jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Margareth S. Baidun | Adarsh V. Kalikadien | Laurent Lefort | Evgeny A. Pidko
Source: The Journal of Physical Chemistry C (article, 2024-05-02)
Field: Chemistry — Inorganic Chemistry

Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation Data-driven catalyst design is a promising approach for addressing the challenges in identifying suitable catalysts for synthetic transformations. Models with descriptor calculations relying solely on the precatalyst structure are potentially generalizable but may overlook catalyst-substrate interactions. This study explores substrate-specific interactions in the context of Rh-catalyzed asymmetric hydrogenation to elucidate the impact of substrate inclusion on the catalyst structure and on the descriptors derived from it. We compare a catalyst-substrate complex with methyl 2-acetamidoacrylate as a model substrate with the generic precatalyst structure involving a placeholder substrate, norbornadiene, across 11 Rh-based catalysts with bidentate bisphosphine ligands. For these systems, a full conformer ensemble analysis reveals an intriguing finding: the rigid substrate induces conformational freedom in the ligand. This flexibility gives rise to a more diverse conformer landscape, showing a previously overlooked aspect of catalyst-substrate dynamics. Electronic descriptor variations particularly highlight differences between substrate-specific and precatalyst structures. This study suggests that generic precatalyst-like models may lack crucial insights into the conformational freedom of the catalyst. We speculate that such conformational freedom may be a more general phenomenon that can influence the development of generalizable predictive models of computational TM-based catalysis. Catalysis ; Conformational isomerism ; Substrate (aquarium) ; Context (archaeology) ; Chemistry ; Molecular dynamics ; Flexibility (engineering) ; Degrees of freedom (physics and chemistry) ; Substrate specificity ; Computational chemistry ; Biochemical engineering ; Organic chemistry ; Mathematics ; Thermodynamics ; Physics ; Molecule ; Enzyme Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Bhupinder Singh | Samuel Hefford | E. Sánchez | Michael Barter | Daniel R. Slocombe | Serena A. Cussen | Georgios Dimitrakis
Source: International Journal of Heat and Mass Transfer (article, 2024-11-12)
Field: Chemistry — Organic Chemistry

A novel method for fast and efficient numerical simulation of microwave heating in liquids during mixing • A novel simulation method is proposed to realize microwave heating of liquids under stirring. • Simulations were carried out to predict the temperature rise with microwave exposure time. • The effect of stirring on temperature uniformity was studied. • The effect of microwave power on coefficient of variation in temperature discussed. Microwave-assisted chemical reactions present potentially more sustainable routes for process intensification compared with traditional approaches, due to the reduction of reaction times, temperatures, and side reactions. Despite the common misconception that microwave heating is uniform, many processes can be expected to show temperature distributions that vary significantly over the volume, even at length scales far below the operating wavelength. Numerical methods are often employed in the design and optimization phase of a given process, however, due to the multitude of interdependent physics required; the fast and efficient modelling of microwave heating in liquids remains a significant challenge, particularly with respect to computational resources. Here, we report a new multi-physics simulation methodology that models microwave heating of liquids during agitation, requiring less computational resources and delivering temperature predictions within 2.78 % of relative root mean square error. By applying the frozen rotor approach, near-perfect temperature profiles are predicted at approximately 600 times faster convergence time compared to the conventional sliding mesh method. Our proposed model can be used to mimic real reaction systems in a fast and resource-efficient way. Mixing (physics) ; Microwave heating ; Microwave ; Materials science ; Mechanics ; Computer science ; Process engineering ; Nuclear engineering ; Telecommunications ; Physics Microwave-Assisted Synthesis and Applications ; Food Drying and Modeling
Authors: Yunxuan Zhu | Markus B. Raschke | Douglas Natelson | Longji Cui
Source: Nanophotonics (review, 2024-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular scale nanophotonics: hot carriers, strong coupling, and electrically driven plasmonic processes Plasmonic modes confined to metallic nanostructures at the atomic and molecular scale push the boundaries of light-matter interactions. Within these extreme plasmonic structures of ultrathin nanogaps, coupled nanoparticles, and tunnelling junctions, new physical phenomena arise when plasmon resonances couple to electronic, exitonic, or vibrational excitations, as well as the efficient generation of non-radiative hot carriers. This review surveys the latest experimental and theoretical advances in the regime of extreme nano-plasmonics, with an emphasis on plasmon-induced hot carriers, strong coupling effects, and electrically driven processes at the molecular scale. We will also highlight related nanophotonic and optoelectronic applications including plasmon-enhanced molecular light sources, photocatalysis, photodetection, and strong coupling with low dimensional materials. Plasmon ; Nanophotonics ; Photodetection ; Materials science ; Quantum tunnelling ; Nanotechnology ; Nanomaterials ; Coupling (piping) ; Optoelectronics ; Photodetector Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Molecular Junctions and Nanostructures
Authors: Peter Rodič | Barbara Kapun | Ingrid Milošev
Source: npj Materials Degradation (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Complementary corrosion protection of cast AlSi7Mg0.3 alloy using Zr-Cr conversion and polyacrylic/siloxane-silica multilayer coatings Abstract Aluminium cast alloy AlSi7Mg0.3 is a lightweight metal commonly used in automotive, aeronautical and mechanical applications. It has good corrosion resistance but, under harsh operative conditions, would benefit from additional protection. In this study, a corrosion-protective multilayer coating system for AlSi7Mg0.3 based on hexafluoro-zirconated trivalent chromium coating (Zr-CrCC) and polyacrylic/siloxane-silica (PEHA-SS) coating was developed. The Zr-CrCC was formed by immersion of the substrate in a commercial conversion bath (SurTec® 650). PEHA-SS synthesis was based on organic precursors (2-ethylhexyl acrylate and [3-(methacryloyloxy)propyl]trimethoxysilane) and an inorganic precursor, tetraethyl orthosilicate. After deposition on AlSi7Mg0.3, each coating was first characterised individually, followed by the analysis of the multilayer using scanning electron microscopy and energy-dispersive X-ray spectroscopy. The adhesion of the coatings was evaluated with a cross-hatch cut test. The corrosion studies in sodium chloride solution using electrochemical impedance spectroscopy and salt spray testing showed that the multilayer system is superior to individual Zr-CrCC and PEHA-SS coatings. After 4 months in 0.1 M NaCl, the multilayer-coated samples exhibited the impedance at 10 mHz in the range of GΩ cm 2 , while scribed samples withstood the corrosion attack in a salt spray chamber for one week. Thus, albeit only about 100 nm thick, the Zr-CrCC deposited between the substrate and a 9-micrometre thick barrier sol-gel PEHA-SS coating acts as an active corrosion protection interlayer and contributes to the overall protectiveness of the multilayer system. Siloxane ; Materials science ; Alloy ; Corrosion ; Metallurgy ; Polyacrylic acid ; Conversion coating ; Composite material ; Polymer Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties ; Material Properties and Applications
Authors: Omar A. El Seoud | Shirley Possidonio | Naved I. Malek
Source: Liquids (article, 2024-01-03)
Field: Chemistry — Physical and Theoretical Chemistry

