jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mikhail Suyetin
Source: Faraday Discussions (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

The application of machine learning for predicting the methane uptake and working capacity of MOFs Multiple linear regression analysis, as a part of machine learning, is employed to develop equations for the quick and accurate prediction of the methane uptake and working capacity of metal-organic frameworks (MOFs). Only three crystal characteristics of MOFs (geometric descriptors) are employed for developing the equations: surface area, pore volume and density of the crystal structure. The values of the geometric descriptors can be obtained much more cheaply in terms of time and other resources compared to running calculations of gas sorption or performing experimental work. Within this work sets of equations are provided for the different cases studied: a series of MOFs with NbO topology, a set of benchmark MOFs with outstanding methane storage and working capacities, and the whole CoRE MOF database (11 000 structures). Methane ; Sorption ; Benchmark (surveying) ; Work (physics) ; Volume (thermodynamics) ; Series (stratigraphy) ; Crystal (programming language) ; Core (optical fiber) ; Computer science ; Materials science ; Biological system ; Thermodynamics ; Chemistry ; Adsorption ; Physical chemistry ; Physics ; Organic chemistry ; Geology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yongtao Liu | Roger Proksch | Chun Yin Wong | Maxim Ziatdinov | Sergei V. Kalinin
Source: Advanced Materials (article, 2021-09-12)
Field: Materials Science — Materials Chemistry

Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. The rVAE discovers the latent representations of the domain wall geometries and their dynamics, thus providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning. Ferroelectricity ; Materials science ; Piezoresponse force microscopy ; Polarization (electrochemistry) ; Condensed matter physics ; Nonlinear system ; Domain wall (magnetism) ; Nanotechnology ; Optoelectronics ; Magnetic field ; Physics ; Magnetization ; Dielectric Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Acoustic Wave Resonator Technologies
Authors: Robert M. Ziolek | Paul Smith | Demi L. Pink | Cécile A. Dreiss | Christian D. Lorenz
Source: Macromolecules (article, 2021-04-14)
Field: Chemistry — Organic Chemistry

Unsupervised Learning Unravels the Structure of Four-Arm and Linear Block Copolymer Micelles Understanding the nanoscale structure of polymeric micelles is challenging: their relatively small size tests the limits of most experimental techniques, while the great conformational flexibility of the individual polymer chains makes deriving insight from computer simulations difficult. Pluronics and Tetronics are amphiphilic block copolymers based on poly(ethylene oxide) and poly(propylene oxide) blocks that self-assemble into micelles, which have been widely studied experimentally given their extensive use as excipients in drug formulations and as biomaterials. In contrast to these wide-ranging applications, the characterization of their nanoscale structure and dynamics is still incomplete. In particular, how the architecture of the blocks in linear Pluronics and four-arm Tetronics influences the arrangement of the chains within a core–shell morphology is not well understood. We apply unsupervised machine learning techniques to provide an unprecedented level of detail regarding the distribution of polymer conformations within the micelles and identify the underlying structure in the seemingly disordered micellar corona. The methodology applied in this work improves our understanding of the structure of these industrially relevant nanoparticles and establishes a general methodology for investigating the conformational distribution of polymers in self-assembled structures. Micelle ; Copolymer ; Polymer ; Materials science ; Nanoscopic scale ; Amphiphile ; Nanoparticle ; Nanotechnology ; Chemistry ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Machine Learning in Materials Science
Authors: Alfredo Serrano Jiménez | Alberto S. Muzas | Yaolong Zhang | Juraj Ovčar | Bin Jiang | Ivor Lončarić | J. I. Juaristi | M. Alducin
Source: Journal of Chemical Theory and Computation (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach Modeling the ultrafast photoinduced dynamics and reactivity of adsorbates on metals requires including the effect of the laser-excited electrons and, in many cases, also the effect of the highly excited surface lattice. Although the recent ab initio molecular dynamics with electronic friction and thermostats, (<i>T</i><sub>e</sub>,<i>T</i><sub>l</sub>)-AIMDEF [Alducin, M.; <i>Phys. Rev. Lett.</i> 2019, 123, 246802], enables such complex modeling, its computational cost may limit its applicability. Here, we use the new embedded atom neural network (EANN) method [Zhang, Y.; <i>J. Phys. Chem. Lett.</i> 2019, 10, 4962] to develop an accurate and extremely complex potential energy surface (PES) that allows us a detailed and reliable description of the photoinduced desorption of CO from the Pd(111) surface with a coverage of 0.75 monolayer. Molecular dynamics simulations performed on this EANN-PES reproduce the (<i>T</i><sub>e</sub>,<i>T</i><sub>l</sub>)-AIMDEF results with a remarkable level of accuracy. This demonstrates the outstanding performance of the obtained EANN-PES that is able to reproduce available density functional theory (DFT) data for an extensive range of surface temperatures (90-1000 K); a large number of degrees of freedom, those corresponding to six CO adsorbates and 24 moving surface atoms; and the varying CO coverage caused by the abundant desorption events. Desorption ; Excited state ; Molecular dynamics ; Chemical physics ; Materials science ; Density functional theory ; Electronic structure ; Monolayer ; Chemistry ; Adsorption ; Physics ; Physical chemistry ; Atomic physics ; Nanotechnology ; Computational chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Liangmei Rao | Xinyu Lu | Lulu Xu | Yifu Zhu | Ting Xue | Yu Ge | Zhongshu Duan | Xuemin Duan | Yangping Wen | Jingkun Xu
Source: Chemosphere (article, 2021-11-27)
Field: Materials Science — Materials Chemistry

