite hexagonal phase of ZnO. The insertion of the dopants (S and N) and the vigorous agitation during the catalyst preparation caused a decrease in the crystallites size. Visible light photocatalytic studies were carried out using 4- nitrophenol (4-NP) as pollutant. The effect of experimental variables for the photocatalytic degradation such as pH, initial 4-NP concentration, temperature and catalyst loading was optimized based on central composite design (CCD) and response surface methodology. The results indicated that photocatalytic activity of N,S-codoped ZnO (99.3%) was better than undoped ZnO (82%), because the band gap of doped was lower than undoped ZnO photocatalyst which is herein attributed to shift in the band gap caused by the dopants. Photocatalysis ; Degradation (telecommunications) ; 4-Nitrophenol ; Materials science ; Nitrophenol ; Chemical engineering ; Environmental science ; Chemistry ; Catalysis ; Nanotechnology ; Computer science ; Telecommunications ; Engineering ; Nanoparticle ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Pierre Mignon | A. Allouche | Neil Richard Innis | Colin Bousige
Source: PubMed (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Neural network approach for a rapid prediction of metal-supported borophene properties We developed a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of density functional theory (DFT) calculations while achieving computational speedups of several orders of magnitude, allowing the study of extensive structures that may reveal intriguing moiré patterns or surface corrugations. We describe an efficient approach to constructing the training data set using an iterative technique known as the "adaptive learning approach". The developed NNP is able to produce, with excellent agreement, the structure, energy, and forces obtained at the DFT level. Finally, the calculated stability of various borophene polymorphs, including those not initially included in the training data set, shows better stabilization for ν ∼ 0.1 hole density, and in particular for the allotrope α <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>(</mml:mo><mml:mrow><mml:mi>ν</mml:mi><mml:mo>=</mml:mo></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>9</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:math>. The stability of borophene on the metal surface is shown to depend on its orientation, implying structural corrugation patterns that can be observed only from long-time simulations on extended systems. The NNP also demonstrates its ability to simulate vibrational densities of states and produce realistic structures with simulated STM images closely matching the experimental ones. Borophene ; Density functional theory ; Materials science ; Stability (learning theory) ; Artificial neural network ; Set (abstract data type) ; Structural stability ; Computer science ; Chemical physics ; Matching (statistics) ; Molecular dynamics ; Surface (topology) ; Biological system ; Statistical physics ; Computational chemistry ; Artificial intelligence ; Physics ; Chemistry ; Machine learning ; Mathematics ; Geometry ; Structural engineering Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Electron and X-Ray Spectroscopy Techniques
Authors: Zisheng Zhang | Ive Hermans | Anastassia N. Alexandrova
Source: ChemRxiv (preprint, 2023-05-04)
Field: Materials Science — Materials Chemistry

Off-stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane Boron-containing materials such as hexagonal boron nitride, recently shown to be active and selective catalysts for the oxidative dehydrogenation of propane (ODHP), have been shown to undergo significant surface oxyfunctionalization and restructuring. Although experimental ex situ studies have probed the change in chemical environment on the surface, the structural evolution of it under varying reaction conditions has not been established. Herein we perform global optimization structural search with grand canonical genetic algorithm to explore the chemical space of off-stoichiometric restructuring of hexagonal boron nitride (hBN) surface under ambient as well as ODHP relevant reaction conditions. A grand canonical ensemble representation of the surface is established, and the predicted 11B solid-state NMR spectra are consistent with previous experimental reports. In addition, we investigated the relative sliding of hBN sheets and how it affects the surface chemistry with ab initio molecular dynamics simulations. The B-O linkages on the edges are found to be significantly strained during the sliding, causing the metastable sliding configurations to have higher reactivity towards activation of propane and water. Dehydrogenation ; Stoichiometry ; Propane ; Hexagonal boron nitride ; Materials science ; Boron ; Ab initio ; Boron nitride ; Metastability ; Computational chemistry ; Chemical physics ; Chemical engineering ; Catalysis ; Physical chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Graphene Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Cian Gabbett | Luke Doolan | Kevin Synnatschke | Laura Gambini | Emmet Coleman | Adam G. Kelly | Shixin Liu | Eoin Caffrey | José Luis Munuera | Catríona Murphy | Stefano Sanvito | Lewys Jones | Jonathan N. Coleman
Source: arXiv (Cornell University) (preprint, 2023-01-26)
Field: Materials Science — Materials Chemistry

