jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Małgorzata Z. Makoś | Niraj Verma | Eric C. Larson | Marek Freindorf | Elfi Kraka
Source: The Journal of Chemical Physics (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Generative adversarial networks for transition state geometry prediction This work introduces a novel application of generative adversarial networks (GANs) for the prediction of starting geometries in transition state (TS) searches based on the geometries of reactants and products. The multi-dimensional potential energy space of a chemical reaction often complicates the location of a starting TS geometry, leading to the correct TS combining reactants and products in question. The proposed TS-GAN efficiently maps the space between reactants and products and generates reliable TS guess geometries, and it can be easily combined with any quantum chemical software package performing geometry optimizations. The TS-GAN was trained and applied to generate TS guess structures for typical chemical reactions, such as hydrogen migration, isomerization, and transition metal-catalyzed reactions. The performance of the TS-GAN was directly compared to that of classical approaches, proving its high accuracy and efficiency. The current TS-GAN can be extended to any dataset that contains sufficient chemical reactions for training. The software is freely available for training, experimentation, and prediction at https://github.com/ekraka/TS-GAN. Computer science ; Software ; Generative grammar ; Chemical space ; Algorithm ; State (computer science) ; Quantum chemical ; Work (physics) ; Isomerization ; Space (punctuation) ; Artificial intelligence ; Chemistry ; Catalysis ; Physics ; Thermodynamics ; Molecule ; Programming language ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zeynep Mine Şenol
Source: International Journal of Biological Macromolecules (article, 2021-05-24)
Field: Chemistry — Inorganic Chemistry

A chitosan-based composite for adsorption of uranyl ions; mechanism, isothems, kinetics and thermodynamics Adsorption ; Uranyl ; Chemistry ; Aqueous solution ; Langmuir ; Epichlorohydrin ; Endothermic process ; Freundlich equation ; Langmuir adsorption model ; Inorganic chemistry ; Composite number ; Ion ; Physical chemistry ; Materials science ; Organic chemistry Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes
Authors: Simona Dordević | Đani Đorđević | Petr Sedláček | Michal Kalina | Karolína Těšíková | Bojan Antonić | Bohuslava Tremlová | Jakub Treml | Marcela Nejezchlebova | Lukáš Vápenka | Aleš Rajchl | Monika Bulakova
Source: Polymers (article, 2021-10-01)
Field: Materials Science — Biomaterials

Incorporation of Natural Blueberry, Red Grapes and Parsley Extract By-Products into the Production of Chitosan Edible Films The aim of the research was to produce edible packaging based on chitosan with the addition of various concentrations of extracts of blueberry, red grape and parsley marcs. Packaging was made from extrudate extracts, which were subsequently analyzed by physicochemical methods: zeta-potential, gas barrier properties, thickness, water content, solubility, swelling degree, textural properties, total polyphenol content (TPC), polyphenols by high pressure liquid chromatography (HPLC), antioxidant activity, attenuated total reflectance Fourier-Transform spectroscopy (FTIR), antimicrobial activity and determination of migration of bioactive substances. The results indicate that a higher content of plant extracts have a statistically significant (<i>p</i> < 0.05) influence on properties of experimentally produced edible films. Edible films produced with the highest concentrations of red grapes marc extracts showed the most advantageous properties since antimicrobial activity against <i>E. coli</i> were the highest in this kind of produced film. The physical properties of edible films were also improved by the addition of extracts; gas permeability toward oxygen can be defined as advantageous, as can swelling degree, which decreased with higher concentrations of extracts. The research emphasized the possibility to use plant foodstuffs by-products in the production of edible/biodegradable films, helping in the overall sustainability and eco-friendliness of food/package production. Food science ; Chemistry ; Chitosan ; Polyphenol ; Food packaging ; Active packaging ; Oxygen permeability ; Solubility ; Fourier transform infrared spectroscopy ; Antimicrobial ; Swelling ; Antioxidant ; Materials science ; Organic chemistry ; Oxygen ; Chemical engineering Nanocomposite Films for Food Packaging ; Microencapsulation and Drying Processes ; Consumer Packaging Perceptions and Trends
Authors: Salvio Suárez–García | Rubén Solórzano | Ramón Alibés | Félix Busquè | Fernando Novio | Daniel Ruiz‐Molina
Source: Coordination Chemistry Reviews (article, 2021-05-08)
Field: Chemistry — Inorganic Chemistry

