jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Franck D’Agosto | Jutta Rieger | Muriel Lansalot
Source: Angewandte Chemie International Edition (review, 2019-10-04)
Field: Chemistry — Organic Chemistry

RAFT‐Mediated Polymerization‐Induced Self‐Assembly After a brief history that positions polymerization-induced self-assembly (PISA) in the field of polymer chemistry, this Review will cover the fundamentals of the PISA mechanism. Furthermore, this Review will also give an overview of some of the features and limitations of RAFT-mediated PISA in terms of the choice of the components involved, the nature of the nanoobjects that can be obtained and how the syntheses can be controlled, as well as some potential applications. Raft ; Polymerization ; Cover (algebra) ; Mechanism (biology) ; Self-assembly ; Nanotechnology ; Reversible addition−fragmentation chain-transfer polymerization ; Chemistry ; Computer science ; Polymer ; Materials science ; Engineering ; Radical polymerization ; Organic chemistry ; Philosophy ; Mechanical engineering ; Epistemology Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Radioactive element chemistry and processing
Authors: Sang Loon Tan | Mukesh M. Jotani | Edward R. T. Tiekink
Source: Acta Crystallographica Section E Crystallographic Communications (article, 2019-02-05)
Field: Chemistry — Physical and Theoretical Chemistry

Utilizing Hirshfeld surface calculations, non-covalent interaction (NCI) plots and the calculation of interaction energies in the analysis of molecular packing The analysis of atom-to-atom and/or residue-to-residue contacts remains a favoured mode of analysing the mol-ecular packing in crystals. In this contribution, additional tools are highlighted as methods for analysis in order to complement the 'crystallographer's tool', <i>PLATON</i> [Spek (2009). <i>Acta Cryst</i>. D<b>65</b>, 148-155]. Thus, a brief outline of the procedures and what can be learned by using <i>Crystal Explorer</i> [Spackman & Jayatilaka (2009). <i>CrystEngComm</i> <b>11</b>, 19-23] is presented. Attention is then directed towards evaluating the nature, <i>i.e</i>. attractive/weakly attractive/repulsive, of specific contacts employing <i>NCIPLOT</i> [Johnson <i>et al.</i> (2010). <i>J. Am. Chem. Soc</i>. <b>132</b>, 6498-6506]. This is complemented by a discussion of the calculation of energy frameworks utilizing the latest version of <i>Crystal Explorer</i>. All the mentioned programs are free of charge and straightforward to use. More importantly, they complement each other to give a more complete picture of how mol-ecules assemble in mol-ecular crystals. Complement (music) ; Crystal (programming language) ; Atom (system on chip) ; Crystallography ; Crystal structure ; Covalent bond ; Chemistry ; Materials science ; Chemical physics ; Computational chemistry ; Computer science ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Andriy Kryshtafovych | Torsten Schwede | Maya Topf | Krzysztof Fidelis | John Moult
Source: Proteins Structure Function and Bioinformatics (article, 2019-10-07)
Field: Materials Science — Materials Chemistry

Critical assessment of methods of protein structure prediction (CASP)—Round XIII CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically "ab initio" modeling). Progress was driven by the successful application of deep learning techniques to predict inter-residue distances. In turn, these results drove dramatic improvements in three-dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long-standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template-based models. Other areas-model refinement, accuracy estimation, and the structure of protein assemblies-have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants. CASP ; Protein structure prediction ; Template ; Computer science ; Protein structure ; Ab initio ; Structural biology ; Artificial intelligence ; Machine learning ; Computational biology ; Chemistry ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Genomics and Phylogenetic Studies
Authors: Juan Pablo | Nicholas E. Jackson | Michael A. Webb | Long‐Qing Chen | Joel E. Moore | Dane Morgan | Ryan Jacobs | Tresa M. Pollock | Darrell G. Schlom | Eric S. Toberer | James G. Analytis | Ismaïla Dabo | Dean M. DeLongchamp | Gregory A. Fiete | Gregory M. Grason | Geoffroy Hautier | Yifei Mo | Krishna Rajan | Evan J. Reed | Efrain E. Rodriguez | Vladan Stevanović | Jin Suntivich | Katsuyo Thornton | Ji‐Cheng Zhao
Source: npj Computational Materials (article, 2019-04-05)
Field: Materials Science — Materials Chemistry