Solvatochromism in Solvent Mixtures: A Practical Solution for a Complex Problem Many reactions are carried out in solvent mixtures, mainly because of practical reasons. For example, E2 eliminations are favored over SN2 substitutions in aqueous organic solvents because the bases are desolvated. This example raises the question: how do we chose binary solvents to favor reaction outcomes? This important question is deceptively simple because it requires that we understand the details of all interactions within the system. Solvatochromism (solvent-dependent color change of a substance) has contributed a great deal to answer this difficult question, because it gives information on the interactions between solvents, solute-solvent, and presumably transition state-solvent. This wealth of information is achieved by simple spectroscopic measurements of selected (solvatochromic) substances, or probes. An important outcome of solvatochromism is that the probe solvation layer composition is almost always different from that of bulk mixed solvent. In principle, this difference can be exploited to “tune” the composition of solvent mixture to favor the reaction outcome. This minireview addresses the use of solvatochromic probes to quantify solute-solvent interactions, leading to a better understanding of the complex effects of solvent mixtures on chemical phenomena. Because of their extensive use in chemistry, we focus on binary mixtures containing protic-, and protic-dipolar aprotic solvents. Solvatochromism ; Solvent ; Solvation ; Chemistry ; Solvent effects ; SN2 reaction ; Aqueous solution ; Computational chemistry ; Organic chemistry Photochemistry and Electron Transfer Studies ; Electrochemical Analysis and Applications ; Analytical Chemistry and Chromatography
Authors: Xia Zhao | Yilin Hou | Bao‐Chen Qian | Guang‐Bin Shen
Source: ACS Omega (article, 2024-06-04)
Field: Chemistry — Organic Chemistry

Thermodynamic H-Abstraction Abilities of Nitrogen Centered Radical Cations as Potential Hydrogen Atom Transfer Catalysts in Y–H Bond Functionalization Y-H bond functionalization has always been the focus of research interest in the area of organic synthesis. Direct hydrogen atom transfer (HAT) from the Y-H bond is one of the most efficient and practical methods to activate the Y-H bond. Recently, nitrogen centered radical cations were broadly utilized as H-abstraction catalysts to activate Y-H bonds via the HAT process. As a type of HAT catalyst, the H-affinity of nitrogen centered radical cations is a significant thermodynamic parameter to quantitatively evaluate the thermodynamic H-abstraction potentials of nitrogen centered radical cations. In this work, the p<i>K</i> <sub>a</sub> values of 120 protonated N-containing compounds in acetonitrile (AN) are predicted, and the H-affinities of 120 nitrogen centered radical cations in AN are derived from the reduction potentials of nitrogen centered radical cations and p<i>K</i> <sub>a</sub> of protonated N-containing compounds using Hess' law. This work focuses on the H-abstraction abilities of 120 nitrogen centered radical cations in AN to enrich the molecule library of novel HAT catalysts or H-abstractors and provides valuable thermodynamic guidelines for the application of nitrogen centered radical cations in Y-H bond functionalization. Surface modification ; Catalysis ; Chemistry ; Hydrogen atom abstraction ; Nitrogen ; Hydrogen bond ; Nitrogen atom ; Hydrogen atom ; Abstraction ; Hydrogen ; Photochemistry ; Organic chemistry ; Physical chemistry ; Molecule ; Group (periodic table) Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Synthesis and Catalytic Reactions
Authors: Koushik Sarkar | Muhammad Anisuzzaman Talukder
Source: Materials Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structurally realistic carbide-derived carbon model in annealing molecular dynamics methodology with analytic bond-order potential A realistic carbide-derived carbon (CDC) model is produced with ABOP at a low computational cost. The simulated CDC agrees well with experiments over a broad temperature range, facilitating the production of atomistic models of experimental samples. Molecular dynamics ; Carbide ; Simulated annealing ; Materials science ; Annealing (glass) ; Computational chemistry ; Statistical physics ; Chemistry ; Chemical physics ; Nanotechnology ; Mathematics ; Physics ; Mathematical optimization ; Composite material Boron and Carbon Nanomaterials Research ; Carbon Nanotubes in Composites ; Diamond and Carbon-based Materials Research
Authors: Caitlin E. Shields | Thomas Fellowes | Anna G. Slater | Andrew I. Cooper | Keith G. Andrews | Filip Szczypiński
Source: Chemical Communications (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation Organic cages can possess complex, functionalised cavities that make them promising candidates for synthetic enzyme mimics. Conformationally flexible, chemically robust structures are needed for adaptable guest binding and catalysis, but rapidly exchanging systems are difficult to resolve in solution. Here, we use low-cost calculations and high-throughput crystallisation to identify accessible conformers of a recently reported organic cage by 'locking' them in the solid state. The conformers exhibit varying distances between the internal carboxylic acid groups, suggesting adaptability for binding a wide array of target guest molecules. Solid-state ; Cage ; Computation ; Automation ; Amide ; Chemistry ; State (computer science) ; Computer science ; Computational chemistry ; Organic chemistry ; Engineering ; Physical chemistry ; Algorithm ; Mechanical engineering Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; Supramolecular Chemistry and Complexes
Authors: Zhikang Zhu | Chenyang Zhu | Yibo Tu | Tianxiang Shao | Yida Wang | Weihong Liu | Yiming Liu | Yue Zang | Qingya Wei | Wensheng Yan
Source: Cell Reports Physical Science (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted exploration of new non-fullerene acceptors for high-efficiency organic solar cells between model prediction and experimental verification is less than 3% Overfitting ; Organic solar cell ; Computer science ; Generalization ; Fullerene ; Throughput ; Artificial intelligence ; Energy conversion efficiency ; Machine learning ; Biological system ; Chemistry ; Materials science ; Artificial neural network ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Computational Drug Discovery Methods
Authors: Yasushi Shibuta
Source: ISIJ International (article, 2024-04-02)
Field: Materials Science — Materials Chemistry

Molecular Dynamics of Solidification Over many years, mesoscale analysis such as the phase-field method has been the mainstream for numerical simulation of solidification. In contrast, our group has taken the initiative in applying molecular dynamics (MD) simulation to various problems in solidification. In this review, recent advances and contributions of MD simulations for solidification are presented. The primary contribution of MD simulation is the derivation of solid-liquid interfacial properties since it is not easy to measure these properties experimentally with high precision. In addition, recent significant progress in computational environments has dramatically expanded the possibilities of MD simulations for solidification. Now, MD simulations with a scale of billion atoms at the micrometer-scale have become a reality, enabling the exploration of analyses previously dominated by mesoscale methods, such as grain growth and dendrite growth. In particular, the dendrite growth at the micrometer-scale presented here represents the first achievement of directly simulating a typical four-fold symmetrical dendrite structure solely through atomic-scale simulations, to the best of the author's knowledge. Moreover, new attempts at the fusion of data-driven methods and MD simulations are presented in this review, aiming to contribute to the rapid development of the field of solidification in the future. Molecular dynamics ; Materials science ; Dynamics (music) ; Metallurgy ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: N. Rajeswari | K. Sunitha | Balasubramanian Kandasubramanian
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Performance of nanofillers in epoxy resin for corrosion protection coating on metallic substrates The paper combines and reviews the experimental studies on performance of various potential nanofillers elements incorporated in the epoxy polymer resin that are used as protective coating for metal substrate. The epoxy composite formed by dispersing the nano sized secondary elements namely silica oxide, aluminum oxide, titanium oxide, cerium oxide, graphite oxide and nano clay in the epoxy matrix are studied in the perspective of corrosion resistance, adhesion strength and dispersion properties. The paper encompasses the study on epoxy nanocomposite with single element reinforcement particles and also the hybrid of two different elements that are used as reinforcement fillers. The discussion witness’s various corrosive protection assessing techniques such as electrochemical studies, pull of adhesion test, salt spray test and their results are studied and analyzed in the prospect of overviewing the performance of nanofillers in epoxy matrix. Epoxy ; Materials science ; Composite material ; Oxide ; Corrosion ; Nanocomposite ; Coating ; Salt spray test ; Metallurgy Polymer Nanocomposite Synthesis and Irradiation ; Corrosion Behavior and Inhibition ; Polymer Nanocomposites and Properties
Authors: Rubina Munir | Sumera Zaib | Muhammad Zia-Ur-Rehman | Hira Javed | Ayesha Roohi | Muhammad Zaheer | Nabiha Fatima | M. A. Bhat | Imtiaz Khan
Source: Frontiers in Chemistry (article, 2024-05-24)
Field: Chemistry — Organic Chemistry

Exploration of morpholine-thiophene hybrid thiosemicarbazones for the treatment of ureolytic bacterial infections via targeting urease enzyme: Synthesis, biochemical screening and computational analysis An important component of the pathogenicity of potentially pathogenic bacteria in humans is the urease enzyme. In order to avoid the detrimental impact of ureolytic bacterial infections, the inhibition of urease enzyme appears to be an appealing approach. Therefore, in the current study, morpholine-thiophene hybrid thiosemicarbazone derivatives (<b>5a-i</b>) were designed, synthesized and characterized through FTIR, <sup>1</sup>H NMR, <sup>13</sup>C NMR spectroscopy and mass spectrometry. A range of substituents including electron-rich, electron-deficient and inductively electron-withdrawing groups on the thiophene ring was successfully tolerated. The synthesized derivatives were evaluated <i>in vitro</i> for their potential to inhibit urease enzyme using the indophenol method. The majority of compounds were noticeably more potent than the conventional inhibitor, thiourea. The lead inhibitor, 2-(1-(5-chlorothiophen-2-yl)ethylidene)-<i>N</i>-(2-morpholinoethyl)hydrazinecarbothioamide (<b>5g</b>) inhibited the urease in an uncompetitive manner with an IC<sub>50</sub> value of 3.80 ± 1.9 µM. The findings of the docking studies demonstrated that compound <b>5g</b> has a strong affinity for the urease active site. Significant docking scores and efficient binding free energies were displayed by the lead inhibitor. Finally, the ADME properties of lead inhibitor (<b>5g</b>) suggested the druglikeness behavior with zero violation. Morpholine ; Urease ; Chemistry ; Thiophene ; ADME ; Enzyme ; Uncompetitive inhibitor ; Docking (animal) ; Semicarbazone ; Lead compound ; Active site ; Stereochemistry ; Combinatorial chemistry ; Biochemistry ; Medicinal chemistry ; Organic chemistry ; Non-competitive inhibition ; In vitro Synthesis and biological activity ; Microbial Applications in Construction Materials ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Samuel W. Yuan | S. V. Dordevic
Source: Scientific Reports (article, 2024-05-04)
Field: Materials Science — Materials Chemistry

Diffusion models for conditional generation of hypothetical new families of superconductors Effective computational search holds great potential for aiding the discovery of high-temperature superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches at predicting new superconductors within existing superconductor families but have yet to be able to generate completely new families of superconductors. We address this limitation by implementing conditioning-a method to control the generation process-for our generative model and develop SuperDiff, a denoising diffusion probabilistic model with iterative latent variable refinement conditioning for HTS discovery-the first deep generative model for superconductor discovery with conditioning on reference compounds. With SuperDiff, by being able to control the generation process, we were able to computationally generate completely new families of hypothetical superconductors for the very first time. Given that SuperDiff also has relatively fast training and inference times, it has the potential to be a very powerful tool for accelerating the discovery of new superconductors and enhancing our understanding of them. Inference ; Generative model ; Computer science ; Generative grammar ; Process (computing) ; Artificial intelligence ; Superconductivity ; Machine learning ; Business process discovery ; Probabilistic logic ; Physics ; Materials science Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Topic Modeling