Antitumour activity of coordination polymer nanoparticles Nanoscale coordination polymers (NCPs) have fascinated researchers over the last years. Their intrinsic theranostic properties of metal ions and organic ligands, the encapsulation of several drugs/biomolecules with excellent yields and the surface functionalisation, enhancing their biocompatibility and targeting, have remarkably impacted in prospective drug delivery alternatives in medicine. Moreover, the properties and characteristics of these nanoparticles (NPs) can be fine-tuned thanks to the synthetic flexibility of coordination chemistry. For all these reasons, the number of examples published has grown exponentially over the last years, embracing different disciplines such as molecular electronics, sensors or nanomedicine, among others. Specifically, significant advances in antitumoural applications are reported, one of the areas where this novel family of NPs has experienced a considerable advance. NCPs have accomplished a high sophistication degree and efficiency as theranostic nanoplatforms (i.e., drug delivery carriers and bioimaging probes) with long residence time in the bloodstream, targeting capacities and remarkable cellular internalisation. In this review, an introduction emphasizing the advantages of NPs for cancer treatment is included. Later on, the most representative examples of NCPs for antitumoural applications are described grouped into six mean representative areas: i) encapsulation approaches, ii) stimuli-responsive NCPs, iii) metal chemotherapy, iv) photodynamic therapy (PDT), v) unconventional therapeutic approaches and vi) theranostics. Particular emphasis is given to understand the encapsulation/release properties of these particles at the nanoscale and their interaction with biological environments, highlighting any limitation and challenges that these systems are facing from a clinical translation perspective and envisioning possible future trends and areas that will deserve further attention for the following years. Nanotechnology ; Nanomedicine ; Chemistry ; Drug delivery ; Nanoparticle ; Cancer therapy ; Biocompatibility ; Cancer ; Materials science ; Medicine Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanoparticle-Based Drug Delivery
Authors: David Sachs | Adam Wahlsten | Sebastian Kozerke | Gaetana Restivo | Edoardo Mazza
Source: Biomechanics and Modeling in Mechanobiology (article, 2021-02-10)
Field: Materials Science — Polymers and Plastics

A biphasic multilayer computational model of human skin The present study investigates the layer-specific mechanical behavior of human skin. Motivated by skin's histology, a biphasic model is proposed which differentiates between epidermis, papillary and reticular dermis, and hypodermis. Inverse analysis of ex vivo tensile and in vivo suction experiments yields mechanical parameters for each layer and predicts a stiff reticular dermis and successively softer papillary dermis, epidermis and hypodermis. Layer-specific analysis of simulations underlines the dominating role of the reticular dermis in tensile loading. Furthermore, it shows that the observed out-of-plane deflection in ex vivo tensile tests is a direct consequence of the layered structure of skin. In in vivo suction experiments, the softer upper layers strongly influence the mechanical response, whose dissipative part is determined by interstitial fluid redistribution within the tissue. Magnetic resonance imaging-based visualization of skin deformation in suction experiments confirms the deformation pattern predicted by the multilayer model, showing a consistent decrease in dermal thickness for large probe opening diameters. Reticular Dermis ; Dermis ; Reticular connective tissue ; Materials science ; Ultimate tensile strength ; Epidermis (zoology) ; Ex vivo ; Papillary dermis ; Anatomy ; Composite material ; Biomedical engineering ; In vivo ; Biology Textile materials and evaluations ; Cellular Mechanics and Interactions ; Elasticity and Material Modeling
Authors: Deboki Naskar | Sunaina Sapru | Ananta K. Ghosh | Rui L. Reis | Tuli Dey | Subhas C. Kundu
Source: Biomedical Materials (review, 2021-08-24)
Field: Materials Science — Biomaterials

Nonmulberry silk proteins: multipurpose ingredient in bio-functional assembly The emerging field of tissue engineering and regenerative medicines utilising artificial polymers is facing many problems. Despite having mechanical stability, non-toxicity and biodegradability, most of them lack cytocompatibility and biocompatibility. Natural polymers (such as collagen, hyaluronic acid, fibrin, fibroin, and others), including blends, are introduced to the field to solve some of the relevant issues. Another natural biopolymer: silkworm silk gained special attention primarily due to its specific biophysical, biochemical, and material properties, worldwide availability, and cost-effectiveness. Silk proteins, namely fibroin and sericin extracted from domesticated mulberry silkworm<i>Bombyx mori</i>, are studied extensively in the last few decades for tissue engineering. Wild nonmulberry silkworm species, originated from India and other parts of the world, also produce silk proteins with variations in their nature and properties. Among the nonmulberry silkworm species,<i>Antheraea mylitta</i>(Indian Tropical Tasar),<i>A. assamensis/A. assama</i>(Indian Muga), and<i>Samia ricini/Philosamia ricini</i>(Indian Eri), along with<i>A. pernyi</i>(Chinese temperate Oak Tasar/Tussah) and<i>A. yamamai</i>(Japanese Oak Tasar) exhibit inherent tripeptide motifs of arginyl glycyl aspartic acid in their fibroin amino acid sequences, which support their candidacy as the potential biomaterials. Similarly, sericin isolated from such wild species delivers unique properties and is used as anti-apoptotic and growth-inducing factors in regenerative medicines. Other characteristics such as biodegradability, biocompatibility, and non-inflammatory nature make it suitable for tissue engineering and regenerative medicine based applications. A diverse range of matrices, including but not limited to nano-micro scale structures, nanofibres, thin films, hydrogels, and porous scaffolds, are prepared from the silk proteins (fibroins and sericins) for biomedical and tissue engineering research. This review aims to represent the progress made in medical and non-medical applications in the last couple of years and depict the present status of the investigations on Indian nonmulberry silk-based matrices as a particular reference due to its remarkable potentiality of regeneration of different types of tissues. It also discusses the future perspective in tissue engineering and regenerative medicines in the context of developing cutting-edge techniques such as 3D printing/bioprinting, microfluidics, organ-on-a-chip, and other electronics, optical and thermal property-based applications. SILK ; Ingredient ; Materials science ; Fibroin ; Active ingredient ; Polymer science ; Nanotechnology ; Composite material ; Food science ; Biology ; Pharmacology Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Insect Utilization and Effects
Authors: He‐Yun Du | Yifan Huang | Deniz Wong | Mao-Feng Tseng | Yi-Hsin Lee | Chen‐Hao Wang | Cheng‐Lan Lin | Germar Hoffmann | Kuei‐Hsien Chen | Li–Chyong Chen
Source: Nature Communications (article, 2021-02-26)
Field: Materials Science — Materials Chemistry

