jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junkil Park | Aseem Partap Singh Gill | Seyed Mohamad Moosavi | Jihan Kim
Source: ChemRxiv (preprint, 2023-07-18)
Field: Materials Science — Materials Chemistry

Inverse Design of Porous Materials: A Diffusion Model Approach The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E, Midjourney and Stable Diffusion, which are tools used for text-to-image generations. Mapping this workflow onto materials discovery, a new diffusion model was developed for the generation of pure silica zeolite, marking it the first application of diffusion models to porous materials. Our model demonstrates the ability to generate novel crystalline porous materials that are not present in the training dataset, while exhibiting exceptional performance in inverse design tasks targeted on various chemical properties including the void fraction, Henry coefficient and heat of adsorption. Comparing our model with a Generative Adversarial Network (GAN) revealed that the diffusion model outperforms the GAN in terms of structure validity, exhibiting an over 2,000-fold improvement in performance. We firmly believe that diffusion models (along with other deep generative models) hold immense potential in revolutionizing the design of new materials, and anticipate the wide extension of our model to other classes of porous materials. Workflow ; Diffusion ; Generative grammar ; Porous medium ; Inverse ; Porosity ; Computer science ; Generative model ; Materials science ; Artificial intelligence ; Mathematics ; Geometry ; Physics ; Thermodynamics ; Database Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Valeriy Kalytka | Ali Mekhtiyev | Yelena Neshina | А. D. Аlkina | Raushan Aimagambetova | Gabit Mukhambetov | Aleksandr Bashirov | D. A. Afanasyev | Arkadiy Bilichenko | Dinara Zhumagulova | Zukhra Ismailova | Yelena Senina
Source: Crystals (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Physical and Mathematical Models of Quantum Dielectric Relaxation in Electrical and Optoelectric Elements Based on Hydrogen-Bonded Crystals The quantum statistical properties of the proton subsystem in hydrogen-bonded crystals (HBC) are investigated. Based on the non-stationary Liouville operator equation (taking into account a number of assumptions established in the experiment), a quantum kinetic equation is constructed for the ensemble of non-interacting protons (an ideal proton gas) moving in the crystal potential image perturbed by the external electric field. The balanced density matrix for the unperturbed proton subsystem is constructed using the quantum canonical Gibbs distribution, and the non-balanced density matrix is calculated from the solutions of the nonlinear quantum kinetic equation by methods in linear approximation of perturbation theory for the blocking electrode model. Full quantum mechanical averaging of the polarization operator makes it possible to study the theoretical frequency-temperature spectra of the complex dielectric permittivity (CDP) calculated using quantum relaxation parameters that differ significantly from their semiclassical counterparts. A scheme is presented for an analytical study of the dielectric loss tangent in the region of quantum nonlinear relaxation in HBC. The results obtained in the given paper are of scientific interest in developing the theoretical foundations of proton conduction processes in energy-independent memory elements (with anomalously high residual polarization) based on thin films of ferroelectric materials in the ultralow temperature range (1–10 K). The theoretical results obtained have a direct application to the study of the tunneling mechanisms of spontaneous polarization in ferroelectric HBC with a rectangular hysteresis loop, in particular in crystals of potassium dideutrophosphate (KDP), widely used in nonlinear optics and laser technology. The quantum properties of proton relaxation in HBC can be applied in the future to the study of solid-state electrolytes with high proton conductivity for hydrogen energy, capacitor technology (superionics, varicodes), and elements of MIS and MSM structures in the development of resonant tunnel diodes for microelectronics and computer technology. Density matrix ; Dielectric ; Quantum ; Quantum tunnelling ; Condensed matter physics ; Ferroelectricity ; Quantum mechanics ; Physics ; Chemistry Solid-state spectroscopy and crystallography ; Optical and Acousto-Optic Technologies ; Geophysics and Sensor Technology
Authors: Maximilian J. Werny | Florian Meirer | Bert M. Weckhuysen
Source: Angewandte Chemie (article, 2023-10-02)
Field: Materials Science — Surfaces, Coatings and Films

Visualizing the Structure, Composition and Activity of Single Catalyst Particles for Olefin Polymerization and Polyolefin Decomposition Abstract The structural and morphological characterization of individual catalyst particles for olefin polymerization, as well as for the reverse process of polyolefin decomposition, can provide an improved understanding for how these catalyst materials operate under relevant reaction conditions. In this review, we discuss an emerging analytical toolbox of 2D and 3D chemical imaging techniques that is suitable for investigating the chemistry and reactivity of related catalyst systems. While synchrotron‐based X‐ray microscopy still provides unparalleled spatial resolutions in 2D and 3D, a number of laboratory‐based techniques, most notably focused ion beam‐scanning electron microscopy, confocal fluorescence microscopy, infrared photoinduced force microscopy and laboratory‐based X‐ray nano‐computed tomography, have helped to significantly expand the arsenal of analytical tools available to scientists in heterogeneous catalysis and polymer science. In terms of future research, the review outlines the role and impact of in situ and operando (spectro−)microscopy experiments, involving sophisticated reactors as well as online reactant and product analysis, to obtain real‐time information on the formation, decomposition, and mobility of polymer phases within single catalyst particles. Furthermore, the potential of fluorescence microscopy, X‐ray microscopy and optical microscopy is highlighted for the high‐throughput characterization of olefin polymerization and polyolefin decomposition catalysts. By combining these chemical imaging techniques with, for example, chemical staining methodologies, selective probe molecules as well as particle sorting approaches, representative structure–activity relationships can be derived at the level of single catalyst particles. Polyolefin ; Characterization (materials science) ; Microscopy ; Polymerization ; Materials science ; Polymer ; Decomposition ; Olefin fiber ; Nanotechnology ; Chemical imaging ; Catalysis ; Chemical engineering ; Chemistry ; Organic chemistry ; Computer science ; Optics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Fikrul Akbar Alamsyah | Chi‐Cheng Cheng | Femiana Gapsari
Source: Applied Sciences (article, 2023-12-28)
Field: Materials Science — Materials Chemistry

Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning This study analyzes the corrosion inhibition efficiency of sodium molybdate (SM) solution on weldment specimens in 1 M HCl, based on H2 bubbles detection. The detection of the bubbles produced by the corrosion process is achieved by a YOLOv4 deep learning algorithm. The results indicate that the corrosion rate is higher on the weld metal zone than on the base metal zone in the same solution, which might be attributed to the coarser grain of the weld metal zone and the stability of the produced oxide layer. The addition of sodium molybdate was discovered to boost the stability of the oxide layer, hence enhancing the specimens’ corrosion resistance. The overall inhibitory efficiency of the sodium molybdate solution was 59% for the weld metal (WM) (0.4 g/L), 52% for the heat-affected zone (HAZ) (0.2 g/L), and 37% for the base metal (BM) (0.2 g/L). The object detection algorithm models showed 97% for the mAP and 0.98 for recall. The minimum average bubble detected for the WM was 0.353 /mm2 at an SM concentration of 0.4 g/L, while the HAZ was 0.612 /mm2 at 0.2 g/L, and the BM was 1.055 /mm2 at 0.2 g/L. The results of the bubbles detection appeared to be consistent with the corrosion experiment outcomes obtained by the potentiodynamic polarization and hydrogen volume measurement tests. This experiment validates the distinctiveness of the different weld zones in terms of the inhibitor concentration required for successful corrosion prevention, as well as the potential of analyzing corrosion using machine learning algorithms for object detection. Corrosion ; Materials science ; Oxide ; Metallurgy ; Metal ; Base metal ; Welding Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Ivo A. W. Filot | Michel P. C. van Etten | D. W. J. G. Trommelen | Emiel J. M. Hensen
Source: The Journal of Open Source Software (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Bramble: adaptive common neighbor analysis (CNA) forthe recognition of surface topologies in nanoparticles Filot et al., (2023). Bramble: adaptive common neighbor analysis (CNA) for the recognition of surface topologies in nanoparticles. Journal of Open Source Software, 8(89), 5710, https://doi.org/10.21105/joss.05710 Network topology ; Open source ; Nanoparticle ; Open source software ; Computer science ; k-nearest neighbors algorithm ; Software ; Topology (electrical circuits) ; Nanotechnology ; Artificial intelligence ; Materials science ; Engineering ; Computer network ; Operating system ; Electrical engineering Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Dominik Jankovič | Mateja Mihelač | Žan Testen | Blaž Likozar | Matej Huš | Martin Gazvoda
Source: Journal of Catalysis (article, 2023-12-21)
Field: Chemistry — Inorganic Chemistry

Mechanistically guided development of homogenous nickel catalysis through rapid computational catalyst screening Advances in understanding the mechanisms of homogeneous nickel catalysis has led to a remarkable progress in this field. However, the discovery of active catalysts is still largely based on trial and error, and the catalysts are mostly based on chelating (nitrogen) ligands. We report a combined theoretical and experimental study of nickel-catalyzed hydroarylation of vinylarenes, where experimental advances were complemented with a rapid computation catalyst screening. Once the mechanism is known and preliminary experimental data is available, the screening approach can predict other suitable ligands by assessing the electronic structure of the catalyst complex without the computationally intensive determination of the transition states. Using the approach described, we developed the reaction that proceeds under unusually mild conditions and is broadly applicable on a range of vinylarenes and (het)aryl halides, including previously inaccessible chlorides. The reaction was found to be highly dependent on the choice of ligand, with monodentate PPh3 proving optimal. Extensive quantum chemical simulations elucidated the reaction mechanism and identified Ni(PPh3)xClyH as the crucial active species. Catalysis ; Chemistry ; Nickel ; Halide ; Aryl ; Combinatorial chemistry ; Denticity ; Homogeneous ; Ligand (biochemistry) ; Homogeneous catalysis ; Quantum chemical ; Reaction mechanism ; Computational chemistry ; Organic chemistry ; Molecule ; Metal ; Thermodynamics Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods ; Catalytic Alkyne Reactions
Authors: Rehab O. El‐Attar | Reda M. Abdelhameed | Elmorsy Khaled
Source: Arabian Journal for Science and Engineering (article, 2023-10-16)
Field: Chemistry — Electrochemistry

Synthesis and Applications of Crown Ether-Linked Metal–Organic Framework Composite-Based Sensors for Stripping Voltammetric Determination of Lead Abstract The present work demonstrates the construction and electrochemical characterization of novel disposable screen-printed carbon sensors integrated with functionalized MIL-53-NH 2 metal–organic framework cross-linked with crown ethers and calixarene macrocyclic compounds for sensitive differential pulse voltammetric determination of lead ions in tap, surface water and biological fluid samples. The electroanalytical parameters were optimized regarding the nature of the electrode modifier, supporting electrolyte, the working pH value, scan rate, deposition potential, deposition time, reproducibility of measurement and the operational lifetime. Working electrodes dropcasted with MIL-53-NH 2 -dibenzo-24-crown-8-ether nanocomposite (MOFCE24) showed the proper performance within the lead concentration ranging from 13.75 to 217.83 ppb with LOD and LOQ values of 3.18 and 9.62 ppb, respectively. The synergistic effect of the metal–organic frameworks as transducer and dibenzo 24-crown-8-ether as sensing elements accelerates the electron transfer process at the electrode surface and improves the sensor selectivity through complexation of the lead ions with the crown ether moiety. The fabricated sensors showed high measuring reproducibility with long operational life time (60 days), which can be attributed to the formation of cross-linked ionophore/metal–organic framework with limited leaching of the sensing element in the measuring solution. The introduced sensors were utilized for the onsite voltammetric determination of lead in environmental and biological samples with acceptable average recoveries comparable with the graphite furnace atomic absorption spectrometric method. Crown ether ; Ionophore ; Electrochemistry ; Metal ions in aqueous solution ; Electrode ; Chemistry ; Selectivity ; Working electrode ; Potentiometric titration ; Supporting electrolyte ; Electrochemical gas sensor ; Ether ; Electrolyte ; Inorganic chemistry ; Metal ; Materials science ; Ion ; Organic chemistry ; Catalysis Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Mengjun Wang | Linzhe Lü | Qiaorong Jiang | Guang Li | Qi‐Ming Hong | Mingmin Wang | Yong Xu | Xiaoqing Huang
Source: Science China Materials (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Kinetics-induced orientational morphological evolution of Pd-Sb rhombohedra from regular nanocrystals to distorted ones Nanocrystal ; Materials science ; Kinetics ; Morphology (biology) ; Nanoscopic scale ; Nanostructure ; Nanotechnology ; Crystallography ; Chemical engineering ; Chemical physics ; Chemistry Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Owain T. Beynon | Alun Owens | Christian Carbogno | Andrew J. Logsdail
Source: The Journal of Physical Chemistry C (article, 2023-08-07)
Field: Chemistry — Inorganic Chemistry