Green synthesis of kudzu vine biochar decorated graphene-like MoSe2 with the oxidase-like activity as intelligent nanozyme sensing platform for hesperetin Biochar ; Graphene ; Materials science ; Detection limit ; Nanomaterials ; Nanotechnology ; Molybdenum trioxide ; Electrochemistry ; Absorbance ; Chemical engineering ; Chemistry ; Molybdenum ; Electrode ; Metallurgy ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Carbon and Quantum Dots Applications
Authors: Georgiy Akopov | Nethmi W. Hewage | Philip Yox | Gayatri Viswanathan | Shannon Lee | Liam Hulsebosch | Sarah D. Cady | Alexander L. Paterson | Frédéric A. Perras | Wenqian Xu | Kui Wu | Yaroslav Mudryk | Kirill Kovnir
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis-enabled exploration of chiral and polar multivalent quaternary sulfides An innovative method of synthesis is reported for the large and diverse (RE)<sub>6</sub>(TM) <sub><i>x</i></sub> (Tt)<sub>2</sub>S<sub>14</sub> (RE = rare-earth, TM = transition metals, Tt = Si, Ge, and Sn) family of compounds (∼1000 members, ∼325 contain Si), crystallizing in the noncentrosymmetric, chiral, and polar <i>P</i>6<sub>3</sub> space group. Traditional synthesis of such phases involves the annealing of elements or binary sulfides at elevated temperatures. The atomic mixing of refractory components technique, presented here, allows the synthesis of known members and vastly expands the family to nearly the entire transition metal block, including 3d, 4d, and 5d TMs with oxidation states ranging from 1+ to 4+. Arc-melting of the RE, TM, and tetrel elements of choice forms an atomically-mixed precursor, which readily reacts with sulfur providing bulk powders and large single crystals of the target quaternary sulfides. Detailed <i>in situ</i> and <i>ex situ</i> experiments show the mechanism of formation, which involves multiphase binary sulfide intermediates. Crystal structures and metal oxidation states were corroborated by a combination of single crystal X-ray diffraction, elemental analysis, EPR, NMR, and SQUID magnetometry. The potential of La<sub>6</sub>(TM) <sub><i>x</i></sub> (Tt)<sub>2</sub>S<sub>14</sub> compounds for non-linear optical applications was also demonstrated. Sulfide ; Polar ; Quaternary ; Semiconductor ; Transition metal ; Phase (matter) ; Chemistry ; Combinatorial chemistry ; Chemical physics ; Materials science ; Nanotechnology ; Organic chemistry ; Physics ; Geology ; Catalysis ; Optoelectronics ; Paleontology Crystal Structures and Properties ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Tetsuro Ueno | Hideaki Ishibashi | Hideitsu Hino | Kanta Ono
Source: npj Computational Materials (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Automated stopping criterion for spectral measurements with active learning Abstract The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics. Optimal stopping ; Computer science ; Stopping time ; Process (computing) ; Stopping rule ; Early stopping ; Gaussian ; Algorithm ; Mathematics ; Artificial intelligence ; Mathematical optimization ; Statistics ; Physics ; Artificial neural network Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Spectroscopy and Chemometric Analyses
Authors: A. Ylinen | Hannu Maaheimo | Adina Anghelescu‐Hakala | Merja Penttilä | Laura Salusjärvi | Mervi Toivari
Source: Journal of Industrial Microbiology & Biotechnology (article, 2021-04-23)
Field: Materials Science — Biomaterials

Production of D-lactic acid containing polyhydroxyalkanoate polymers in yeast <i>Saccharomyces cerevisiae</i> Polyhydroxyalkanoates (PHAs) provide biodegradable and bio-based alternatives to conventional plastics. Incorporation of 2-hydroxy acid monomers into polymer, in addition to 3-hydroxy acids, offers possibility to tailor the polymer properties. In this study, poly(D-lactic acid) (PDLA) and copolymer P(LA-3HB) were produced and characterized for the first time in the yeast Saccharomyces cerevisiae. Expression of engineered PHA synthase PhaC1437Ps6-19, propionyl-CoA transferase Pct540Cp, acetyl-CoA acetyltransferase PhaA, and acetoacetyl-CoA reductase PhaB1 resulted in accumulation of 3.6% P(LA-3HB) and expression of engineered enzymes PhaC1Pre and PctMe resulted in accumulation of 0.73% PDLA of the cell dry weight (CDW). According to NMR, P(LA-3HB) contained D-lactic acid repeating sequences. For reference, expression of PhaA, PhaB1, and PHA synthase PhaC1 resulted in accumulation 11% poly(hydroxybutyrate) (PHB) of the CDW. Weight average molecular weights of these polymers were comparable to similar polymers produced by bacterial strains, 24.6, 6.3, and 1 130 kDa for P(LA-3HB), PDLA, and PHB, respectively. The results suggest that yeast, as a robust and acid tolerant industrial production organism, could be suitable for production of 2-hydroxy acid containing PHAs from sugars or from 2-hydroxy acid containing raw materials. Moreover, the wide substrate specificity of PHA synthase enzymes employed increases the possibilities for modifying copolymer properties in yeast in the future. Polyhydroxyalkanoates ; Yeast ; Saccharomyces cerevisiae ; Biochemistry ; Lactic acid ; Enzyme ; Chemistry ; Substrate (aquarium) ; Copolymer ; Biosynthesis ; Polymer ; Bacteria ; Biology ; Organic chemistry biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Biopolymer Synthesis and Applications
Authors: Tobias Lemke | Moritz Edte | Denis Gebauer | Christine Peter
Source: The Journal of Physical Chemistry B (article, 2021-09-02)
Field: Materials Science — Materials Chemistry

Three Reasons Why Aspartic Acid and Glutamic Acid Sequences Have a Surprisingly Different Influence on Mineralization Understanding the role of polymers rich in aspartic acid (Asp) and glutamic acid (Glu) is the key to gaining precise control over mineralization processes. Despite their chemical similarity, experiments revealed a surprisingly different influence of Asp and Glu sequences. We conducted molecular dynamics simulations of Asp and Glu peptides in the presence of calcium and chloride ions to elucidate the underlying phenomena. In line with experimental differences, in our simulations, we indeed find strong differences in the way the peptides interact with ions in solution. The investigated Asp pentapeptide tends to pull a lot of ions into its vicinity, and many structures with clusters of calcium and chloride ions on the surface of the peptide can be observed. Under the same conditions, comparatively fewer ions can be found in proximity of the investigated Glu pentapeptide, and the structures are characterized by single calcium ions bound to multiple carboxylate groups. Based on our simulation data, we identified three reasons contributing to these differences, leading to a new level of understanding additive-ion interactions. Aspartic acid ; Glutamic acid ; Mineralization (soil science) ; Chemistry ; Environmental chemistry ; Biochemistry ; Amino acid ; Organic chemistry ; Nitrogen Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Geological and Geochemical Analysis
Authors: Jake R. Hughes | Alyssa S. Miller | Camryn E. Wallace | Gopi Nath Vemuri | Peter M. Iovine
Source: Frontiers in Chemistry (review, 2021-01-20)
Field: Materials Science — Materials Chemistry

Biomedically Relevant Applications of Bolaamphiphiles and Bolaamphiphile-Containing Materials Bolaamphiphiles (BAs) are structurally segmented molecules with rich assembly characteristics and diverse physical properties. Interest in BAs as standalone active agents or as constituents of more complex therapeutic formulations has increased substantially in recent years. The preorganized amphiphilicity of BAs allows for a range of biological activities including applications that rely on multivalency. This review summarizes BA-related research in biomedically relevant areas. In particular, we review BA-related literature in four areas: gene delivery, antimicrobial materials, hydrogels, and prodrugs. We also discuss several distinguishing characteristics of BAs that impact their utility as biomedically relevant compounds. Prodrug ; Self-healing hydrogels ; Nanotechnology ; Chemistry ; Drug delivery ; Antimicrobial ; Combinatorial chemistry ; Computational biology ; Materials science ; Biochemistry ; Organic chemistry ; Biology Quantum Dots Synthesis And Properties ; Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery
Authors: Julian D. Gale | Luc M. LeBlanc | Peter R. Spackman | Alessandro Silvestri | Paolo Raiteri
Source: Journal of Chemical Theory and Computation (article, 2021-11-04)
Field: Materials Science — Materials Chemistry

