jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pratibha L. Gai | Edward Boyes
Source: Annual Review of Materials Research (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

Dynamic In Situ Microscopy in Single-Atom Catalysis: Advancing the Frontiers of Chemical Research Most heterogeneous catalytic processes occur between combinations of gases, liquids, and solids at elevated temperatures. They play a critical role for society in energy production, health care, a cleaner environment, industrial products, food, fuel cells, battery technologies, and photocatalysis. Dynamic gas–solid catalyst reactions take place at the atomic level, with active catalyst structures forming, and often also progressively and competitively deactivating, under reaction conditions. There is increasing evidence that single atoms and small clusters of atoms can act as primary active sites in catalytic reactions. Understanding and directing the reactions at the atomic level under controlled operating conditions are crucial for the development of improved materials and processes. We review advances in dynamic in situ microscopy for directly probing heterogeneous catalysis at the atomic level in live action and real time. Benefits include new knowledge and improved management of process fundamentals for greater efficiency and sustainability. Catalysis ; Nanotechnology ; Materials science ; Chemical reaction ; In situ ; Atom (system on chip) ; Photocatalysis ; Atomic force microscopy ; Chemical engineering ; Chemistry ; Computer science ; Organic chemistry Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Xiaojing WU | Stephan N. Steinmann | Carine Michel
Source: ChemRxiv (preprint, 2023-10-04)
Field: Materials Science — Materials Chemistry

Gaussian Attractive Potential for Carboxylate/Cobalt Surface Interactions Ligand-decorated metal surfaces play a pivotal role in various areas of chemistry, particularly in selective catalysis. Molecular dynamics simulations at the molecular mechanics level of theory are best adapted to gain complementary insights to experiments regarding the structure and dynamics of such organic films. However, standard force fields tend to capture only weak physisorption interactions. This is inadequate for ligands that are strongly adsorbed such as carboxylates on metal surfaces. To address this limitation, we employ the Gaussian Lennard-Jones (GLJ) potential, which incorporates an attractive Gaussian potential between the surface and ligand atoms. Here, we develop this approach for the interaction between cobalt surfaces and carboxylate ligands. The accuracy of the GLJ approach is validated through the analysis of the interaction of oxygen with two distinct cobalt surfaces. The accuracy of this method reaches a root mean square deviation (RMSD) of about 3 kcal/mol across all probed configurations, which corresponds to a percentage error of roughly 4%. Application of the GLJ force field to the dynamics of the organic layer on these surfaces reveals how the ligand concentration influences the film order, and highlights differing mobility in the x and y directions, attributable to surface corrugation on Co(1120). GLJ is versatile, suitable for a broad range of metal/ligand systems, and can, subsequently, be utilized to study the organic film on the adsorption/desorption of reactants and products during a catalytic process. Physisorption ; Carboxylate ; Cobalt ; Molecular dynamics ; Adsorption ; Desorption ; Ligand (biochemistry) ; Chemical physics ; Gaussian ; Chemistry ; Materials science ; Computational chemistry ; Physical chemistry ; Inorganic chemistry ; Stereochemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Surface and Thin Film Phenomena
Authors: B Swathi | K. Praveena | Neeraj Chahuan | Niti Sharma | Hazim Y. Saeed | Alok Jain
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