Evaluating the Role of Anharmonic Vibrations in Zeolite β Materials The characterization of zeolitic materials is often facilitated by spectroscopic analysis of vibrations, which informs about the bonding character of the substrate and any adsorbents. Computational simulations aid the interpretation of the spectra but often ignore anharmonic effects that can affect the spectral characteristics significantly. Here, the impact of anharmonicity is demonstrated with a combination of dynamical and static simulations applied to the structures formed during the synthesis of Sn-BEA <i>via</i> solid-state incorporation (SSI): the initial siliceous BEA (Si-β), aluminosilicate BEA (H-β), dealuminated BEA (deAl-β), and Sn-BEA (Sn-β). Heteroatom and defect-containing BEA are shown to have strong anharmonic vibrational contributions, with atomic and elemental resolution highlighting particularly the prevalence for H atoms (H-β, deAl-β) as well as localization to heteroatoms at defect sites. We simulate the vibrational spectra of BEA accounting for anharmonic contributions and observe an improved agreement with experimental data compared to harmonic methods, particularly at wavenumbers below 1500 cm<sup>-1</sup>. The results demonstrate the importance of incorporating anharmonic effects in simulations of vibrational spectra, with consequences toward future characterization and application of zeolitic materials. Anharmonicity ; Heteroatom ; Zeolite ; Spectral line ; Characterization (materials science) ; Materials science ; Vibration ; Computational chemistry ; Molecular vibration ; Chemical physics ; Chemistry ; Molecular physics ; Physical chemistry ; Nanotechnology ; Ring (chemistry) ; Organic chemistry ; Condensed matter physics ; Molecule ; Physics ; Catalysis ; Quantum mechanics Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Mandi Fang | Shi Tang | Zheyong Fan | Shi Yao | Nan Xu | Yi He
Source: arXiv (Cornell University) (preprint, 2023-01-31)
Field: Materials Science — Materials Chemistry

A study of simulating Raman spectra for alkanes with a machine learning-based polarizability model Polarizability is closely related to many fundamental characteristics of molecular systems and plays an indispensable role in simulating the Raman spectra. However, the calculations of polarizability for large systems still suffers from the limitations of processing ability of the quantum mechanical (QM) methods. This work assessed and compared the accuracy of the bond polarizability model (BPM) and a ML-based atomic polarizability model (AlphaML) in predicting polarizability of alkanes and then also investigated the ability of simulating Raman spectra. We found that the AlphaML has appreciable advantages over the BPM in learning the polarizability in the training data set and predicting polarizability of molecules that configurational differently from training structures. In addition, the BPM has inherent disadvantages in predicting polarizability anisotropy due to many factors including large uncertainties of estimating bond anisotropy, omitting of off-diagonal parameters in the construction of the model. As a result, the BPM has larger errors than the AlphaML in the simulation of anisotropic Raman scattering. Finally, we demonstrated that both the BPM and AlphaML suffer from transference to alkanes larger than those used in the training data sets, but the problem for the AlphaML can be circumvented by exploring more proper training structures. Polarizability ; Anisotropy ; Raman spectroscopy ; Raman scattering ; Spectral line ; Chemistry ; Molecular physics ; Molecule ; Statistical physics ; Chemical physics ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy and Chemometric Analyses
Authors: Daniel de las Heras | Toni Zimmermann | Florian Sammüller | Sophie Hermann | Matthias Schmidt
Source: arXiv (Cornell University) (preprint, 2023-01-28)
Field: Materials Science — Materials Chemistry

Perspective: How to overcome dynamical density functional theory We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur in soft matter. To give guidance for navigating the theoretical and practical challenges that lie ahead, we discuss and exemplify the limitations of dynamical density functional theory. Instead of the implied adiabatic sequence of equilibrium states that this approach provides as a makeshift for the true time evolution, we posit that the pending theoretical tasks lie in developing a systematic understanding of the dynamical functional relationships that govern the genuine nonequilibrium physics. While static density functional theory gives a comprehensive account of the equilibrium properties of many-body systems, we argue that power functional theory is the only present contender to shed similar insights into nonequilibrium dynamics, including the recognition and implementation of exact sum rules that result from the Noether theorem. As~a~demonstration of the power functional point of view, we consider an idealized steady sedimentation flow of the three-dimensional Lennard-Jones fluid and machine-learn the kinematic map from the mean motion to the internal force field. The trained model is capable of both predicting and designing the steady state dynamics universally for various target density modulations. This demonstrates the significant potential of using such techniques in nonequilibrium many-body physics and overcomes both the conceptual constraints of dynamical density functional theory as well as the limited availability of its analytical functional approximations. Statistical physics ; Non-equilibrium thermodynamics ; Dynamical systems theory ; Physics ; Active matter ; Density functional theory ; Adiabatic process ; Computer science ; Classical mechanics ; Theoretical physics ; Quantum mechanics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Thermodynamics and Statistical Mechanics
Authors: Tanja Gulder | Pierre Zeides | Kathrin Bellmann‐Sickert | Ru Zhang | Catharina Julia Seel | Viktoria Most | Clara T. Schoeder | M. Groll
Source: ChemRxiv (preprint, 2023-12-26)
Field: Chemistry — Inorganic Chemistry

Unraveling the Molecular Basis of Substrate Specificity and Halogen Activation in Vanadium-Dependent Haloperoxidases Vanadium-dependent haloperoxidases (VHPOs) are biotechnologically valuable and operationally versatile biocatalysts that do not require complex electron shuttling systems. These enzymes share remarkable active-site structural similarities yet display broadly variable reactivity and selectivity. The factors dictating substrate and halogen specificity and, thus, a general understanding of VHPO reaction control still need to be discovered. This work's strategic single-point mutation in the cyanobacterial bromoperoxidase AmVHPO facilitates a selectivity switch to allow aryl chlorination. This mutation induces loop formation absent in the wild-type enzyme, and that interacts with the neighboring protein monomer, creating a tunnel to the active sites. Structural analysis of the substrate-R425S-mutant complex reveals a substrate-binding site at the interface of two adjacent units. There, residues Glu139 and Phe401 interact with arenes, extending the substrate residence time close to the vanadate cofactor and stabilizing intermediates. Our findings validate the long-debated existence of direct substrate binding and provide detailed VHPO mechanistic understanding. This work will thus pave the way for a broader application of VHPOs in diverse chemical processes. Chemistry ; Active site ; Substrate (aquarium) ; Cofactor ; Vanadium ; Selectivity ; Mutant ; Reactivity (psychology) ; Combinatorial chemistry ; Enzyme ; Stereochemistry ; Biochemistry ; Catalysis ; Biology ; Organic chemistry ; Gene Vanadium and Halogenation Chemistry ; Metal-Catalyzed Oxygenation Mechanisms ; Oxidative Organic Chemistry Reactions