A Universal Force Field for Materials, Periodic GFN-FF: Implementation and Examination In this study, the adaption of the recently published molecular GFN-FF for periodic boundary conditions (pGFN-FF) is described through the use of neighbor lists combined with appropriate charge sums to handle any dimensionality from 1D polymers to 2D surfaces and 3D solids. Numerical integration over the Brillouin zone for the calculation of π bond orders of periodic fragments is also included. Aside from adapting the GFN-FF method to handle periodicity, improvements to the method are proposed in regard to the calculation of topological charges through the inclusion of a screened Coulomb term that leads to more physical charges and avoids a number of pathological cases. Short-range damping of three-body dispersion is also included to avoid collapse of some structures. Analytic second derivatives are also formulated with respect to both Cartesian and strain variables, including prescreening of terms to accelerate the dispersion/coordination number contribution to the Hessian. The modified pGFN-FF scheme is then applied to a wide range of different materials in order to examine how well this universal model performs. Hessian matrix ; Brillouin zone ; Ansatz ; Periodic boundary conditions ; Coulomb ; Curse of dimensionality ; Dispersion (optics) ; Cartesian coordinate system ; Instanton ; Computer science ; Boundary value problem ; Statistical physics ; Topology (electrical circuits) ; Physics ; Applied mathematics ; Mathematics ; Mathematical analysis ; Quantum mechanics ; Geometry Boron and Carbon Nanomaterials Research ; Energetic Materials and Combustion ; Advanced Chemical Physics Studies
Authors: Siddhartha Pati | Tanmay Sarkar | Hassan I. Sheikh | Kaushik Kumar Bharadwaj | Pranab Kishor Mohapatra | Anil Chatterji | Bisnu Prasad Dash | Hisham Atan Edinur | Bryan Raveen Nelson
Source: Frontiers in Marine Science (article, 2021-05-05)
Field: Materials Science — Biomaterials

γ-Irradiated Chitosan From Carcinoscorpius rotundicauda (Latreille, 1802) Improves the Shelf Life of Refrigerated Aquatic Products The γ-irradiated horseshoe crab chitosan was used as food coating to extend the shelf life of marine shrimp and fish. Fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), and X-ray diffraction (XRD) were used to characterize the γ-irradiated chitosan. After employing control (untreated seafood samples/no preservatives), chemical preservation (treated with 2% glacial acetic acid) and 2% chitosan (0, 10, and 20 kGy γ-irradiation) for the assessment assay, the shrimp ( Penaeus merguiensis ), pomfret ( Pampus argenteus ), and hilsa fish ( Tenualosa ilisha ) samples were examined for pH, thiobarbituric acid reactive substance (TBARS), total viable counts (TVC), and sensory evaluation changes while under 15-day refrigeration at 4°C. The results of FT-IR, XRD, and FE-SEM analysis revealed that irradiated chitosan possessed a crystalline structure with smooth texture on its surface. Analysis of pH, TBARS, TVC, and sensory evaluation demarcated irradiated chitosan with the ability to delay microbial growth and this prolonged the shelf life of refrigerated shrimp and fish. With novelty on γ-irradiated horseshoe crab chitosan use as natural preserving agent, fisheries industries and food packaging practitioners would benefit from its microbial-inert abilities particularly for long distant cold storage transport of packaged marine meats. Chitosan ; Shrimp ; Shelf life ; Food science ; Fishery ; TBARS ; Preservative ; Chemistry ; Biology ; Antioxidant ; Biochemistry Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Date Palm Research Studies
Authors: Elena Bañón | A N Jesús García | A. Marcilla
Source: Journal of Analytical and Applied Pyrolysis (article, 2021-07-02)
Field: Materials Science — Biomaterials

Thermogravimetric analysis and Py-GC/MS for discrimination of leather from different animal species and tanning processes Thermogravimetric analysis ; Pyrolytic carbon ; Pyrolysis ; Principal component analysis ; Chemistry ; Thermal decomposition ; Gas chromatography ; Gas chromatography–mass spectrometry ; Animal species ; Decomposition ; Pulp and paper industry ; Mass spectrometry ; Thermal analysis ; Materials science ; Chromatography ; Thermal ; Organic chemistry ; Biology ; Mathematics Collagen: Extraction and Characterization ; Meat and Animal Product Quality ; Thermal and Kinetic Analysis
Authors: Sarah E. Biehn | Steffen Lindert
Source: Annual Review of Physical Chemistry (review, 2021-11-01)
Field: Chemistry — Spectroscopy

Protein Structure Prediction with Mass Spectrometry Data Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Mass spectrometry ; Footprinting ; Chemistry ; Protein structure ; Protein structure prediction ; Hydrogen–deuterium exchange ; Protein function ; Drug discovery ; Computer science ; Biological system ; Computational chemistry ; Data mining ; Chromatography ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Analytical Chemistry and Chromatography