RETRACTED: Thermal Modulation in Nanostructured Materials for Advanced Applications The Publisher has been made aware of ethical breaches affecting this proceeding published in E3S Web of Conferences, Volume 430 (2023) . These instances involve a specific author, K.K. Saxena who used citation manipulation and inappropriate references in 47 articles, for a total of 310 citations. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. See our publishing ethics policies . The Guest Editor of the proceedings volume endorsed the Publisher's decision to retract these articles. Web of Conferences is extremely grateful to the whistleblower for bringing this case to our attention. See the retraction notice E3S Web of Conferences 430 , 00002 (2023), https://doi.org/10.1051/e3sconf/202443000002 Thermoelectric materials ; Materials science ; Nanotechnology ; Engineering physics ; Thermal conductivity ; Thermoelectric effect ; Nanoscopic scale ; Thermal ; Heat transfer ; Mechanical engineering ; Engineering ; Composite material ; Physics ; Mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials
Authors: Xiaojing WU | Stephan N. Steinmann | Carine Michel
Source: ChemRxiv (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Gaussian Attractive Potential for Carboxylate/Cobalt Surface Interactions Ligand-decorated metal surfaces play a pivotal role in various areas of chemistry, particularly in selective catalysis. Molecular dynamics simulations at the molecular mechanics level of theory are best adapted to gain complementary insights to experiments regarding the structure and dynamics of such organic films. However, standard force fields tend to capture only weak physisorption interactions. This is inadequate for ligands that are strongly adsorbed such as carboxylates on metal surfaces. To address this limitation, we employ the Gaussian Lennard-Jones (GLJ) potential, which incorporates an attractive Gaussian potential between the surface and ligand atoms. Here, we develop this approach for the interaction between cobalt surfaces and carboxylate ligands. The accuracy of the GLJ approach is validated through the analysis of the interaction of oxygen with two distinct cobalt surfaces. The accuracy of this method reaches a root mean square deviation (RMSD) of about 3 kcal/mol across all probed configurations, which corresponds to a percentage error of roughly 4%. Application of the GLJ force field to the dynamics of the organic layer on these surfaces reveals how the ligand concentration influences the film order, and highlights differing mobility in the x and y directions, attributable to surface corrugation on Co(1120). GLJ is versatile, suitable for a broad range of metal/ligand systems, and can, subsequently, be utilized to study the organic film on the adsorption/desorption of reactants and products during a catalytic process. Physisorption ; Carboxylate ; Molecular dynamics ; Adsorption ; Cobalt ; Chemical physics ; Ligand (biochemistry) ; Desorption ; Gaussian ; Chemistry ; Computational chemistry ; Materials science ; Physical chemistry ; Inorganic chemistry ; Stereochemistry Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Surface and Thin Film Phenomena
Authors: Din Bandhu | B. Pravallika | Abhishek Kaushik | Surovi Paul | Hanaa Addai Ali | Vishal S. Sharma
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

RETRACTED: Revolutionizing Material Science: Exploring the Novel Applications of Thermally-Enhanced Processes in Next-Generation Materials The Publisher has been made aware of ethical breaches affecting this proceeding published in E3S Web of Conferences, Volume 430 (2023) . These instances involve a specific author, K.K. Saxena who used citation manipulation and inappropriate references in 47 articles, for a total of 310 citations. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. See our publishing ethics policies . The Guest Editor of the proceedings volume endorsed the Publisher's decision to retract these articles. Web of Conferences is extremely grateful to the whistleblower for bringing this case to our attention. See the retraction notice E3S Web of Conferences 430 , 00002 (2023), https://doi.org/10.1051/e3sconf/202443000002 Materials science ; Nanotechnology ; Spark plasma sintering ; Materials processing ; Electronics ; Ceramic ; Renewable energy ; Material Design ; Process engineering ; Engineering physics ; Composite material ; Engineering Diamond and Carbon-based Materials Research ; Advanced materials and composites ; Laser-Ablation Synthesis of Nanoparticles
Authors: Devan Solanki | Jason A. Röhr | Zachary S. Fishman | Bin Liu | Rito Yanagi | Eli Stavitski | Deyu Lu | Shu Hu
Source: MRS Communications (article, 2023-11-28)
Field: Materials Science — Materials Chemistry