New frontiers for the materials genome initiative Abstract The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts. Pace ; Software deployment ; Engineering ethics ; Foundation (evidence) ; Key (lock) ; Data science ; Computer science ; Management science ; Engineering ; Political science ; Software engineering ; Computer security Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Michael Heinrich | Giovanni Appendino | Thomas Efferth | Robert Fürst | Angelo A. Izzo | Oliver Kayser | John M. Pezzuto | Alvaro Viljoen
Source: Journal of Ethnopharmacology (review, 2019-09-14)
Field: Materials Science — Biomaterials

Best practice in research – Overcoming common challenges in phytopharmacological research Relevance (law) ; Computer science ; Best practice ; Perspective (graphical) ; Data science ; Natural (archaeology) ; Engineering ethics ; Process (computing) ; Management science ; Medicine ; Epistemology ; Psychology ; Political science ; Biology ; Engineering ; Artificial intelligence ; Law Phytochemistry and Bioactive Compounds ; Complementary and Alternative Medicine Studies ; Botanical Studies and Applications
Authors: Jiang Wang | Simon Olsson | Christoph Wehmeyer | Adrià Pérez | Nicholas E. Charron | Gianni De Fabritiis | Frank Noé | Cecilia Clementi
Source: ACS Central Science (article, 2019-04-15)
Field: Materials Science — Materials Chemistry

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction. Granularity ; Computer science ; Curse of dimensionality ; Molecular dynamics ; Dimensionality reduction ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Beatriz García-Pinel | Cristina Porras-Alcalá | Alicia Ortega-Rodríguez | Francisco Sarabia | José Prados | Consolación Melguizo | Juan Manuel López‐Romero
Source: Nanomaterials (review, 2019-04-19)
Field: Materials Science — Biomaterials

Lipid-Based Nanoparticles: Application and Recent Advances in Cancer Treatment Many therapeutically active molecules are non-soluble in aqueous systems, chemically and biologically fragile or present severe side effects. Lipid-based nanoparticle (LBNP) systems represent one of the most promising colloidal carriers for bioactive organic molecules. Their current application in oncology has revolutionized cancer treatment by improving the antitumor activity of several chemotherapeutic agents. LBNPs advantages include high temporal and thermal stability, high loading capacity, ease of preparation, low production costs, and large-scale industrial production since they can be prepared from natural sources. Moreover, the association of chemotherapeutic agents with lipid nanoparticles reduces active therapeutic dose and toxicity, decreases drug resistance and increases drug levels in tumor tissue by decreasing them in healthy tissue. LBNPs have been extensively assayed in in vitro cancer therapy but also in vivo, with promising results in some clinical trials. This review summarizes the types of LBNPs that have been developed in recent years and the main results when applied in cancer treatment, including essential assays in patients. Cancer treatment ; In vivo ; Cancer ; Solid lipid nanoparticle ; Drug ; Pharmacology ; Cancer therapy ; Therapeutic index ; Drug delivery ; Drug carrier ; Nanotechnology ; Medicine ; Materials science ; Biology ; Biotechnology ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Computational Drug Discovery Methods
Authors: Rajesh Kumar | Amit Sharma | H.B. Singh | Paolo Suating | Hyeong Seok Kim | Kyoung Sunwoo | Inseob Shim | Bruce C. Gibb | Jong Seung Kim
Source: Chemical Reviews (review, 2019-07-15)
Field: Chemistry — Spectroscopy

Revisiting Fluorescent Calixarenes: From Molecular Sensors to Smart Materials Calix[<i>n</i>]arenes (<i>n</i> = 4, 5, 6, 8) are "chalicelike" phenol-based macrocycles that are among the most fascinating and highly studied scaffolds in supramolecular chemistry. This stems from the functional and tunable diversity at both their upper and lower rims, their preorganized nonpolar cavities and preorganized ion-binding sites, and their well-defined conformations. Conjugation of calixarene scaffolds with various fluorogenic groups has led to the development of smart fluorescent probes that have been utilized as molecular sensors, in bioimaging, for drug and gene delivery, in self-assembly/aggregation, and as smart materials. The fine-tuning and incorporation of different ligating sites in the calix[4]arene scaffold have produced numerous molecular sensors for cations, anions, and biomolecules. Moreover, the aqueous solubility of <i>p</i>-sulfonatocalix[4]arenes has engendered their potential use in drug/gene delivery and enzymatic assays. In addition, because of their strong optical properties, fluorescent calix[4]arenes have been used to develop smart materials, including gels as well as nonlinear optical, organic light-emitting diode, and multiphoton materials. Finally, significant developments in the utility of fluorescent higher calixarenes have been made for bioapplications. This review critically summarizes the recent advances made in all of these different areas. Calixarene ; