jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M Falahati | Majid Soleimani | Fateme Aflatouni
Source: American Journal of Heterocyclic Chemistry (article, 2023-04-20)
Field: Chemistry — Analytical Chemistry

Separation and Preconcentration of Anionic Dyes Using Magnetic Nanoparticles with Modify Polymer Ionic Liquid In this study, functionalized magnetic nanoparticles of poly (ionic liquid) 1-allyl-3-methylimidazolium chloride were prepared through free radical copolymerization method and tested as a high efficient adsorbent for the removal of anionic dyes in water samples. The physical and chemical structure of prepared magnetic nanoparticles (MNPs) were investigated using Fourier transform infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Next, synthesized magnetic nanoparticles were used in magnetic solid phase extraction (MSPE) method to extract three anionic dyes (Alizarin Red S, Congo Red and Methyl Orange) in water samples. Also, the effect of several parameters such as pH, temperature, contact time and amount of adsorbent was investigated and the optimum values were determined. In addition, the adsorbent has a high ability to remove anionic dyes from water samples containing different dyes. Limit of detection (LOD), limit of quantitation (LOQ), and correlation coefficient were determined for methyl orange, Congo red and alizarin red S dyes under optimum conditions. The synthesized magnetic nanoparticles can be collected by an external magnetic field and regenerated with an alkaline solution (NaOH) and reused. The obtained results showed that the MNPs@PIL method was very efficient and successful in removing dye pollutants in water samples. Congo red ; Methyl orange ; Ionic liquid ; ALIZARIN RED ; Adsorption ; Magnetic nanoparticles ; Detection limit ; Fourier transform infrared spectroscopy ; Nanoparticle ; Nuclear chemistry ; Chemistry ; Ionic strength ; Scanning electron microscope ; Transmission electron microscopy ; Materials science ; Polymer ; Analytical Chemistry (journal) ; Chemical engineering ; Chromatography ; Aqueous solution ; Organic chemistry ; Nanotechnology ; Photocatalysis ; Catalysis Analytical chemistry methods development
Authors: Annabel R. Basford | Steven P. Bennett | Muye Xiao | Lukas Turcani | Jasmine Allen | Kim E. Jelfs | Rebecca L. Greenaway
Source: ChemRxiv (preprint, 2023-11-16)
Field: Chemistry — Organic Chemistry

Streamlining the Automated Discovery of Porous Organic Cages Self-assembly through dynamic covalent chemistry (DCC) can yield a range of multi-component organic assemblies. The reversibility and dynamic nature of DCC has made prediction of reaction outcome particularly difficult and thus slows the discovery rate of new organic materials. In addition, traditional experimental processes are time-consuming and often rely on serendipity. Here, we present a streamlined hybrid workflow that combines automated high-throughput experimentation, automated data analysis, and computational modelling, to accelerate the discovery process of one particular subclass of molecular organic materials, porous organic cages. We demonstrate how the design and implementation of this workflow aids in the identification of organic cages with desirable properties. The curation of a precursor library of 55 tri- and di-topic aldehyde and amine precursors enabled the experimental screening of 366 imine condensation reactions experimentally, and 1464 hypothetical organic cage outcomes to be computationally modelled. From the screen, 225 cages were identified experimentally using mass spectrometry, 54 of which were cleanly formed as a single topology as determined by both turbidity measurements and 1H NMR spectroscopy. Integration of these characterisation methods into a fully automated Python pipeline, named cagey, led to over a 350-fold decrease in the time required for data analysis. This work highlights the advantages of combining automated synthesis, characterisation, and analysis, for large-scale data curation towards an accessible data-driven materials discovery approach. Workflow ; Computer science ; Drug discovery ; Chemistry ; Nanotechnology ; Materials science ; Database Supramolecular Chemistry and Complexes ; Innovative Microfluidic and Catalytic Techniques Innovation ; Covalent Organic Framework Applications
Authors: Javid Abdurahim | Christophe A. Serra | Madeline Vauthier
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2023-07-14)
Field: Materials Science — Materials Chemistry

Elongational-flow elaboration of blend- and Janus-type polymeric nanoparticles Janus particles ; Nanoparticle ; Polymer ; Materials science ; Solvent ; Chemical engineering ; Janus ; Flow (mathematics) ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry ; Mechanics ; Physics Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Surfactants and Colloidal Systems
Authors: Vishnu Jayaprakash | Huazhou Li
Source:  (preprint, 2023-11-13)
Field: Materials Science — Materials Chemistry

