jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mario G. Zauchner | Stefano Dal Forno | Gábor Cśanyi | Andrew P. Horsfield | Johannes Lischner
Source: Machine Learning Science and Technology (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Predicting polarizabilities of silicon clusters using local chemical environments Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies. Cluster (spacecraft) ; Silicon ; Scaling ; Limit (mathematics) ; Construct (python library) ; Computer science ; Statistical physics ; Coupled cluster ; Chemical physics ; Machine learning ; Molecular physics ; Computational science ; Materials science ; Physics ; Molecule ; Mathematics ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Nicolai Ree | Mads Koerstz | Kurt V. Mikkelsen | Jan H. Jensen
Source: ChemRxiv (preprint, 2021-07-05)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm We present a computational methodology for the screening of a chemical space of 10²⁵ substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34,000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg. Norbornadiene ; Kinetic energy ; Molecule ; Thermal ; Absorption spectroscopy ; Absorption (acoustics) ; Genetic algorithm ; Materials science ; Chemical physics ; Biological system ; Chemistry ; Physics ; Optics ; Mathematics ; Thermodynamics ; Quantum mechanics ; Mathematical optimization ; Organic chemistry Photochemistry and Electron Transfer Studies ; Perovskite Materials and Applications ; Free Radicals and Antioxidants
Authors: Daniel S. King | Laura Gagliardi | Donald G. Truhlar
Source: ChemRxiv (preprint, 2021-06-21)
Field: Materials Science — Materials Chemistry

Machine-Learned Functionals for Strongly Correlated Systems We introduce multiconfiguration data-driven functional theory (MC-DDFT) as a new approach to multiconfiguration nonclassical functional theory (MC-NCFT), in which the classical energy of a multiconfigurational wave function is combined with a machine-learned functional for the nonclassical exchange-correlation energy. We also present results obtained by a related approach, multiconfiguration energy-correcting functional theory (MC-ECFT), in which the total energy of a wave function method (e.g. CASSCF or NEVPT2) is corrected with a machine-learned functional. On a dataset of carbene singlet-triplet energy splittings, we demonstrate that these new multiconfiguration data-driven functional methods (MC-DDFMs) are able to achieve near-benchmark performance on systems not used for training while being less active-space dependent than multiconfiguration pair-density functional theory using currently available translated functionals. Density functional theory ; Wave function ; Singlet state ; Orbital-free density functional theory ; Hybrid functional ; Energy (signal processing) ; Function (biology) ; Energy functional ; Space (punctuation) ; Benchmark (surveying) ; Time-dependent density functional theory ; Physics ; Statistical physics ; Computer science ; Quantum mechanics ; Excited state Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Takashi Kojima | Takashi Washio | Satoshi Hara | Masakata Koishi
Source: Research Square (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Search Strategy for Rare Microstructure to Optimize Material Properties of Filled Rubber Abstract A shortcut to understand the microstructure-property relationship is sampling and analysis of microstructures that induce the desired material property. In the case of filled rubber, the simulation of complex filler morphology involves hundreds of filler particles. This requires a large amount of iterative sampling, because the number of parameters is when using coordinates of the n particles as the search objective. Furthermore, the morphology that induces the desired property, e.g. extremely high modulus, only occurs rarely. In this paper, we propose an effective three-step search method for the filler morphology. In the first step, the replica exchange Markov chain Monte Carlo (MCMC) was employed to discretely search among a wide range of morphologies. In this step, we reduced the filler morphology space in sampling by introducing distributed filler candidate points and spin function. In the second step, the gradient descent method was applied to search for the desired morphology locally in the high-dimensional space , starting from the morphologies obtained by the replica exchange MCMC. Lastly, the coarse-grained molecular dynamics (CGMD) simulations were performed to validate the morphologies actually show the desired properties, because the surrogate model of CGMD was employed in the first 2 steps for the efficient search. Using the proposed method, we demonstrate the search for morphologies that induce high elastic modulus. Replica ; Natural rubber ; Materials science ; Markov chain Monte Carlo ; Microstructure ; Monte Carlo method ; Sampling (signal processing) ; Molecular dynamics ; Composite material ; Biological system ; Statistical physics ; Computer science ; Mathematics ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Rheology and Fluid Dynamics Studies
Authors: Wei-Tse Hsu | Dominique Ramirez | Tarek Sammakia | Zhongping Tan | Michael R. Shirts
Source: ChemRxiv (preprint, 2021-10-31)
Field: Chemistry — Organic Chemistry

Identifying signatures of proteolyticstability and monomeric propensity inO-glycosylated insulin using molecularsimulation Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms. Insulin ; Proteolytic enzymes ; Glycan ; Glycosylation ; Chemistry ; Biochemistry ; Glycoprotein ; Computational biology ; Biology ; Enzyme ; Endocrinology Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Protein Structure and Dynamics
Authors: Wei-Tse Hsu | Dominique Ramirez | Tarek Sammakia | Zhongping Tan | Michael R. Shirts
Source:  (preprint, 2021-11-25)
Field: Chemistry — Organic Chemistry

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms. Insulin ; Glycan ; Proteolytic enzymes ; Glycosylation ; Chemistry ; Biochemistry ; Glycoprotein ; Computational biology ; Biology ; Enzyme ; Endocrinology Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Enzyme Production and Characterization
Authors: Alexis M. Fenton | Fikile R. Brushett
Source:  (preprint, 2021-08-23)
Field: Chemistry — Electrochemistry

Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in solutions and as such has been used in numerous fields of study. Recently, automation has been introduced to extend the capabilities of voltammetric analysis through approaches such as Bayesian parameter estimation and compound identification. However, opportunities exist to enable more versatile methods across a wider range of solution compositions and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of analytes in multicomponent solutions across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled solutions each containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating the ability to identify redox-active constituents of a multicomponent solution. Finally, we identify areas of further improvement—such as achieving greater detection accuracy—and future applications to potentially enhance in situ or operando diagnostic workflows. Analyte ; Voltammetry ; Computer science ; Phenothiazine ; Bayesian probability ; Protocol (science) ; Cyclic voltammetry ; Inference ; Experimental data ; Electrochemistry ; Biological system ; Chemistry ; Materials science ; Electrode ; Artificial intelligence ; Chromatography ; Mathematics ; Statistics ; Physical chemistry Electrochemical Analysis and Applications ; Conducting polymers and applications ; Advanced Chemical Sensor Technologies
Authors: Gbolade O. Kayode | M. M. Montemore
Source:  (preprint, 2021-07-30)
Field: Materials Science — Materials Chemistry

Factors Controlling Oxophilicity and Carbophilicity of Transition Metals and Main Group Metals The strength of interaction between a metal and oxygen and/or carbon is a crucial factor for catalytic performance, materials stability, and other important applications. While these are fundamental properties in materials science, there is no general understanding of what makes a metal oxophilic or carbophilic, especially for main group metals. In this work, we elucidate the factors that control how oxophilic or carbophilic a metal is by creating a predictive model and applying it to a variety of data sets for transition metals and main group metals, including DFT-calculated adsorption energies and experimental formation energies. Our model is easily interpretable and accurately describes oxophilic and carbophilic trends across different regions of the periodic table. This model captures the ionic contribution to bonding, the adsorbate-sp contribution to bonding, and the adsorbate-d contribution to bonding by using the reduction potential, matrix coupling elements, band centers, and band filling. For transition metals, the adsorbate-surface d coupling is the major factor that determines oxophilicity relative to carbophilicity. For metals that do not contain d electrons either in their core or valence shell (Li, Be, Na, Mg, Al, K, and Ca), the reduction potential and the adsorbate-surface s coupling are the major factors. As a simple application, we demonstrate the utility of oxophilicity and carbophilicity in rapidly screening metal dopants for improved selectivity for ethylene epoxidation on silver-based catalysts. Using our model, we established a direct relationship between the electronic properties of the metal dopants and their selectivity for ethylene epoxidation. The results suggest that transition metals with high adsorbate-surface d coupling and main group metals with low adsorbate-surface s coupling are good silver-dopant candidates for this reaction. Overall, the improved linkage between a metal’s electronic structure and its interaction with carbon or oxygen will be broadly useful in design of functional materials for a variety of applications. Transition metal ; Dopant ; Metal ; Chemical physics ; Valence electron ; Ionic bonding ; Adsorption ; Catalysis ; Chemistry ; Valence (chemistry) ; Main group element ; Selectivity ; Materials science ; Computational chemistry ; Inorganic chemistry ; Electron ; Physical chemistry ; Ion ; Doping ; Physics ; Organic chemistry Catalytic Processes in Materials Science ; Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Troy Munro
Source: Digital Commons - USU (Utah State University) (article, 2021-10-08)
Field: Materials Science — Polymers and Plastics

Thermal Property Measurement of Thin Fibers by Complementary Methods To improve measurement reliability and repeatability and resolve the orders of magnitude discrepancy between the two different measurements (via reduced model transient electrothermal and lock-in IR thermography), this dissertation details the development of three complementary methods to accurately measure the thermal properties of the natural and synthetic Nephila (N.) clavipes spider dragline fibers. The thermal conductivity and diffusivity of the dragline silk of the N. clavipes spider has been characterized by one research group to be 151-416 W m−1 K −1 and 6.4-12.3 ×10−5 m2 s −1 , respectively, for samples with low to high strains (zero to 19.7%). Thermal diffusivity of the dragline silk of a different spider species, Araneus diadematus, has been determined by another research group as 2 ×10−7 m2 s −1 for un-stretched silk. This dissertation seeks to resolve this discrepancy by three complementary methods. The methods detailed are the transient electrothermal technique (in both reduced and full model versions), the 3ω method (for both current and voltage sources), and the non-contact, photothermal, quantum-dot spectral shape-based fluorescence thermometry method. These methods were also validated with electrically conductive and non-conductive fibers. The resulting thermal conductivity of the dragline silk is 1.2 W m−1 K −1 , the thermal diffusivity is 6 ×10−7 m2 s −1 , and the volumetric heat capacity is 2000 kJ m−3 K −1 , with an uncertainty of about 12% for each property Property (philosophy) ; Materials science ; Computer science ; Philosophy ; Epistemology Textile materials and evaluations ; Radiative Heat Transfer Studies ; Mechanical and Thermal Properties Analysis
Authors: Dong‐Jin Lee | Dae Yu Kim
Source: Research Square (preprint, 2021-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D Hotspot Matrix of Au Nanoparticles on Au Nanostructures with a Spacer Layer of Dithiol Molecules for Highly Sensitive Surface-Enhanced Raman Spectroscopy Abstract Engineering of efficient plasmonic hotspots has been receiving great attention to enhance the sensitivity of surface-enhanced Raman scattering (SERS). In the present study, we propose a highly sensitive SERS platform based on Au nanoparticles (AuNPs) on Au nanostructures (AuNSs) with a spacer layer of 1,4-benzenedimethanethiol (BDMT). The three-dimensional (3D) hotspot matrix has been rationally designed based on the idea of employing 3D hotspots with a vertical nanogap between AuNSs and AuNPs after generating large area two-dimensional hotspots of AuNSs. AuNP@BDMT@AuNSs are fabricated by functionalizing BDMT on AuNSs and then immobilizing AuNPs. The Raman signal of the AuNP@BDMT@AuNSs is approximately twelve times higher than that of AuNSs at 100 nM of rhodamine 6G. The AuNP@BDMT@AuNSs are then employed to detect thiram, which is used as a fungicide, with a detection limit of 13 nM. Our proposed platform thus shows significant potential for use in highly sensitive SERS sensors. Rhodamine 6G ; Materials science ; Raman scattering ; Raman spectroscopy ; Nanoparticle ; Surface-enhanced Raman spectroscopy ; Nanostructure ; Colloidal gold ; Plasmon ; Nanotechnology ; Surface plasmon resonance ; Molecule ; Optoelectronics ; Chemistry ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Extracellular vesicles in disease
Authors: Maxime Ducamp | François‐Xavier Coudert
Source:  (preprint, 2021-06-23)
Field: Chemistry — Inorganic Chemistry

Systematic Study of the Thermal Properties of Zeolitic Frameworks We report a systematic study of the thermal and mechanical properties of 134 pure SiO2 zeolites through DFT-based calculations by making use of the quasi-harmonic approximation (out of a total of 242 known fully ordered zeolitic frameworks). The comparison of our results with reported experimental data for several zeolites revealed a very good accuracy and validated our simulation methodology. We observe a wide range of thermal expansion coefficients (from −5 to −35 MK−1), highlighting the great influence of the framework topology over this physical property, while demonstrating that all pure-silica zeolites exhibit negative thermal expansion (NTE). Our simulations also provide a path for the computation of the bulk modulus for each structure, as well as its pressure and temperature dependence. Results revealed a large gamut of bulk modulus values (from 8 to 134 GPa), showing that most frameworks display pressure-induced softening — but not all! Thermal expansion ; Materials science ; Bulk modulus ; Thermal ; Thermodynamics ; Modulus ; Softening ; Atmospheric temperature range ; Range (aeronautics) ; Composite material ; Physics Zeolite Catalysis and Synthesis ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Greta Koumarianou | Irène Wang | Lincoln Satterhwaite | David Patterson
Source:  (preprint, 2021-11-05)
Field: Chemistry — Spectroscopy

Assignment-free chirality detection in unknown samples via microwave three-wave mixing Straightforward identification of chiral molecules in multi-component mixtures of unknown composition is extremely challenging. Current spectrometric and chromatographic methods cannot unambiguously identify components while the state of the art spectroscopic methods are limited by the difficult and time-consuming task of spectral assignment. Here, we introduce a highly sensitive generalized version of microwave three-wave mixing that uses broad-spectrum fields to detect chiral molecules in enantiomeric excess without any prior chemical knowledge of the sample. This method does not require spectral assignment as a necessary step to extract information out of a spectrum. We demonstrate our method by recording three-wave mixing spectra of multi-component samples that provide direct evidence of enantiomeric excess. Our method opens up new capabilities in ultrasensitive phase-coherent spectroscopic detection that can be applied for chiral detection in real-life mixtures, raw products of chemical reactions and difficult to assign novel exotic species. Mixing (physics) ; Chirality (physics) ; Microwave ; Enantiomer ; Component (thermodynamics) ; Molecule ; Enantiomeric excess ; Spectral line ; Biological system ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Physics ; Stereochemistry ; Organic chemistry ; Catalysis ; Thermodynamics ; Quantum mechanics Molecular Spectroscopy and Structure ; Terahertz technology and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Rodrigo Silva | Gabriel M. F. Batista | Timothy J. Brocksom | Kléber T. de Oliveira
Source: Research Square (preprint, 2021-12-29)
Field: Chemistry — Organic Chemistry

Discovery of EDA-Complex Photocatalyzed Reactions Using Multidimensional Image Processing: Iminophosphorane Synthesis as a Case Study Abstract Herein, we report a multidimensional screening strategy for the discovery of EDA-complex photocatalyzed reactions using only photographic devices (webcam, cellphone) and TLC analysis. An algorithm was designed to identify automatically EDA-complex reactive mixtures in solution from digital image processing in a 96-wells microplate and by TLC-analysis. The code highlights the region of absorption of the mixture in the visible spectrum, and the quantity of the color change through grayscale values. Furthermore, the code identifies automatically the blurs on the TLC plate and classifies the mixture as colorimetric reactions, non-reactive or potentially reactive EDA mixtures. This strategy allowed us to discover and then optimize a new EDA-mediated approach for obtaining iminophosphoranes in up to 90% yield. Computer science ; Computer architecture ; Combinatorial chemistry ; Chemistry Radical Photochemical Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Sulfur-Based Synthesis Techniques