3D-imaging of Printed Nanostructured Networks using High-resolution FIB-SEM Nanotomography Networks of solution-processed nanomaterials are important for multiple applications in electronics, sensing and energy storage/generation. While it is known that network morphology plays a dominant role in determining the physical properties of printed networks, it remains difficult to quantify network structure. Here, we utilise FIB-SEM nanotomography to characterise the morphology of nanostructured networks. Nanometer-resolution 3D-images were obtained from printed networks of graphene nanosheets of various sizes, as well as networks of WS2 nanosheets, silver nanosheets and silver nanowires. Important morphological characteristics, including network porosity, tortuosity, pore dimensions and nanosheet orientation were extracted and linked to network resistivity. By extending this technique to interrogate the structure and interfaces within vertical printed heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation. Materials science ; Tortuosity ; Nanotechnology ; Nanosheet ; Nanomaterials ; Porosity ; Nanometre ; Nanowire ; Nanostructure ; 3d printed ; Graphene ; Composite material ; Biomedical engineering Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Molecular Junctions and Nanostructures
Authors: Luis Barroso-Luque | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-01-05)
Field: Materials Science — Materials Chemistry

The cluster decomposition of the configurational energy of multicomponent alloys Lattice models parameterized using first-principles calculations constitute an effective framework to simulate the thermodynamic behavior of physical systems. The cluster expansion method is a flexible lattice-based method used extensively in the study of multicomponent alloys. Yet despite its prevalent use, a well-defined understanding of expansion terms has remained elusive. In this letter, we introduce the cluster decomposition as a unique and basis-agnostic decomposition of any general function of the atomic configuration in a crystal. We demonstrate that cluster expansions constructed from arbitrary orthonormal basis sets are all representations of the same cluster decomposition. We show how the norms of expansion coefficients associated with the same crystallographic orbit are invariant to changes between orthonormal bases. Based on its uniqueness and orthogonality properties, we identify the cluster decomposition as an invariant ANOVA decomposition. We leverage these results to illustrate how functional analysis of variance and sensitivity analysis can be used to directly interpret interactions among species and gain insight into computed thermodynamic properties. The work we present in this letter opens new directions for parameter estimation, interpretation, and use of applied lattice models on well-established mathematical and statistical grounds. Orthonormal basis ; Cluster expansion ; Parameterized complexity ; Lattice (music) ; Leverage (statistics) ; Cluster (spacecraft) ; Statistical physics ; Mathematics ; Applied mathematics ; Computer science ; Physics ; Thermodynamics ; Algorithm ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies
Authors: Jaehyun Lee | Corina Urdaniz | Stefano Reale | Kyungju Noh | Denis S. Krylov | Andrin Doll | Luciano Colazzo | Yujeong Bae | Christoph Wolf | Fabio Donati
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Interpreting X-ray absorption spectra of Vanadyl Phthalocyanines Spin Qubit Candidates using a Machine Learning-Assisted Approach The magnetic dilution of Vanadyl phthalocyanine (VOPc) within the isostructural diamagnetic Titanyl phthalocyanine (TiOPc) affords promising molecular spin qubit platforms for solid-state quantum computing. The development of quantitative methods for determining how the interactions with a supporting substrate impact the electronic structure of the system are fundamental to determine their potential integration in physical devices. In this work we propose a combined approach based on X-ray absorption spectroscopy (XAS), atomic multiplet calculations, and density functional theory (DFT) to investigate the 3d orbital level structure of VOPc on TiOPc/Ag(100). We characterize VOPc in different molecular environments realized by changing the thickness of TiOPc interlayer and adsorption configuration on Ag(100). Depending on the molecular film structure, we find characteristic XAS features that we analyze using atomic multiplet calculations. We use a Bayesian optimization algorithm to accelerate the parameter search process in the multiplet calculations and identify the ground state properties, such as the 3d orbital occupancy and splitting, as well as intra-atomic interactions. Our analysis indicates that VOPc retains its spin S = 1/2 character in all configurations. Conversely, the energy separation and sequence of the unoccupied V 3d orbitals sensitively depend on the interaction with the surface and TiOPc interlayer. We validate the atomic orbital picture obtained from the multiplet model by comparison with DFT, which further allows us to understand the VOPc electronic properties using a molecular orbital description. Multiplet ; Chemistry ; Phthalocyanine ; Molecular orbital ; Electronic structure ; Density functional theory ; Ground state ; Absorption spectroscopy ; Chemical physics ; Molecular physics ; Computational chemistry ; Spectral line ; Atomic physics ; Molecule ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis