jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Dana | Mu Li | S. GELIN | Susan B. Sinnott | Ismaïla Dabo
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Cluster expansion by transfer learning for phase stability predictions Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. This algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations. Computer science ; Cluster (spacecraft) ; Convergence (economics) ; Cluster expansion ; Exploit ; Inference ; Implementation ; Dimension (graph theory) ; Bayesian probability ; Bayesian inference ; Dimensionality reduction ; Reduction (mathematics) ; Construct (python library) ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Maryam Javadi | Hurieh Mohammadzadeh | Abbas Aghaeinejad‐Meybodi
Source: Research Square (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Structural characterization, lattice features, and optical, and magnetic properties of Ni-Cr oxide nanocomposite Abstract In the present study, the nanocomposite of Ni-Cr oxide was synthesized through the chemical co-precipitation route with different ratios of NiO to Cr 2 O 3 , including 50–50, 60 − 40, and 70 − 30 wt. %. The morphological and physical properties of products were assessed thoroughly. Moreover, the possible chemical reactions through synthesis were proposed based on FTIR and XRD results. The crystallite size, the lattice strain, and stress were evaluated through some well-known methods based on XRD diffraction. The particle size and morphology of produced powders were studied by FESEM imaging. Also, the optical and magnetic features were assessed by UV-Vis spectroscopy and VSM analysis, respectively. The results proved the presence of NiO and NiCr 2 O 4 in the calcined products, with uniform distribution of elements and an average particle size of &lt; 30 nm. Moreover, the crystallite size, and the lattice stress and strain decreased with increasing NiO in the product. According to the VSM curves, nanocomposite powders showed a weak ferro/ferrimagnetic signal with a fairly dominant superparamagnetic feature. While the largest coercivity of 252 Oe belonged to the sample 70 − 30, the highest magnetization saturation (0.291 emu/g) was observed for 50–50. According to the UV-Vis curves, the value of band gap energy was in the semiconductor’s domain and increased by NiO value in the hybrid oxide combination. Crystallite ; Materials science ; Coercivity ; Ferrimagnetism ; Non-blocking I/O ; Nanocomposite ; Superparamagnetism ; Analytical Chemistry (journal) ; Calcination ; Particle size ; Oxide ; Lattice constant ; Band gap ; Coprecipitation ; Chemical engineering ; Diffraction ; Magnetization ; Nanotechnology ; Metallurgy ; Condensed matter physics ; Optics ; Chemistry ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Transition Metal Oxide Nanomaterials ; Pigment Synthesis and Properties
Authors: Alireza Kokabi | Zahra Nasirimahd | Zohreh Naghibi
Source: Research Square (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Cross-Validation and Cosine Similarity-based Deep Correlation Analysisof Nonlinear Properties in Transition Metal Clusters Abstract A new approach for the rapid and accurate correlation study of the nonlinear properties in the Transition Metal (TM) clusters based on the Deep Leave-One-Out Cross-Validation (LOO-CV) method is presented. This study shows that the DNN-based approach proposes a more efficient method for predicting several properties of the fourth-row TM nanoclusters in comparison with the conventional methods based on Density Functional Theory (DFT), which are computationally expensive and significantly time-consuming. The feature space or equivalently called descriptors are defined based on a wide range of electronic and physical properties. Considering the similarities between these clusters, the DNN-based model is employed to investigate the correlation between the TM cluster properties. The method together with the cosine similarity delivers significant accuracy in the order of at most 10 − 9 for the prediction of total energy, lowest vibrational mode, binding energy and HOMO-LUMO energy gap of TM 2 , TM 3 , and TM 4 nanoclusters. Based on the correlation errors, the most coupling TM clusters are obtained. In this regard, Mn and Ni clusters has the maximum and minimum amount of energy couplings with other transition metals, respectively. In general, energy prediction errors of TM 2 , TM 3 , and TM 4 demonstrate comparable patterns while an even-odd behavior is observed for vibrational modes and binding energies. In addition, Ti, V and Co demonstrate maximum binding energy coupling to the TM 2 , TM 3 and TM 4 sets, respectively. For the case of the energy gap, Ni shows the maximum correlation in the smallest TM 2 clusters while Cr dependence is highest for TM 3 and TM 4 sets. Finally, Zn has the highest error for HOMO-LUMO energy gap in all sets and consequently the maximum independent energy gap characteristics. Nanoclusters ; Cluster (spacecraft) ; Energy (signal processing) ; Similarity (geometry) ; Nonlinear system ; Binding energy ; Materials science ; Correlation ; Range (aeronautics) ; Coupling (piping) ; Molecular physics ; Statistical physics ; Physics ; Mathematics ; Atomic physics ; Computer science ; Nanotechnology ; Artificial intelligence ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chalcogenide Semiconductor Thin Films
Authors: Matthew A. Sanders | Supraja S. Chittari | Nicole Sherman | Jack R. Foley | Abigail Knight
Source: ChemRxiv (preprint, 2023-02-22)
Field: Chemistry — Organic Chemistry

Versatile triphenylphosphine-containing polymeric catalysts and elucidation of structure-function relationships Synthetic polymers are a modular solution to bridging the two most common classes of catalysts: proteins and small molecules. Polymers offer the synthetic versatility of small molecule catalysts, while simultaneously having the ability to construct microenvironments mimicking those of natural proteins. We synthesized a panel of polymeric catalysts containing a novel triphenylphosphine acrylamide monomer (TPPAm) and investigated how their properties impact the rate of a model Suzuki-Miyaura cross-coupling reaction. Systematic variation of polymer properties, such as molecular weight, functional density, and comonomer identity, led to tunable reaction rates and solvent compatibility, including full conversion in an aqueous medium. Studies with bulky substrates revealed connections between polymer parameters and reaction conditions that were further elucidated with a regression analysis. Some connections were substrate-specific, highlighting the value of the rapidly tunable polymer catalyst. Collectively, these results aid in building structure-function relationships to guide the development of polymer catalysts with tunable substrate and environment compatibility. Polymer ; Comonomer ; Catalysis ; Monomer ; Materials science ; Triphenylphosphine ; Molecule ; Coupling reaction ; Combinatorial chemistry ; Chemistry ; Polymer chemistry ; Chemical engineering ; Organic chemistry Synthetic Organic Chemistry Methods ; Cyclopropane Reaction Mechanisms ; Catalytic Cross-Coupling Reactions
Authors: Fran Adar
Source: LCGC North America (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Measuring the Crystallinity of PHBHx with Varying Amounts of Sidechains on a Benchtop Instrument Current benchtop instruments with edge filters providing Raman spectra down to 50 cm-1 can be used to study the crystallization of PHBHx (polyhydroxybutyrate-hexanoate) without the complexity of larger instruments. By collecting the averaged signal from spherulites, the effects of orientation can be averaged out, enabling the use of multivariate techniques to compare samples. These polymers are being commercialized because they are created by fermentation and are biodegradable, making them an ideal replacement for petroleum-derived polymers that do not biodegrade. In addition, it is understood how to control the polymer’s physical properties by controlling the molecular weight and the percent of sidechains which, in turn, controls the crystallinity. To compare spectra of different samples, it is necessary to remove the effects of polymer chain orientation, which averaging the spectrum of a spherulite will enable. Crystallinity ; Polymer ; Materials science ; Raman spectroscopy ; Spherulite (polymer physics) ; Crystallization ; Polyhydroxybutyrate ; Crystallization of polymers ; Chemical engineering ; Composite material ; Optics ; Engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Avan Kareem | Ibtisam Kamal
Source: Eurasian Journal of Science and Engineering (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Optimization and Modeling the Processes of Green Magnetite Nanoparticle: Synthesis, Photo Catalysis Tendency and Kinetics of Removing Organic Dye from Aqueous Solutions Green magnetite nanoparticles (NPs) are synthesized, characterized and employed for degradation Methylene Blue (MB) from aqueous solutions. The effect of the concentrations of the NPs and MB on NPs yield and removal efficiency is optimized and modeled using two factorial central composite experimental design. The analysis of variance confirmed that the concentration of iron metal salts seemed more significant than plant extract. The developed mathematical model is estimated with high R2 reflecting its accuracy. The results proved that the removal efficiency of MB increases up to an optimum of 82.07 % when using 0.17 g of the nano photo catalyst versus 10.8 ppm of MB with sunlight irradiation time of 200 min. The dye degradation kinetic results revealed that photo catalytic degradation follow pseudo-first-order model. Response Surface Methodology proved as an efficient tool for optimization and modeling the processes of NPs production and removing of organic pollutants from aqueous solutions. Aqueous solution ; Kinetics ; Magnetite ; Magnetite Nanoparticles ; Nanoparticle ; Catalysis ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Magnetic nanoparticles ; Organic chemistry ; Physics ; Engineering Nanomaterials for catalytic reactions ; Pigment Synthesis and Properties
Authors: Xiuhan Li | Ju Rong | Jiaojiao Bu | Yudong Sui | Yannan Zhang | Yan Wei
Source: Research Square (preprint, 2023-10-03)
Field: Materials Science — Materials Chemistry

Electrochemical model of anodic dissolution for magnesium nanoparticles Abstract The frontiers of material corrosion research are transitioning from macroscopic corrosion to the micro or even nanoscale. However, nanomaterials contain a large number of simulated atoms, which brings great difficulties to the research of corrosion mechanisms and material development. Therefore, under the framework of lattice dynamics, combined with nano-thermodynamic theory, the electrochemical Butler-Volmer (BV) equation is developed to simplify the investigation of the anodic dissolution behavior of nanomaterials. The results show that the difference between the lattice parameters optimized using the General Utility Lattice Program (GULP) and the Cambridge Sequential Total Energy Package (CASTEP) results is 0.01 Å, and the variation in cohesion and surface energies is only 0.001 eV and 0.005 eV/Å 2 . Meanwhile, the anodic dissolution rates of Magnesium (Mg) at the (0001), (10一10), and (11一20) crystal planes calculated from the BV model based on lattice dynamics are in agreement with the results derived from first principles. During the active dissolution zone, the corrosion potential increases from − 7.53 V to -5.44 V, and the corrosion current density decreases from 47.48 A/cm 2 to 29.97 A/cm 2 as the Mg nanoparticles size increases from 1 nm to 6 nm. The improved model quantifies the relationship between surface properties and corrosion behavior through the size effect of nanoparticles, which enriches the way of studying electrochemical properties at the nanoscale. Dissolution ; Nanomaterials ; Corrosion ; Materials science ; Electrochemistry ; Magnesium ; Nanoparticle ; Anode ; CASTEP ; Lattice (music) ; Nanoscopic scale ; Chemical engineering ; Metallurgy ; Nanotechnology ; Physical chemistry ; Chemistry ; Band gap ; Electrode ; Physics Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties
Authors: Edgar Clyde R. Lopez
Source:  (article, 2023-05-05)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks as Novel Photocatalysts: Opportunities for Catalyst Design Metal-organic frameworks (MOFs) are an evolving class of crystalline porous materials made of organic linkers and metallic nodes. The rich chemistry of MOFs allows them to have an almost infinite number of possible structures. Consequently, they have been of great interest because of their highly tunable properties and unique features, such as their high porosity, high surface area, structural stability, structural diversity, and tailorability. These enable MOFs to be a flexible catalytic platform for photocatalytic applications. Thus, this paper discusses the opportunities of MOFs for use in catalysis. In particular, the use of metal-organic frameworks as a photocatalyst is briefly discussed. Specifically, MOFs can be used as a photocatalyst for carbon dioxide reduction (CO2RR), nitrogen reduction reactions (NRRs), and water-splitting reactions (HERs and ORRs). However, using MOFs as catalytic platforms has some challenges that must be addressed to achieve commercialization. Therefore, this paper also discusses some prospects of designing MOFs for their specific catalytic applications to improve their catalytic properties and enhance selectivity. More importantly, an outlook is also provided on how MOF catalysts can further be developed to enable other catalytic reactions. Overall, MOFs have great potential as a photocatalytic material, provided they are uniquely designed to suit their intended applications. Photocatalysis ; Catalysis ; Metal-organic framework ; Materials science ; Nanotechnology ; Porosity ; Chemistry ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Carbon dioxide utilization in catalysis
Authors: Ali Hashemi | Sana Bougueroua | Marie‐Pierre Gaigeot | Evgeny A. Pidko
Source: ChemRxiv (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

High-Throughput Reactivity Exploration for Extended Databases of Transition Metal Catalysts A method is introduced for the automated reactivity exploration of extended in silico databases of transition metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical datasets. To address these challenges we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst datasets opens new opportunities for the design of high performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration. Reactivity (psychology) ; Workflow ; Catalysis ; Throughput ; Computer science ; Chemical space ; Database ; Key (lock) ; Characterization (materials science) ; In silico ; Chemistry ; Combinatorial chemistry ; Materials science ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Semantic Web and Ontologies
Authors: Lu Cheng | Xuning Chen | P. Hu | Xiaoming Cao
Source: ChemRxiv (preprint, 2023-04-18)
Field: Materials Science — Materials Chemistry