Nanoscale redox mapping at the MoS2-liquid interface Layered MoS<sub>2</sub> is considered as one of the most promising two-dimensional photocatalytic materials for hydrogen evolution and water splitting; however, the electronic structure at the MoS<sub>2</sub>-liquid interface is so far insufficiently resolved. Measuring and understanding the band offset at the surfaces of MoS<sub>2</sub> are crucial for understanding catalytic reactions and to achieve further improvements in performance. Herein, the heterogeneous charge transfer behavior of MoS<sub>2</sub> flakes of various layer numbers and sizes is addressed with high spatial resolution in organic solutions using the ferrocene/ferrocenium (Fc/Fc<sup>+</sup>) redox pair as a probe in near-field scanning electrochemical microscopy, i.e. in close nm probe-sample proximity. Redox mapping reveals an area and layer dependent reactivity for MoS<sub>2</sub> with a detailed insight into the local processes as band offset and confinement of the faradaic current obtained. In combination with additional characterization methods, we deduce a band alignment occurring at the liquid-solid interface. Redox ; Materials science ; Nanoscopic scale ; Water splitting ; Ferrocene ; Nanotechnology ; Band offset ; Characterization (materials science) ; Chemical physics ; Electrochemistry ; Offset (computer science) ; Electrode ; Catalysis ; Optoelectronics ; Chemistry ; Band gap ; Photocatalysis ; Physical chemistry ; Computer science 2D Materials and Applications ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: James E. M. Lewis
Source: Chemistry - A European Journal (article, 2021-01-07)
Field: Chemistry — Organic Chemistry

Multi‐functional, Low Symmetry Pd<sub>2</sub>L<sub>4</sub> Nanocage Libraries** Although many impressive metallo-supramolecular architectures have been reported, they tend towards high symmetry structures and avoid extraneous functionality to ensure high fidelity in the self-assembly process. This minimalist approach, however, limits the range of accessible structures and thus their potential applications. Herein is described the synthesis of a family of ditopic ligands wherein the ligand scaffolds are both low symmetry and incorporate exohedral functional moieties. Key to this design is the use of Cu<sup>I</sup> -catalysed azide-alkyne cycloaddition (CuAAC) chemistry, as the triazole is capable of acting as both a coordinating heterocycle and a tether between the ligand framework and functional unit simultaneously. A common precursor was used to generate ligands with various functionalities, allowing control of electronic properties whilst maintaining the core structure of the resultant cis-Pd<sub>2</sub> L<sub>4</sub> nanocage assemblies. The isostructural nature of the scaffold frameworks enabled formation of combinatorial libraries from the self-assembly of ligand mixtures, generating a statistical mixture of multi-functional, low symmetry architectures. Isostructural ; Ligand (biochemistry) ; Chemistry ; Supramolecular chemistry ; Combinatorial chemistry ; Cycloaddition ; Nanocages ; Scaffold ; Nanotechnology ; Materials science ; Computer science ; Crystallography ; Crystal structure ; Catalysis ; Organic chemistry Supramolecular Chemistry and Complexes ; Nanocluster Synthesis and Applications ; Supramolecular Self-Assembly in Materials
Authors: Zhang Yu | Ye Sang | Yanli Sun | Hucheng Zhao | Xi‐Qiao Feng
Source: Materials Today Communications (article, 2021-06-19)
Field: Materials Science — Materials Chemistry

Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets Materials science ; Aluminium ; Metallurgy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition
Authors: Zan Lian | Min Yang | Faheem Jan | Bo Li
Source: The Journal of Physical Chemistry Letters (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium–Sulfur Battery The "shuttle effect" and sluggish kinetics at cathode significantly hinder the further improvements of the lithium-sulfur (Li-S) battery, a candidate of next generation energy storage technology. Herein, machine learning based on high-throughput density functional theory calculations is employed to establish the pattern of polysulfides adsorption and screen the supported single-atom catalyst (SAC). The adsorptions are classified as two categories which successfully distinguish S-S bond breaking from the others. Moreover, a general trend of polysulfides adsorption was established regarding of both kind of metal and the nitrogen configurations on support. The regression model has a mean absolute error of 0.14 eV which exhibited a faithful predictive ability. Based on adsorption energy of soluble polysulfides and overpotential, the most promising SAC was proposed, and a volcano curve was found. In the end, a reactivity map is supplied to guide SAC design of the Li-S battery. Blueprint ; Battery (electricity) ; Lithium–sulfur battery ; Cathode ; Sulfur ; Catalysis ; Lithium (medication) ; Atom (system on chip) ; Materials science ; Engineering physics ; Chemistry ; Computer science ; Engineering ; Physics ; Physical chemistry ; Psychology ; Metallurgy ; Mechanical engineering ; Embedded system ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Christopher M. Andolina | Marta Bon | Daniele Passerone | Wissam A. Saidi
Source: The Journal of Physical Chemistry C (article, 2021-07-29)
Field: Materials Science — Materials Chemistry

Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials Materials composed of Ag, Au, and Ag–Au alloys remain of great interest despite decades of intense research scrutiny. We interpret these efforts as an impetus for developing robust, accurate, and relatively fast computational methods for modeling these materials. Herein, we describe the training, development, and validation of a machine learning deep neural-network potential (DNP) for improved modeling of Ag–Au systems. This DNP was iteratively trained using density functional theory (DFT) to produce a robust multi-length-scale potential, which yields results comparable to DFT on a wide range of properties such as equilibrium and nonequilibrium lattices, mechanical properties, and defect energies. Further, this DNP can well describe adatom (Ag or Au) energy barriers for diffusion on {100}-, {110}-, and {111}-terminated surfaces (Ag or Au), in agreement with previously reported works. We utilized the DNP to study the nucleation and growth of simulated seeded core–shell Ag and Au nanoparticles (NP). We show that both nanoalloys grow such that {111} facets significantly increase at the expense of the {100} ones. In contrast, the Ag core NP is found to have a more disordered inner structure than the Au one and that Ag adatoms in Au@Ag NP have a more pronounced penetration power than Au in Ag@Au NP. These findings are rationalized in terms of adatom adsorption and diffusion energies. Nucleation ; Bimetallic strip ; Materials science ; Density functional theory ; Length scale ; Nanoparticle ; Nanotechnology ; Nanocrystal ; Chemical physics ; Computational chemistry ; Thermodynamics ; Chemistry ; Metal ; Physics ; Metallurgy Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Catalytic Processes in Materials Science
Authors: Shuyuan Li | Yunjiang Zhang | Yuxuan Hu | Bijin Wang | Shaorui Sun | Xinwu Yang | Hong He
Source: Journal of Materiomics (article, 2021-02-09)
Field: Chemistry — Inorganic Chemistry

Predicting metal-organic frameworks as catalysts to fix carbon dioxide to cyclic carbonate by machine learning The process of discovering and developing new materials currently requires considerable effort, time, and expense. Machine learning (ML) algorithms can potentially provide quick and accurate methods for screening new materials. In the present work, the features of the metal organic frameworks (MOFs) as a catalyst for fixing carbon dioxide into cyclic carbonate were extracted to build a data set, which were collected from the experimental results of approximately 100 published papers. Classifiers were trained with the data set with various ML algorithms, including support vector machine (SVM), K-nearest neighbor classification (KNN), decision trees (DT), stochastic gradient descent (SGD), and neural networks (NN), to predict the catalytic performance. The ML models were trained on 80% of the data set and then tested on the remaining 20% to predict the carbon dioxide fixation ability. The trained ML model was extended to explore 1311 hypothetical MOFs, and some structures displayed a strong catalytic ability. Finally, the six best metal ions (Mn, V, Cu, Ni, Zr and Y) and four best ligands (tactmb, tdcbpp, TCPP, H3L) were determined. These six metals and four ligands could be combined into 24 MOFs, which are strongly potential catalysts for carbon dioxide fixation. Using machine learning methods can speed up the screening of materials, and this methodology is promising for application not only to MOFs as catalysts but also in many other materials science projects. Support vector machine ; Machine learning ; Artificial intelligence ; Catalysis ; Carbon fixation ; Artificial neural network ; Carbon dioxide ; Computer science ; Materials science ; Stochastic gradient descent ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science