Probing rutile solid-phase crystallization of atomically mixed Mn-alloyed TiO2 coatings through XANES analysis Materials science ; Dopant ; Rutile ; Anatase ; Crystallization ; Amorphous solid ; Microstructure ; Chemical engineering ; Ternary operation ; Phase (matter) ; Metastability ; Oxide ; Metallurgy ; Crystallography ; Doping ; Catalysis ; Optoelectronics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Cheng Zeng | Sushree Jagriti Sahoo | Andrew J. Medford | Andrew A. Peterson
Source: arXiv (Cornell University) (preprint, 2023-06-02)
Field: Materials Science — Materials Chemistry

The phase stability of large-size nanoparticle alloy catalysts at ab initio quality using a nearsighted force-training approach CoPt nanoparticle catalysts are integral to commercial fuel cells. Such systems are prohibitive to fully characterize with electronic structure calculations. Machine-learned potentials offer a scalable solution; however, such potentials are only reliable if representative training data can be employed, which typically requires large electronic structure calculations. Here, we use the nearsighted-force training approach to make high-fidelity machine-learned predictions on large nanoparticles with $&gt;$5,000 atoms using only systematically generated small structures ranging from 38-168 atoms. The resulting ensemble model shows good accuracy and transferability in describing relative energetics for CoPt nanoparticles with various shapes, sizes and Co compositions. It is found that the fcc(100) surface is more likely to form a L1$_0$ ordered structure than the fcc(111) surface. The energy convex hull of the icosahedron shows the most stable particles have Pt-rich skins and Co-rich underlayers. Although the truncated octahedron is the most stable shape across all sizes of Pt nanoparticles, a crossover to icosahedron exists due to a large downshift of surface energy for CoPt nanoparticle alloys. The downshift can be attributed to strain release on the icosahedron surface due to Co alloying. We introduced a simple empirical model to describe the role of Co alloying in the crossover for CoPt nanoparticles. With Monte-Carlo simulations we additionally searched for the most stable atomic arrangement for a truncated octahedron with equal Pt and Co compositions, and also we studied its order-disorder phase transition. We validated the most stable configurations with a new highly scalable density functional theory code called SPARC. Lastly, the order-disorder phase transition for a CoPt nanoparticle exhibits a lower transition temperature and a smoother transition, compared to the bulk CoPt alloy. Materials science ; Nanoparticle ; Ab initio ; Phase (matter) ; Octahedron ; Chemical physics ; Nanotechnology ; Crystallography ; Crystal structure ; Chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Electrocatalysts for Energy Conversion
Authors: Toshiaki Taniike | Aya Fujiwara | Sunao Nakanowatari | Fernando Escobar | Keisuke Takahashi
Source: ChemRxiv (preprint, 2023-07-06)
Field: Materials Science — Materials Chemistry

Automatic Feature Engineering for Catalyst Design Using Small Data without Prior Knowledge of the Target Catalysis The empirical aspect of descriptor design with limited data in catalyst informatics entails a logical contradiction as it relies on sufficient prior knowledge for exploring the unknown. In this study, we developed a technique for automatic feature engineering (AFE) that works on small catalyst data without requiring any prior knowledge of the target catalysis. This technique generates a large number of features through mathematical operations on general physicochemical fea-tures of catalytic components, and extracts the relevant features for the desired catalysis, essentially screening a large number of hypotheses on a machine. AFE yielded reasonable regression results for three types of heterogeneous cataly-sis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set was swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualized the machine’s process of acquiring precise recognition of catalyst design. AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries. Catalysis ; Computer science ; Feature (linguistics) ; Process (computing) ; Artificial intelligence ; Cheminformatics ; Feature engineering ; Machine learning ; Data mining ; Chemistry ; Computational chemistry ; Deep learning ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Text and Document Classification Technologies
Authors: Ruquan Ye | Weihua Guo | Siwei Zhang | Jun‐Jie Zhang | Haoran Wu | Yangbo Ma | Yun Mi Song | Le Cheng | Liang Chang | Geng Li | Yong Liu | Guodan Wei | Lin Gan | Minghui Zhu | Shibo Xi | Xue Wang | Boris I. Yakobson | Ben Zhong Tang
Source: Research Square (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Accelerating multielectron reduction at CuxO nanograins interfaces with controlled local electric field Abstract Regulating electron transport rate and ion concentrations in the local microenvironment of active site can overcome the slow kinetics and unfavorable thermodynamics of CO 2 electroreduction. However, simultaneous optimization of both kinetics and thermodynamics is hindered by synthetic constraints and poor mechanistic understanding. Here we leverage laser-assisted manufacturing for synthesizing Cu x O bipyramids with controlled tip angles and abundant nanograins, and elucidate the mechanism of the relationship between electron transport/ion concentrations and electrocatalytic performance. Potassium/OH − adsorption tests and finite element simulations corroborate the contributions from strong electric field at the sharp tip. In situ Fourier transform infrared spectrometry and differential electrochemical mass spectrometry unveil the dynamic evolution of critical *CO/*OCCOH intermediates and product profiles, complemented with theoretical calculations that elucidate the thermodynamic contributions from improved coupling at the Cu + /Cu 2+ interfaces. Through modulating the electron transport and ion concentrations, we achieve high Faradaic efficiency of 81% at ~900 mA cm −2 for C 2+ products via CO 2 RR. Similar enhancement is also observed for nitrate reduction reaction (NITRR), achieving 81.83 mg h −1 ammonia yield rate. Coupling the CO 2 RR and NITRR systems demonstrates the potential for valorizing flue gases and nitrate wastes, which suggests a practical approach for carbon-nitrogen cycling. Reduction (mathematics) ; Electric field ; Field (mathematics) ; Materials science ; Engineering physics ; Optoelectronics ; Physics ; Quantum mechanics Electronic and Structural Properties of Oxides ; Copper-based nanomaterials and applications ; ZnO doping and properties
Authors: Dane Morgan | Jun Meng | Md Sariful Sheikh | Ryan Jacobs | Jian Liu | William O. Nachlas | Xiangguo Li
Source: Research Square (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Computational Discovery of Fast Interstitial Oxygen Conductors Oxygen ; Electrical conductor ; Materials science ; Conductivity ; Diffusion ; Chemical physics ; Interstitial defect ; Ionic bonding ; Chemistry ; Ion ; Thermodynamics ; Physical chemistry ; Optoelectronics ; Composite material ; Physics Advancements in Solid Oxide Fuel Cells ; Nuclear Materials and Properties ; Metal Extraction and Bioleaching
Authors: Mayukh Das | Akshay Wali | Amit Patel | Suyash Rai | Anand B. Puthirath | Pulickel M. Ajayan | Anchal Srivast | Saptarshi Das
Source: Research Square (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

An Atomically Thin and Photosensitive Vanadium Disulfide Memtransistor Vanadium ; Disulfide bond ; Materials science ; Molybdenum disulfide ; Nanotechnology ; Optoelectronics ; Chemistry ; Metallurgy ; Biochemistry 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Grzegorz Kaszuba | Amirhossein Naghdi Dorabati | Stefanos Papanikolaou | Andrzej Jaszkiewicz | Piotr Sankowski
Source: arXiv (Cornell University) (preprint, 2023-12-26)
Field: Materials Science — Materials Chemistry

Compositional Search of Stable Crystalline Structures in Multi-Component Alloys Using Generative Diffusion Models Exploring the vast composition space of multi-component alloys presents a challenging task for both \textit{ab initio} (first principles) and experimental methods due to the time-consuming procedures involved. This ultimately impedes the discovery of novel, stable materials that may display exceptional properties. Here, the Crystal Diffusion Variational Autoencoder (CDVAE) model is adapted to characterize the stable compositions of a well studied multi-component alloy, NiFeCr, with two distinct crystalline phases known to be stable across its compositional space. To this end, novel extensions to CDVAE were proposed, enhancing the model's ability to reconstruct configurations from their latent space within the test set by approximately 30\% . A fact that increases a model's probability of discovering new materials when dealing with various crystalline structures. Afterwards, the new model is applied for materials generation, demonstrating excellent agreement in identifying stable configurations within the ternary phase space when compared to first principles data. Finally, a computationally efficient framework for inverse design is proposed, employing Molecular Dynamics (MD) simulations of multi-component alloys with reliable interatomic potentials, enabling the optimization of materials property across the phase space. Component (thermodynamics) ; Ternary operation ; Diffusion ; Space (punctuation) ; Inverse ; Computer science ; Crystal structure prediction ; Statistical physics ; Autoencoder ; Materials science ; Phase (matter) ; Generative model ; Generative grammar ; Crystal structure ; Thermodynamics ; Artificial intelligence ; Mathematics ; Physics ; Crystallography ; Chemistry ; Artificial neural network ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Microstructure and Mechanical Properties of Steels
Authors: Kalsoom Akhtar | Asma A. Alhaj | Esraa M. Bakhsh | Sher Bahadar Khan | Taghreed M. Fagieh
Source: Materials (article, 2023-10-31)
Field: Chemistry — Organic Chemistry

SnAg2O3-Coated Adhesive Tape as a Recyclable Catalyst for Efficient Reduction of Methyl Orange Silver oxide-doped tin oxide (SnAg<sub>2</sub>O<sub>3</sub>) nanoparticles were synthesized and different spectroscopic techniques were used to structurally identify SnAg<sub>2</sub>O<sub>3</sub> nanoparticles. The reduction of 4-nitrophenol (4-NP), congo red (CR), methylene blue (MB), and methyl orange (MO) was studied using SnAg<sub>2</sub>O<sub>3</sub> as a catalyst. Only 1.0 min was required to reduce 95% MO; thus, SnAg<sub>2</sub>O<sub>3</sub> was found to be effective with a rate constant of 3.0412 min<sup>-1</sup>. Being a powder, SnAg<sub>2</sub>O<sub>3</sub> is difficult to recover and recycle multiple times. For this reason, SnAg<sub>2</sub>O<sub>3</sub> was coated on adhesive tape (AT) to make it recyclable for large-scale usage. SnAg<sub>2</sub>O<sub>3</sub>@AT catalyst was assessed toward MO reduction under various conditions. The amount of SnAg<sub>2</sub>O<sub>3</sub>@AT, NaBH<sub>4</sub>, and MO was optimized for best possible reduction conditions. The catalyst had a positive effect since it speed up the reduction of MO by adding more SnAg<sub>2</sub>O<sub>3</sub>@AT and NaBH<sub>4</sub> as well as lowering the MO concentration. SnAg<sub>2</sub>O<sub>3</sub>@AT totally reduced MO (98%) in 3.0 min with a rate constant of 1.3669 min<sup>-1</sup>. These findings confirmed that SnAg<sub>2</sub>O<sub>3</sub>@AT is an effective and useful catalyst for MO reduction that can even be utilized on a large scale for industrial purposes. Adhesive ; Orange (colour) ; Catalysis ; Methyl orange ; Materials science ; Composite material ; Chemistry ; Organic chemistry ; Food science ; Layer (electronics) Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Felipe Lopes Oliveira | Pierre M. Esteves
Source: arXiv (Cornell University) (preprint, 2023-10-23)
Field: Materials Science — Materials Chemistry

Suplementary data for pyCOFBuilder: A python package for automated creation of Covalent Organic Framework models based on the reticular approach Covalent Organic Frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry. These traits make COFs a highly promising choice for a range of applications. However, with their vast potential structures, exploring COFs experimentally can be challenging and time-consuming, yet it remains an attractive avenue for computational high-throughput studies. However, generating COF structures can be a time-consuming and challenging task. To address this challenge, here we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder. Python (programming language) ; Computer science ; Computational science ; Network topology ; Software ; Software engineering ; Programming language ; Operating system Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Computing and Algorithms