Advantages and Limitations of Hydrogen Peroxide for Direct Oxidation of Methane to Methanol at Mono-Copper Active Sites in Cu-Exchanged Zeolites The performance of the direct oxidation of methane to methanol (DMTM) is significantly influenced by the oxidant. It is still incredibly challenging to realize one-pot DMTM using dioxygen. So far, hydrogen peroxide is still the most frequently reported green oxidant for DMTM with a high selectivity for methanol. To achieve insights into the influence of oxidants on the DMTM performance, we computationally investigated the reaction mechanisms of DMTM using hydrogen peroxide at mono-copper sites in three kinds of Cu-exchanged zeolites with different sizes of the micropores. We identified the common advantages and limitations of hydrogen peroxide as the oxidant. In contrast to dioxygen, the O-O bond of hydrogen peroxide could be easily broken to produce reactive surface oxygen species, which enables the facile C-H bond activation of methane at a lower temperature. However, because of the radical-like process for C-H bond activation at mono-copper sites, it is kinetically challenging to actualize the preferential C-H bond activation of methane as compared to that of methanol. Moreover, the lower O-H bonding energy of hydrogen peroxide would result in the self-decomposition of hydrogen peroxide. Despite the bottlenecks, the kinetic analysis shows that it is still promising to improve catalysts to boost the performance of DMTM using hydrogen peroxide. Hydrogen peroxide ; Chemistry ; Methanol ; Catalysis ; Peroxide ; Copper ; Inorganic chemistry ; Photochemistry ; Decomposition ; Methane ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: See Piro | M Mohammed | Jan 2023 182-191 Mohan | S Lahari | K Amreen | S Dubey | R | P Goel | S | C Lai | X Ji | L Lai | P Lai | S Yuan | J Lakkakula | D Divakaran | R Srivastava | P Ingle | A Gade | R Raut | V Lassalle | See Montiel | M Schneider | A Lay-Ekuakille | K Sravani | S Lee | M Cho | C Li | S Lim | Y Jeong | H Ryu | S Jung | H Li | J Yu | J Li | S See Zong | J See Lv | L Li | D See Tan | M Li | Y Wang | H Gong | Y Li | B Song | X Zeng | S Liao | D See Zhu | H Lin | Y Zhao | L Lin | W Yu | F Liu | J Liu | S Liu | C Zou | S Xu | C Zhou | K Liu | L Huang | L Liu | S Ma | Y Gu | W Peng | N Liu | X Yu | Z Chen | T Yue | Y Liu | C Yang | H Jiang | H Pu | P Liu | J Sun | T See Jian | S Lotter | M Schafer | R Schober | X Lu | C Bai | H Lv | Y Xiao | J See Lakkakula
Source: IEEE Transactions on NanoBioscience (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

2023 Index IEEE Transactions on NanoBioscience Vol. 22 Index (typography) ; Computer science ; World Wide Web Advanced Nanomaterials in Catalysis
Authors: Gerald T. Seidler | Jared E. Abramson | Brandi M. Cossairt | Alexandra Velian | Ricardo A. Rivera-Maldonado | William M. Holden
Source: ChemRxiv (preprint, 2023-01-26)
Field: Chemistry — Analytical Chemistry

Laboratory X-ray Emission Spectrometer for Phosphorus Kα and Kβ Study of Air-Sensitive Samples The analytical chemistry of phosphorus-containing materials is often impeded by the long measurement times and relatively large sample masses needed for 31P NMR spectroscopy, by the scarcity and access limitations of synchrotron beamlines operating in the energy range of the P K-edge, by the challenges posed by species interconversion during liquid extraction, and by the considerable air-sensitivity typical of many phosphorus-containing materials and nanophases. To this end, we report the design and operation of a new laboratory-based spectrometer to simultaneously perform P Kα and Kβ X-ray emission spectroscopy (XES) while being housed in a research-grade controlled-atmosphere glovebox. Demonstration studies on nickel phosphide nanophases illustrate the importance of air-free XES and the value of simultaneous Kα and Kβ spectroscopy for identifying the P oxidation state and for investigating nanoscale influences on valence level electronic structure. Glovebox ; Spectrometer ; Phosphide ; Synchrotron ; Spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Environmental chemistry ; Nickel ; Physics ; Nuclear physics ; Optics ; Metallurgy Analytical chemistry methods development ; Electrochemical Analysis and Applications ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Xiaodong Zhao | YiXuan Luo | Juejing Liu | Wenjun Liu | Kevin M. Rosso | Xiaofeng Guo | Tong Geng | Ang Li | Xin Zhang
Source: arXiv (Cornell University) (preprint, 2023-03-20)
Field: Materials Science — Materials Chemistry

Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based models are capable to solve u-XRD mapping data for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we collected u-XRD mapping data from an LaCl3-calcite hydrothermal fluid system and trained two categories of models to solve the experimental XRD patterns. The models trained by synthetic XRD patterns show low accuracy (as low as 64%) when solving experimental u-XRD mapping data. The accuracy of the DNN models was significantly improved (90% or above) when training them with the dataset containing both synthetic and small number of labeled experimental u-XRD patterns. This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase. Artificial neural network ; Materials science ; Experimental data ; Calcite ; Synchrotron ; Diffraction ; Artificial intelligence ; Synthetic data ; X-ray crystallography ; Phase (matter) ; In situ ; Computer science ; Sample (material) ; Machine learning ; Analytical Chemistry (journal) ; Biological system ; Mineralogy ; Mathematics ; Physics ; Optics ; Chemistry ; Statistics ; Chromatography ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis