jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dong Fan | Supriyo Naskar | Guillaume Maurin
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Unconventional mechanical and thermal behaviors of MOF CALF-20 CALF-20 was recently identified as a novel benchmark sorbent for CO$_2$ capture at the industrial scale, however comprehensive atomistic insight into its mechanical/thermal properties under working conditions is still lacking. In this study, we developed a general-purpose machine-learned potential (MLP) for the CALF-20 MOF framework that predicts the thermodynamic and mechanical properties of the structure at finite temperatures within first-principles accuracy. Interestingly, CALF-20 was demonstrated to exhibit both negative area compression and negative thermal expansion. Most strikingly, upon application of the tensile strain along the [001] direction, CALF-20 was shown to display a distinct two-step elastic deformation behavior, unlike typical MOFs that undergo plastic deformation after elasticity. Furthermore, this MOF was shown to exhibit a spectacular fracture strain of up to 27% along the [001] direction at room temperature comparable to that of MOF glasses. These abnormal thermal and mechanical properties make CALF-20 as attractive material for flexible and stretchable electronics and sensors. Materials science ; Ultimate tensile strength ; Thermal expansion ; Thermal ; Elasticity (physics) ; Composite material ; Compression (physics) ; Deformation (meteorology) ; Nanotechnology ; Thermodynamics ; Physics Thermal Expansion and Ionic Conductivity ; Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Sathiyaraj Chinnasamy | M Ramachandran | Murugan Aswini
Source: Journal on Materials and its Characterization (article, 2023-03-08)
Field: Materials Science — Materials Chemistry

Evaluation of Smart Materials in using the VIKOR Method Smart materials are environmentally friendly Materials that respond to changes and then a Material property is subject to change. Any extras without the need for control or electronics an actuator or sensor from objects these property changes can be used to create. Smart material technology and its functions adapting to environmental changes through implementation helps us. Multifunctional material, of sorts A smart material, its geometry modification or property by electrical impulses, to produce change can be implemented. From this analysis the VIKOR technique is the most accurate determines a worst-case-good solution, however, is variation these Distance is not taken into account. VIKOR the approach is Multi-Criteria Selection (MCDM) or Multicriteria selection is an evaluation technique. Alternative: VIKOR method ; Computer science ; Artificial intelligence Material Selection and Properties
Source: NanoWorld Journal (review, 2023-12-22)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Nanomaterials and Polymers: A Detailed Review Approach on Nano-informatics, Polymer Informatics, and Informational Materials Science Neelesh Ashok, Prabaharan Poornachandran, K.P. Soman and V.G. Sujadevi This paper envisages a detailed review about progress in nano-informatics, polymer-informatics, and material informatics. The incorporation of engineered nanomaterials (ENMs) enable novel materials in which matter can be controlled at nano-range (1 to 100 nm). Numerous areas of human life, research and engineering are influenced by artificial intelligence (AI)-based data driven approaches. Nano reinforcements play a vital role in materials development. Informatics ; Materials informatics ; Nanomaterials ; Nanotechnology ; Polymer ; Materials science ; Computer science ; Data science ; Engineering ; Health informatics ; Engineering informatics ; Medicine Machine Learning in Materials Science
Source: Biointerface Research in Applied Chemistry (article, 2023-12-15)
Field: Chemistry — Organic Chemistry

Design and Biological Evaluation of 4-Iminohydantoin Sulfamides as New Anti-Acinetobacter baumannii Agents With the emergence of multidrug-resistant bacterial strains, there is an urgent need to find antibacterial agents directed at alternative molecular targets. A series of 4-iminohydantoin sulfamides were designed, synthesized, and evaluated as antibacterials. QSAR studies of iminohydantoin's antibacterial activity were made using OCHEM. The predictive ability of the classification models has a balanced accuracy of 83-85%. Validation of the model using a set of external tests showed that the models could be used to predict the activity of newly developed compounds with acceptable accuracy and applicability domain. The models were used to detect a virtual chemical library with the expected activity of compounds against A. baumannii. The five most promising compounds were identified, synthesized, and tested. Characterization of these compounds was done by 1H and 13C NMR techniques. The tested iminohydantoin derivatives showed antibacterial activity against studied antibiotic-resistant clinical isolates of A. baumannii. Among these derivatives, N-(4-Chlorobenzyl)-N'-[(4Z)-5-(dichloromethylene)-2-oxoimidazolidin-4-ylidene]sulfamide (8) was found to be most active with the diameters of bacterial growth inhibition ranging from 18 to 28 mm against all MDR A. baumannii strains. Molecular docking results of bioactive compound 8 show the complex form into the active center of A. baumannii methionine aminopeptidase with a calculated binding affinity of ̶ 9.3 kcal/mol. Acinetobacter baumannii ; Quantitative structure–activity relationship ; Docking (animal) ; Chemistry ; Sulfamide ; Combinatorial chemistry ; Stereochemistry ; Antibacterial activity ; Bacteria ; Biology ; Medicine ; Organic chemistry Synthesis and biological activity ; Chemical Synthesis and Analysis ; Click Chemistry and Applications
Authors: Zhi-Hang Lee | Po Chuan Lin | Tzuhsiung Yang
Source: ChemRxiv (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Inverse Design of Ligands Using A Deep Generative Model Semi-supervised by A Data-driven Ligand Field Strength Metric Transition metal (TM) complexes exhibit diverse structural and electronic properties. The properties of a TM complex can be tuned through modulating the ligand field strength (LFS) inflicted by its ligands. Current quantification of the LFS of a ligand is mainly derived from experimental measurements on a subset of highly symmetrical TM complexes and is limited in ligand scope. Herein, we report a data-driven method to quantify the LFS of ligands assigned from experimental crystal structures of TM complexes. We first show that the experimental metal-ligand bond lengths of over 4000 mononuclear Fe, Co, and Mn complexes form bimodal distributions. Using gaussian fits on the bimodal distributions, each TM complex is assigned with a spin state label. These spin state labels can then be used to calculate the LFS of the ligands of the complexes. Using the obtained data-driven LFS metric, we establish that a semi-supervised deep generative model, junction tree variational autoencoder (JTVAE), can be employed to predict LFS values. Our model exhibits a mean absolute error (MAE) of 0.047 and root mean squared error of 0.072 on the training set. The model also allows the generation of novel ligands with desirable LFS values. Ligand (biochemistry) ; Metric (unit) ; Gaussian ; Inverse ; Algorithm ; Chemistry ; Biological system ; Materials science ; Mathematics ; Computer science ; Physics ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Hang Zhang | Sankaran Sundaresan | Michael A. Webb
Source: ChemRxiv (preprint, 2023-01-30)
Field: Materials Science — Surfaces, Coatings and Films

Molecular Dynamics Investigation of Nanoscale Hydrophobicity of Polymer Surfaces: What Makes Water Wet? The wettability of a polymer surface--related to its hydrophobicity or tendency to repel water--can be crucial for determining its utility, such as for a coating or a purification membrane. While wettability is commonly associated with the macroscopic measurement of a contact angle between surface, water, and air, the molecular physics that underlie these macroscopic observations are not fully known, and anticipating relative behavior of different polymers is challenging. To address this gap in molecular-level understanding, we use molecular dynamics simulations to investigate and contrast interactions of water with six chemically distinct polymers: polytetrafluoroethylene, polyethylene, polyvinyl chloride, poly(methyl methacrylate), Nylon 66, and polyvinyl alcohol. We show that several prospective quantitative metrics for hydrophobicity agree well with experimental contact angles. Moreover, the behavior of water in proximity to these polymer surfaces can be distinguished with analysis of interfacial water dynamics, extent of hydrogen bonding, and molecular orientation--even when macroscopic measures of hydrophobicity are similar. The predominant factor dictating wettability is found to be the extent of hydrogen-bonding between polymer and water, but the precise manifestation of hydrogen-bonding and its impact on surface-water structure varies. In the absence of hydrogen-bonding, other molecular interactions and polymer mechanics control hydrophobic ordering. These results provide new insights into how polymer chemistry specifically impacts water-polymer interactions and translates to surface hydrophobicity. Such factors may facilitate the design or processing of polymer surfaces to achieve targeted wetting behavior, and presented analyses can be useful in studying the interfacial physics of other systems. Wetting ; Polymer ; Contact angle ; Materials science ; Molecular dynamics ; Hydrogen bond ; Chemical physics ; Chemical engineering ; Nanoscopic scale ; Polyvinyl chloride ; Methyl methacrylate ; Nanotechnology ; Polymer chemistry ; Composite material ; Molecule ; Chemistry ; Monomer ; Organic chemistry ; Computational chemistry Surface Modification and Superhydrophobicity ; Phase Equilibria and Thermodynamics
Authors: Jonas Eriksson | Karin Holm
Source: Pharmaceutical Sciences and Drug Design (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Strategies Rooted in Quantum Mechanics for Drug Discovery: Keeping Pace with Novel Hurdles in Pharmaceutical Design The exploration of vastly enlarged chemical spaces, consisting of practical libraries with billions of achievable synthetic compounds, presents thrilling possibilities for drug development while also testing the limits of computational tools in selecting the top prospects. This particularly impacts quantum mechanics (QM) techniques, which offer highly precise chemical insights but are confined to smaller molecular scales. Central to numerous research initiatives is the balance between retaining Pace ; Quantum chemical ; Drug discovery ; Balance (ability) ; Pharmaceutical industry ; Pharmaceutical engineering Synthesis and Reactivity of Heterocycles ; Supramolecular Chemistry and Complexes ; Synthesis and Properties of Aromatic Compounds
Authors: Richard Kelly | Harriet Riley | Helen Saxton | Danny Andrews | Ershad Abubacker | Becky Webb | Kirstine Anderson | Matthew Bown | Laura Cooper | Hannah Fielding | Clare Fitzgerald | Anoushka Handa | Claire Harding | Alan Holder | Charlie Palmer | Rosie Rothwell | Donna Smith | Laura Smith | Jade Holliday | Natalie Ford | Jeanne Andres | Douglas Stephan | Lutz Ackermann | Davide Bonifazi | Connie Lu | Amy Prieto | Sandeep Verma | Meijuan Yin | Bohan Li | Zishuo Yi | Yuchi Zhang | Zhiguo Xia | * | Yan Xu ; Lin | Shitan Liu | Fang Xu | Meng Tang | Wenjing Wu | Chen Yang | * Xu | Xianhong Rui | ; Justus | F Thu mmler | Ramesh Maragani | Franz-Josef Schmitt | Guo Tang | Samira Rahmanlou | Jan Laufer | Henrike Lucas | Karsten Ma | Wolfgang H ; Steven Huband | Marc Walker | Richard Walton | Paulo De Sousa Filho ; Wouter | Niels Peeters | Cristianne ; faure Hauwert | Thomas Beaulieu | Nicholas | L John | Andrew Gardner | Goodwin | L Volker | Deringer | Xiang Wang | Lei Li | Wei Wang | Keju Yu | * Sun | Jingyue Yang | ; Rogovoy | Mariana Rakhmanova | H Evgeniy | Gia Sadykov | Carignan
Source: Chemical Communications (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list Chem. Commun., 2023, 59, 11289 DOI: 10.1039/D3CC90315J 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 ; Computer science ; Attribution ; World Wide Web ; Library science ; Chemistry ; Computer security ; Psychology ; Social psychology Radioactive element chemistry and processing
Authors: Alexis M. Fenton | Fikile R. Brushett
Source: ChemRxiv (preprint, 2023-02-22)
Field: Chemistry — Electrochemistry

Leveraging graphical models to enhance in situ analyte identification via multiple voltammetric techniques Voltammetry is a powerful analytical technique for evaluating electrochemical reactions and holds particular promise for interrogating electrolyte solutions suitable for energy storage technologies, including examining features such as state-of-charge and state-of-health. However, individual voltammetry techniques are likely to be subcomponents of broader analytical workflows that incorporate complementary methods to diagnose evolving electrolyte solutions of uncertain composition. As such, we demonstrate that jointly evaluating electrolyte solutions with distinct voltammetric modes can enhance the capabilities and sensitivities of characterization protocols. Specifically, by considering both macroelectrode cyclic square wave and microelectrode cyclic voltammograms in sequential ("one after another") and simultaneous ("all at once") manners, the composition of an electrolyte solution may be estimated with greater accuracy, and analytes that exhibit near identical electrode potentials may be more readily differentiated. We additionally explore means of further improving this method, finding that protocol accuracy increases when multiple voltammetry techniques are included in the training dataset. We also observe that the algorithm typically becomes more confident--but not necessarily more accurate--when the number of data points increases. Overall, these studies show that the sequential and simultaneous methods may hold utility when evaluating multiple voltammetry datasets that, in turn, may be leveraged to streamline diagnostic workflows used to examine electrolyte solutions within electrochemical technologies. Analyte ; Cyclic voltammetry ; Electrolyte ; Voltammetry ; Computer science ; Workflow ; Electrochemistry ; Supporting electrolyte ; Electrode ; Identification (biology) ; Characterization (materials science) ; Biological system ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Materials science ; Chromatography Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Péter Pál Fehér | Ádám Madarász | András Stirling
Source: ChemRxiv (preprint, 2023-03-08)
Field: Materials Science — Materials Chemistry

Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning The accurate prediction of excited state properties is a key element of rational photocatalyst design. This involves the prediction of ground and excited state redox potentials, for which an accurate description of electronic structures is needed. Even with highly sophisticated computational approaches, however, a number of difficulties arise from the complexity of excited state redox potentials as they require the calculation of the corresponding ground state redox potentials and the estimation of the 0-0 transition energies (E0,0). In this study we have systematically evaluated the performance of DFT methods for these quantities on a set of 37 organic photocatalysts representing nine different chromophore scaffolds. We have found that the ground state redox potentials can be predicted with reasonable accuracy that can be further improved by rationally minimizing the systematic underestimations. The challenging part is to obtain E0,0 as calculating it directly is highly demanding and its accuracy depends strongly on the DFT functional employed. We have found that approximating E0,0 with appropriately scaled vertical absorption energies offers the best compromise between accuracy and computational effort. An even more accurate and cost-effective approach, however, is to predict E0,0 with machine learning and avoid the use of DFT for excited state calculations. Indeed, the best excited state redox potential predictions are achieved with the combination of M062X for ground state redox potentials and ML for E0,0. With this protocol the excited state redox potential windows of the photocatalyst frameworks could be adequately predicted. This shows the potential of combining DFT with ML in the computational design of photocatalysts with preferred photochemical properties. Excited state ; Redox ; Chromophore ; Ground state ; Reduction (mathematics) ; Chemistry ; Computer science ; Computational chemistry ; Biological system ; Materials science ; Atomic physics ; Physics ; Mathematics ; Photochemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrochemical Analysis and Applications
Authors: S. V. Bobyr | Joakim Odqvist
Source: Defect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Statistical Model of Hydrogen Diffusion in BCC Metals The purpose of this work is developing of the statistical model of hydrogen diffusion in the crystal lattice of BCC metals with an estimate of the contribution of quantum effects and deviations from the Arrhenius equation. The values of the statistical model calculations of H diffusion coefficients in Fe, V, Nb and Ta are in good agreement with the experimental data. The statistical model can also explain deviations from the Arrhenius equation at temperatures 300-500 K in Fe and Nb. The downward deviation of the diffusion coefficient at 300K can be explained by the fact that the statistical model does not consider the tunneling effect at temperatures below 300K. It was suggested that thermally activated fast tunnelling transition of hydrogen atoms through the potential barrier at temperatures below 500 K provides an almost free movement of H atoms in the α-Fe and V. Using the statistical model allows for the prediction of the diffusion coefficient for H in BCC metals at intermediate temperatures. Arrhenius equation ; Diffusion ; Quantum tunnelling ; Thermodynamics ; Hydrogen ; Effective diffusion coefficient ; Lattice diffusion coefficient ; Materials science ; Standard deviation ; Lattice (music) ; Statistical model ; Condensed matter physics ; Chemistry ; Activation energy ; Physical chemistry ; Physics ; Statistics ; Mathematics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Md Shafkat Bin Hoque | Eric R. Hoglund | Boyang Zhao | De‐Liang Bao | Hao Zhou | Sandip Thakur | Eric Osei‐Agyemang | Khalid Hattar | Ethan A. Scott | Mythili Surendran | John A. Tomko | John T. Gaskins | Kiumars Aryana | Sara Makarem | Alwen, Adie | Hodge, Andrea | Ganesh Balasubramanian | Ashutosh Giri | Tianli Feng | Jordan A. Hachtel | Jayakanth Ravichandran | Sokrates T. Pantelides | Patrick E. Hopkins
Source: arXiv (Cornell University) (preprint, 2023-12-05)
Field: Materials Science — Materials Chemistry

Ruddlesden-Popper chalcogenides push the limit of mechanical stiffness and glass-like thermal conductivity in single crystals Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded single crystals in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline Ruddlesden-Popper Ban+1ZrnS3n+1, n = 2,3, which are derivatives of BaZrS3. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of mechanically stiff phonon glasses. Thermal conductivity ; Phonon ; Anharmonicity ; Materials science ; Thermal conduction ; Condensed matter physics ; Elastic modulus ; Thermoelectric materials ; Composite material ; Physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Enesi Y. Salawu | Kenneth Shittu | Sunday A. Afolalu | Joseph F. Kayode | Sunday L. Lawal | O. O. Ajayi | Samson O. Ongbali
Source: Nigerian Journal of Technological Development (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

Microstructural evaluations of mild steel and HT250 grey cast iron in ethanol environment The study focused on the response of mild steel and HT250 grey cast iron in varying ethanol concentration. First, a robust review of both materials’ behaviour under different environment was established using microstructural evolutions. Secondly, 30 × 30 × 10 mm dimension of both materials were employed in the study by immersing them in varying ethanol concentrations of BG-43 %, ESG- 42 %, CG-43 %, SQD- 42 % using distilled water as control. The microstructural behaviour showed that every of the sample responded according to the medium of interaction (ethanol). The degree of surface damage also depends on the ethanol concentration and the heavy presence of Fe and oxygen especially in that of grey cast iron. Corrosion cracks and deposits can be attributed to the reaction between the surface and the ethanol. Thus, environment of application is important in material selection. Results suggests that the microstructural changes provide to the material scientist the qualitative corrosion information of these materials in different environment. Conclusively, the study has enlightened the design engineers and material specialist on the need for proper material selection during engineering design. Cast iron ; Materials science ; Distilled water ; Metallurgy ; Material selection ; Corrosion ; Microstructure ; Ethanol ; Composite material ; Chemistry ; Chromatography Corrosion Behavior and Inhibition ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques
Authors: Şenol Şahin | Meltem Dağdelen | Duygu Tunçman | Ömer Erol Uzel | Songül Karaçam | Şefika Arzu Ergen | Tülin Şahin | DİDEM ÇOLPAN ÖKSÜZ
Source: Kocaeli Üniversitesi - AVESİS (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

DOSIMETRIC EVALUATION OF PLA AND ABS MATERIALS PRODUCED BY TWO DIFFERENT PRODUCTION TECHNIQUES Production (economics) ; Process (computing) ; Raw material ; Quality (philosophy) Material Selection and Properties ; Polymer Foaming and Composites ; Recycling and utilization of industrial and municipal waste in materials production