Deep-Learning-Based Acceleration of Critical Point Calculations Computation of the critical point of complex fluid mixtures is an important part of thermodynamic phase behaviour computations. While algorithms for these calculations are well established, they are often slow when the number of constituting components is large. In this work, we propose a new procedure to significantly accelerate critical point calculations by leveraging deep neural network (DNN) models. A DNN model for critical point predictions of a given mixture is first trained based on the critical points of such a mixture with various compositions. Such a DNN model is then used to initialize both of the commonly used algorithms for mixture critical point calculations: root finding and global minimization. We demonstrate that when using the DNN-based predictions to initialize the root-finding-based and optimization-based algorithms, we can achieve 50-90% and 80-90% reductions in the number of required iterations, respectively. Critical point (mathematics) ; Computation ; Computer science ; Point (geometry) ; Artificial neural network ; Acceleration ; Algorithm ; Minification ; Root (linguistics) ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Block Copolymer Self-Assembly
Authors: Gerliz M. Gutiérrez-Finol | Silvia Giménez‐Santamarina | Ziqi Hu | Lorena E. Rosaleny | Salvador Cardona‐Serra | Alejandro Gaita‐Ariño
Source: arXiv (Cornell University) (preprint, 2023-01-19)
Field: Materials Science — Materials Chemistry

Lanthanide molecular nanomagnets as probabilistic bits Over the decades, the spin dynamics of a large set of lanthanide complexes have been explored. Lanthanide-based molecular nanomagnets are bistable spin systems, generally conceptualized as classical bits, but many lanthanide complexes have also been presented as candidate quantum bits (qubits). Here we offer a third alternative and model them as probabilistic bits (p-bits), where their stochastic behavior constitutes a computational resource instead of a limitation. We present a modelling tool for molecular spin p-bits, we demonstrate its capability to simulate bulk magnetic relaxation data and ac experiments and to simulate a minimal p-bit network under realistic conditions. Finally, we go back to a recent systematic data gathering and screen the best lanthanide complexes for p-bit behavior, lay out the performance of the different lanthanide ions and chemical families and offer some chemical design considerations. Lanthanide ; Probabilistic logic ; Qubit ; Nanomagnet ; Bistability ; Computer science ; Set (abstract data type) ; Quantum ; Spin (aerodynamics) ; Relaxation (psychology) ; Ion ; Algorithm ; Chemistry ; Nanotechnology ; Materials science ; Theoretical computer science ; Physics ; Quantum mechanics ; Artificial intelligence ; Thermodynamics ; Magnetic field Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Electron Spin Resonance Studies
Authors: Martin Priessner | Richard J. Lewis | Magnus J. Johansson | Jonathan M. Goodman | Jon Paul Janet | Anna Tomberg
Source: Apollo (University of Cambridge) (article, 2023-10-03)
Field: Chemistry — Spectroscopy

HSQC Spectra Simulation and Matching for Molecular Identification <code>data_HMDB.zip</code>: This file comprises curated experimental 2D HSQC NMR data extracted from the HMDB, selected and cleaned to facilitate HSQC spectra comparison. Furthermore, it provides the simulated spectra from the same molecules from ACD, MNova, ML and DFT calculations. <code>data_molformer.zip</code>: This file provides the simulated analogues from the molformer generated molecules (Level 3) and their simulated HSQC NMR spectra calculated with ACD, MNova, ML and DFT. <code>data_regio.zip</code>: This file encompasses data for the regioisomer augmentation task, where regioisomers of selected molecules were manually generated to assess the efficacy of the simulation and matching methodologies in distinguishing between correct and incorrect molecules based on their spectra similarities (Level 2). It contains all the simulated spectra created with ACD, MNova, ML and DFT. <code>data_stereo.zip</code>: This file houses data for the stereoisomer identification task (Level 1), where alternative stereoisomers of selected structures were generated to further challenge and evaluate the simulation and matching methodologies in identifying correct molecules based on HSQC spectra similarities. It contains all the simulated spectra created with ACD, MNova, ML and DFT. <code>data_sarotti.zip</code>: This file contains a curated dataset by Sarotti et al., comprising mischaracterized molecules from published works. It's used in the final evaluation to test the molecule distinction methodology on real misassigned molecular spectra, assessing the accuracy of the different simulation techniques in correcting structure mischaracterizations. It contains all the simulated spectra created with ACD, MNova, ML and DFT. Heteronuclear single quantum coherence spectroscopy ; Identification (biology) ; Matching (statistics) ; Computer science ; Mathematics ; Physics ; Nuclear magnetic resonance ; Statistics ; Biology ; Two-dimensional nuclear magnetic resonance spectroscopy Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: George A. Marchant | A. Miguel | Bora Karasulu | Livia Bartók-Pártay
Source: Research Square (preprint, 2023-02-14)
Field: Materials Science — Materials Chemistry

Exploring the configuration space ofelemental carbon with empirical and machine learned interatomicpotentials Abstract In the present work we detail how the many-body potential energy landscape of elemental carbon, as described by interatomic potentials, can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram up to high pressures. We present a comparison of four interatomic potential models: Tersoff, EDIP, GAP-20 and its recently updated version, GAP-20U. Our evaluation is focused on their macroscopic properties, particularly on their melting transition and on identifying thermodynamically stable solid structures up to at least 100 GPa. The studied models all form graphite structures upon freezing at lower pressure, then the diamond structure as the pressure increases. In the cases of the Tersoff, EDIP and GAP-20U potentials we were able to locate the transition between these phases. We placed particular focus on the state-of-the-art machine-learned models, the GAP-20 and GAP-20U, and calculated their phase diagrams up to 1 and 0.1 TPa, respectively, to evaluate their predictive capabilities well outside of the models’ fitting range. The phase diagrams of the GAP models showed remarkably good agreement with the experimental phase diagram up to 200 GPa. However, we found that the accuracy of the models’ descriptions of graphite was adversely affected by the relative stability of phases with incorrect layer spacing. By adding a suitable selection of graphite structures to the GAP training set and re-training the model, we have derived an improved model — referred to as the GAP-20U+gr — that suppresses erroneous local minima in the graphitic energy landscape. At extreme high pressure nested sampling identified two novel stable solid structures in the GAP-20 model, a strained diamond structure above 270 GPa and a strained hexagonal-close-packed structure above 890 GPa. However, the stability of these two phases were not confirmed by DFT calculations, highlighting potential routes to further improve the GAP model. Space (punctuation) ; Carbon fibers ; Computer science ; Algorithm Boron and Carbon Nanomaterials Research ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Bjarne Kreitz | Patrick Lott | Andrew J. Medford | Felix Studt | Olaf Deutschmann | C. Franklin Goldsmith
Source: ChemRxiv (preprint, 2023-05-07)
Field: Materials Science — Materials Chemistry

Automated Generation of Microkinetics forHeterogeneously Catalyzed ReactionsConsidering Correlated Uncertainties The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. Two thousand unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the conversion of exhaust gas emissions from stoichiometric gasoline combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems. Metadynamics ; Combustion ; Mechanism (biology) ; Chemistry ; Gasoline ; Ab initio ; Density functional theory ; Reaction mechanism ; Computer science ; Catalysis ; Biochemical engineering ; Biological system ; Computational chemistry ; Thermodynamics ; Molecular dynamics ; Physics ; Physical chemistry ; Engineering Catalytic Processes in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Catalysis and Oxidation Reactions
Authors: Jonni Firdaus | Usman Ahmad | I Wayan Budiastra | I Dewa Made Subrata
Source: Advances in Technology Innovation (article, 2023-12-29)
Field: Chemistry — Analytical Chemistry

Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil. Partial least squares regression ; Linear regression ; Principal component regression ; Principal component analysis ; Coefficient of determination ; Phosphorus ; Soil test ; Potassium ; Nitrogen ; Mathematics ; Regression analysis ; Simple linear regression ; Environmental science ; Near-infrared spectroscopy ; Soil science ; Chemistry ; Statistics ; Soil water ; Biology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Geochemistry and Geologic Mapping
Authors: Zihan Pengmei | Junyu Liu | Yinan Shu
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Beyond MD17: the reactive xxMD dataset System specific neural force fields (NFFs) have gained popularity in computational chemistry. One of the most popular datasets as a bencharmk to develop NFFs models is the MD17 dataset and its subsequent extension. These datasets comprise geometries from the equilibrium region of the ground electronic state potential energy surface, sampled from direct adiabatic dynamics. However, many chemical reactions involve significant molecular geometrical deformations, for example, bond breaking. Therefore, MD17 is inadequate to represent a chemical reaction. To address this limitation in MD17, we introduce a new dataset, called Extended Excited-state Molecular Dynamics (xxMD) dataset. The xxMD dataset involves geometries sampled from direct non-adiabatic dynamics, and the energies are computed at both multireference wavefunction theory and density functional theory. We show that the xxMD dataset involves diverse geometries which represent chemical reactions. Assessment of NFF models on xxMD dataset reveals significantly higher predictive errors than those reported for MD17 and its variants. This work underscores the challenges faced in crafting a generalizable NFF model with extrapolation capability. Extrapolation ; Adiabatic process ; Wave function ; Excited state ; Statistical physics ; Molecular dynamics ; Benchmarking ; Computer science ; Chemical Dynamics ; Work (physics) ; Potential energy ; Potential energy surface ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Classical mechanics ; Quantum mechanics ; Molecule ; Chemical physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Luis Ruiz Pestana | Meili Liu
Source: Research Square (preprint, 2023-10-11)
Field: Materials Science — Ceramics and Composites

Mechanisms and Energetics of Calcium Aluminosilicate Glass Dissolution through Ab initio Molecular Dynamics-Metadynamics Simulations Abstract The dissolution of silicate glasses has implications in diverse fields ranging from the immobilization of radioactive waste to the development of sustainable alternatives to Portland cement. Here, we used ab initio molecular dynamics simulations coupled with well-tempered metadynamics to study Si-O-T bridge dissociation in calcium aluminosilicate glasses, crucial for understanding their dissolution processes. In a departure from the conventional Michalske-Freiman model, our findings reveal a nucleophilic substitution reaction mechanism characterized by a short-lived, 5-fold coordinated Si intermediate state with a near-trigonal bipyramidal geometry. We find that the reorganization energy introduces activation energy barriers that depend on the Si bridge coordination, with Si Q3 species serving as the rate-limiting step in the dissolution reaction. Our findings not only challenge long-standing theoretical models but also pave the way for more accurate and comprehensive frameworks for understanding the dissolution of silicate glasses in various applications. Metadynamics ; Dissolution ; Molecular dynamics ; Ab initio ; Aluminosilicate ; Chemical physics ; Calcium aluminosilicate ; Computational chemistry ; Materials science ; Chemistry ; Physical chemistry ; Catalysis ; Organic chemistry Glass properties and applications ; Concrete and Cement Materials Research ; Clay minerals and soil interactions
Authors: Akwasi Nana Prempeh Ansah-Antwi | Pedro H. Amorim Valença | Mohsen Farshad | Jonathan K. Whitmer
Source: ChemRxiv (preprint, 2023-12-06)
Field: Chemistry — Physical and Theoretical Chemistry

Non-equilibrium Trajectory Sampling (NETS) method for generating free-energy landscapes and steady-state distributions This study presents a novel method for constructing free-energy profiles and steady-state distributions from either equilibrium or non-equilibrium trajectories along a defined reaction coordinate. The method works by tracking the final states of a swarm of short simulations launched from different initial conditions with no prior knowledge of the free energy landscape. Subsequently, this trajectory information is used to build a transition matrix whose primary eigenvector captures the steady-state occupation probability for each value of the reaction coordinate, yielding the free energy profile in equilibrium. This innovative method holds potential for many new materials and engineering applications where it is desired to know the free energy of rate-limiting configurations as may be relevant for transport processes (in, e.g. battery electrolytes and nano-filtration membranes), complexation (in, e.g., self-assembly and ligand-binding interactions) or in tuning properties such as adsorption. We illustrate the effectiveness of the method by capturing the free energy associated with a one-dimensional barrier potential modeling a separation membrane, and the particle distribution associated with thermophoresis under a temperature gradient. Further extensions and applications of the method are also discussed. Thermophoresis ; Trajectory ; Energy landscape ; Energy (signal processing) ; Statistical physics ; Steady state (chemistry) ; Metadynamics ; Computer science ; Chemistry ; Thermodynamics ; Physics ; Mathematics ; Molecular dynamics ; Computational chemistry ; Physical chemistry ; Statistics Electrostatics and Colloid Interactions ; Fuel Cells and Related Materials ; Membrane-based Ion Separation Techniques