jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alberto Fabrizio | Ksenia R. Briling | David D. Girardier | Clemence Corminboeuf
Source: The Journal of Chemical Physics (article, 2020-11-28)
Field: Materials Science — Materials Chemistry

Learning on-top: Regressing the on-top pair density for real-space visualization of electron correlation The on-top pair density [Πr] is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in space. Being the simplest quantity related to the two-particle density matrix, the on-top pair density is a powerful indicator of electron correlation effects, and as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the complete active space self-consistent field (CASSCF)-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules, bypassing completely the need for ab initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density. Electron density ; Metric (unit) ; Ab initio ; Wave function ; Electronic correlation ; Visualization ; Basis (linear algebra) ; Density functional theory ; Electron Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Kang-ni He | Xiang-Shan Kong | Changsong Liu
Source: Computational Materials Science (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

Robust activation energy predictions of solute diffusion from machine learning method Support vector machine ; Diffusion ; Host (biology) ; Ranking (information retrieval) ; Mean squared error ; Machine learning ; Activation energy ; Materials science ; Algorithm ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Mathematics ; Physical chemistry ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Christian Emeka Okafor | Anthony Chinweuba Onovo | Christopher Chukwutoo Ihueze
Source: SN Applied Sciences (article, 2020-11-21)
Field: Materials Science — Polymers and Plastics

Predictive Energy Requirement Models in Bio-fiber Comminution Process Comminution ; Fiber ; Materials science ; Response surface methodology ; Process optimization ; Specific energy ; Process engineering ; Pulp and paper industry ; Composite material ; Environmental science ; Computer science ; Engineering ; Metallurgy ; Machine learning Natural Fiber Reinforced Composites ; Mineral Processing and Grinding ; Recycling and Waste Management Techniques
Authors: Katharina Keller | Irina Ritsch | Henrik Hintz | Marlina Hülsmann | Mian Qi | Frauke D. Breitgoff | Daniel Klose | Yevhen Polyhach | Maxim Yulikov | Adelheid Godt | Gunnar Jeschke
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accessing distributions of exchange and dipolar couplings in stiff molecular rulers with Cu(<scp>ii</scp>) centres Determining distributed exchange couplings is important for understanding the properties of synthetic magnetic molecules. Such distributions can be determined from pulsed dipolar spectroscopy (PDS) data, but this is challenging due to the similar influence of both exchange and dipolar couplings on such data. In this work we introduce two models that aim to identify these two contributions to the spin-spin couplings from frequency-domain PDS data of shape-persistent molecules having either two Cu(ii) ions, or a Cu(ii) ion and a nitroxide radical as the paramagnetic moieties. The first model assumes correlated Lorentzian or Gaussian exchange and dipole-dipole coupling distributions whose parameters are the model's unknowns. The second model relies on prior knowledge of the distance distribution and by performing Tikhonov regularization along the exchange coupling dimension yields the latter distribution model-free. Both models were able to differentiate between the absence and the presence of exchange interaction, to determine the coupling regime (ferro- or antiferromagnetic) and to estimate the distribution shape. In contrast, calculations within the exchange resilient model of the neural network analysis implemented in DeerAnalysis2018 were not able for our data to identify exchange couplings and return correct distance distributions. However, the generic model was able to identify and separate the strongly curved intermolecular background in the relaxation-induced dipolar modulation enhancement (RIDME) experiments. Our analysis revealed that in such systems exchange coupling may be present up to at least 3.3 nm in π-conjugated systems involving Cu(ii)-PyMTA, while it is negligible for distances r ≥ 4.5 nm between Cu(ii) ions and r ≥ 3.8 nm between a Cu(ii) ion and an unpaired electron of a nitroxide radical. Disruption of the π-conjugation between the ligand of the Cu(ii) complex and the nitroxide leads to negligible exchange coupling at distances r ≥ 2.6 nm in the corresponding [Cu(ii)-TAHA]-nitroxide ruler. Overall, for cases with known distance distributions, the presented analysis techniques allow to determine distributions of exchange couplings from PDS data. Dipole ; Physics ; Crystallography ; Chemistry ; Quantum mechanics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies
Authors: Huaiwei Shi | Teng Zhou
Source: Frontiers of Chemical Science and Engineering (article, 2020-10-06)
Field: Chemistry — Inorganic Chemistry

Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing Abstract Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality. Considering their significant effects, systematic methods for the optimal selection and design of materials are essential. The conventional synthesis-and-test method for materials development is inefficient and costly. Additionally, the performance of the resulting materials is usually limited by the designer’s expertise. During the past few decades, computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes. This article selectively focuses on two important process functional materials, namely heterogeneous catalyst and gas separation agent. Theoretical methods and representative works for computational screening and design of these materials are reviewed. Biochemical engineering ; Process engineering ; Computer science ; Process (computing) ; Process design ; Chemical industry ; Material selection ; Quality (philosophy) ; Functional design ; Product design ; Selection (genetic algorithm) ; Product (mathematics) ; Nanotechnology ; Materials science ; Engineering ; Process integration ; Artificial intelligence Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport ; Catalytic Processes in Materials Science
Authors: Karla Lienau | Carlos A. Triana | L.A. Reith | Sebastian Siol | Greta R. Patzke
Source: Frontiers in Chemistry (article, 2020-06-09)
Field: Materials Science — Materials Chemistry

Microwave-Hydrothermal Tuning of Spinel-Type Co3O4 Water Oxidation Catalysts Water oxidation is the bottleneck reaction for overall water splitting as a direct and promising strategy toward clean fuels. However, the development of robust and affordable heterogeneous water oxidation catalysts remains challenging, especially with respect to the wide parameter space of synthesis and resulting material properties. Oxide catalysts performance in particular has been shown to depend on both synthetic routes and applied catalytic test methods. We here focus on spinel-type Co<sub>3</sub>O<sub>4</sub> as a representative case for an in-depth study of the influence of rather subtle synthetic parameter variations on the catalytic performance. To this end, a series of Co<sub>3</sub>O<sub>4</sub> samples was prepared via time-saving and tunable microwave-hydrothermal synthesis, while systematically varying a single parameter at a time. The resulting spinel-type catalysts were characterized with respect to key materials properties, including crystallinity, oxidation state and surface area using a wide range of analytical methods, such as PXRD, Raman/IR, XAS and XPS spectroscopy. Their water oxidation activity in electrocatalytic and chemical oxidation setups was then compared and correlated with the obtained catalyst properties. Both water oxidation methods displayed related trends concerning favorable synthetic parameters, namely higher activity for lower synthesis temperatures, lower precursor concentrations, addition of hydrogen peroxide and shorter ramping and reaction times, respectively. In addition to the surface area, structural features such as disorder were found to be influential for the water oxidation activity. The results prove that synthetic parameter screening is essential for optimal catalytic performance, given the complexity of the underlying performance-properties relationships. Spinel ; Hydrothermal circulation ; Catalysis ; Chemical engineering ; Chemistry ; Materials science ; Inorganic chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Insung Han | Joseph T. McKeown | Ling Tang | Cai‐Zhuang Wang | Hadi Parsamehr | Zhucong Xi | Ying‐Rui Lu | M. J. Kramer | Ashwin J. Shahani
Source: Physical Review Letters (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

Dynamic Observation of Dendritic Quasicrystal Growth upon Laser-Induced Solid-State Transformation We report the laser-induced solid-state transformation between a periodic "approximant" and quasicrystal in the Al-Cr system during rapid quenching. Dynamic transmission electron microscopy allows us to capture in situ the dendritic growth of the metastable quasicrystals. The formation of dendrites during solid-state transformation is a rare phenomenon, which we attribute to the structural similarity between the two intermetallics. Through ab initio molecular dynamics simulations, we identify the dominant structural motif to be a 13-atom icosahedral cluster transcending the phases of matter. Quasicrystal ; Icosahedral symmetry ; Metastability ; Materials science ; Intermetallic ; Chemical physics ; Molecular dynamics ; Quenching (fluorescence) ; Crystallography ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry ; Optics Quasicrystal Structures and Properties ; Microstructure and mechanical properties ; High-pressure geophysics and materials
Authors: Hao Gao | Junjie Wang | Zhaopeng Guo | Jian Sun
Source: npj Computational Materials (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

Determining dimensionalities and multiplicities of crystal nets Abstract Low-dimensional materials have attracted significant attention over the past decade. To discover new low-dimensional materials, high-throughput screening methods for structures with target dimensionality have been applied in different materials databases. For this purpose, the reliability of dimensionality identification is therefore highly important. In this work, we find that the existence of self-penetrating nets may lead to incorrect results by previous methods. Instead of this, we use the quotient graph to analyse the topologies of structures and compute their dimensionalities. Based on the quotient graph, we can calculate not only the dimensionality but also the multiplicity of self-penetrating structures. As a demonstration, we screened the Crystallography Open Database using the method and find hundreds of structures with different dimensionalities and high multiplicities up to 11. Some of the self-penetrating materials may have application values in gas storage, selective catalysis or photocatalysis because of their high gas sorption capacities and various electronic structures. Crystal (programming language) ; Materials science ; Computer science ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Graph theory and applications
Authors: Shinobu Takizawa
Source: Chemical and Pharmaceutical Bulletin (review, 2020-03-31)
Field: Chemistry — Organic Chemistry

Organocatalytic Synthesis of Highly Functionalized Heterocycles by Enantioselective aza-Morita–Baylis–Hillman-Type Domino Reactions Organocatalytic enantioselective domino reactions are an extremely attractive methodology, as their use enables the construction of complex chiral skeletons from readily available starting materials in two or more steps by a single operation under mild reaction conditions. Thus, these reactions can save both the quantity of chemicals and length of time typically required for the isolation and/or purification of synthetic intermediates. Additionally, no metal contamination of the products occurs, given that organocatalysts include no expensive or toxic metals. The aza-Morita-Baylis-Hillman (aza-MBH) reaction is an atom-economical carbon-carbon bond-forming reaction between α,β-unsaturated carbonyl compounds and imines mediated by Lewis base (LB) catalysts, such as nucleophilic phosphines and amines. aza-MBH products are functionalized chiral β-amino acid derivatives that are highly valuable as pharmaceutical raw materials. Although various enantioselective aza-MBH processes have been investigated, very few studies of aza-MBH-type domino reactions have been reported due to the complexity of the aza-MBH process, which involves a Michael/Mannich/H-transfer/β-elimination sequence. Accordingly, in this review article, our recent efforts in the development of enantioselective domino reactions initiated by MBH processes are described. In the domino reactions, chiral organocatalysts bearing Brønsted acid (BA) and/or LB units impart synergistic activation to substrates, leading to the easy synthesis of highly functionalized heterocycles (some of which have tetrasubstituted and/or quaternary carbon stereocenters) in high yield and enantioselectivity. Enantioselective synthesis ; Domino ; Morita therapy ; Organocatalysis ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Catalysis ; Mathematics ; Pure mathematics Asymmetric Synthesis and Catalysis ; Synthesis of heterocyclic compounds ; Chemical Synthesis and Analysis
Authors: Alejandro Regalado-Méndez | Martín Ruiz | José Antonio Hernández Servín | Reyna Natividad | Rubı́ Romero | Mario E. Cordero | Carlos Estrada-Vázquez | Ever Peralta-Reyes
Source: Processes (article, 2020-12-17)
Field: Chemistry — Electrochemistry

Electrochemical Mineralization of Ibuprofen on BDD Electrodes in an Electrochemical Flow Reactor: Numerical Optimization Approach Statistical analysis was applied to optimize the electrochemical mineralization of ibuprofen with two boron-doped diamond (BDD) electrodes in a continuous electrochemical flow reactor under recirculation batch mode. A central composite rotatable (CCR) experimental design was used to analyze the effect of initial pH (2.95–13.04), current intensity (2.66–4.34 A), and volumetric flow rate (0.16–1.84 L/min) and further optimized by response surface methodology (RSM) to obtain the maximum mineralization efficiency and the minimum specific energy consumption. A 91.6% mineralization efficiency (EM) of ibuprofen with a specific energy consumption (EC) of 4.36 KW h/g TOC within 7 h of treatment was achieved using the optimized operating parameters (pH0 = 12.29, I = 3.26 A, and Q of 1 L/min). Experimental results of RSM were fitted via a third-degree polynomial regression equation having the performance index determination coefficients (R2) of 0.8658 and 0.8468 for the EM and EC, respectively. The reduced root-mean-square error (RMSE) was 0.1038 and 0.1918 for EM and EC, respectively. This indicates an efficient predictive performance to optimize the operating parameters of the electrochemical flow reactor with desirability of 0.9999993. Besides, it was concluded that the optimized conditions allow to achieve a high percentage of ibuprofen mineralization (91.6%) and a cost of 0.002 USD $/L. Therefore, the assessed process is efficient for wastewater remediation.” Mineralization (soil science) ; Electrochemistry ; Response surface methodology ; Electrode ; Central composite design ; Volumetric flow rate ; Ibuprofen ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Thermodynamics ; Nitrogen ; Physics Electrochemical Analysis and Applications ; Advanced oxidation water treatment ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Yaoguang Zhai | Alessandro Caruso | Sicun Gao | Francesco Paesani
Source: ChemRxiv (preprint, 2020-01-23)
Field: Materials Science — Materials Chemistry

Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;The efficient selection of representative configurations that are used in high-level electronic structure calculations needed for the development of many-body molecular models poses a challenge to current data-driven approaches to molecular simulations. Here, we introduce an active learning (AL) framework for generating training sets corresponding to individual many-body contributions to the energy of a N-body system, which are required for the development of MB-nrg potential energy functions (PEFs). Our AL framework is based on uncertainty and error estimation, and uses Gaussian process regression (GPR) to identify the most relevant configurations that are needed for an accurate representation of the energy landscape of the molecular system under exam. Taking the Cs&lt;sup&gt;+&lt;/sup&gt;–water system as a case study, we demonstrate that the application of our AL framework results in significantly smaller training sets than previously used in the development of the original MB-nrg PEF, without loss of accuracy. Considering the computational cost associated with high-level electronic structure calculations for training set configurations, our AL framework is particularly well-suited to the development of many-body PEFs, with chemical and spectroscopic accuracy, for molecular simulations from the gas to condensed phase. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Representation (politics) ; Process (computing) ; Computer science ; Energy (signal processing) ; Function (biology) ; Space (punctuation) ; Potential energy ; Molecular dynamics ; Phase space ; Physics ; Statistical physics ; Biological system ; Artificial intelligence ; Chemistry ; Atomic physics ; Computational chemistry ; Thermodynamics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Vellore Kannan Gopinath | S. Soumya | V. Yogeshwar Chakrapani | T. S. Sampath Kumar
Source: Dental Materials Journal (article, 2020-10-14)
Field: Materials Science — Biomaterials

Odontogenic differentiation of inflamed dental pulp stem cells (IDPSCs) on polycaprolactone (PCL) nanofiber blended with hydroxyapatite The objective of this study was to develop electrospun polycaprolactone (PCL) membranes blended with hydroxyapatite (HA) and evaluate its potential in differentiating inflamed dental pulp stem/progenitor cells (IDPSCs) into odontoblasts. Electrospun nanofibrous membrane consisting of PCL blended with 10 wt% and 15 wt% of HA were fabricated and the characterization was done by Scanning electron microscopy (SEM), Fourier- transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and contact angle analysis. Cytocompatibility, cell adhesion and odontogenic differentiation ability of the membranes were assessed by MTT, Live/Dead, SEM/DAPI and qPCR studies. The mineral deposition ability of the membranes with IDPSCs was estimated by SEM-EDS. The SEM analysis revealed a nanofibrous texture with an average fiber diameter of 140 nm for PCL, 220 nm for PCL10%HA and 250 nm for PCL15%HA. Among the membranes tested, PCL10%HA favored positive cell attachments, upregulated expression of DSPP and ALP gene and higher Ca/P ratio compared to PCL and PCL15%HA. Materials science ; Polycaprolactone ; Nanofiber ; Pulp (tooth) ; Dental pulp stem cells ; Dentistry ; Stem cell ; Biomedical engineering ; Composite material ; Medicine ; Cell biology Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Mesenchymal stem cell research
Authors: Yan-Ge Tian | Zheng-Nan Zhang | Shuangqi Tian
Source: Journal of Analytical Methods in Chemistry (review, 2020-11-20)
Field: Chemistry — Analytical Chemistry

Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data Nondestructive testing with sensor technology is one of the fastest growing and most promising wheat quality information analysis technologies. Nondestructive testing with sensor technology benefits from the latest achievement of many disciplines such as computer, optics, mathematics, chemistry, and chemometrics. It has the advantages of simplicity, speed, low cost, no pollution, and no contact. It is widely used in wheat quality analysis and testing research. This article summarizes nondestructive testing with sensor technology for wheat quality, including the mechanical model, hyperspectral technology, Raman spectroscopy, and near-infrared techniques for wheat mechanical properties, storage properties, and physical and chemical properties (such as moisture, ash, protein, and starch) in the past decade. Based on the current research progress, big data technology needs a lot of research in spectral data mining, modeling algorithm optimization, model robustness, etc. to provide more data support and method reference for the research and application of wheat quality. Hyperspectral imaging ; Chemometrics ; Computer science ; Robustness (evolution) ; Nondestructive testing ; Process engineering ; Quality (philosophy) ; Data quality ; Systems engineering ; Environmental science ; Artificial intelligence ; Engineering ; Machine learning ; Metric (unit) ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Anastasia Yu. Gitlina | Anastasiia Surkova | Maria V. Ivonina | Vladimir V. Sizov | Stanislav K. Petrovskii | Andrey Legin | Галина Л. Старова | Igor O. Koshevoy | Elena V. Grachova | Dmitry Kirsanov
Source: Dyes and Pigments (article, 2020-04-19)
Field: Chemistry — Electrochemistry

Cyclometalated Ir(III) complexes as tuneable multiband light sources for optical multisensor systems: Feasibility study Computer science ; Materials science ; Metal ions in aqueous solution ; Metal Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors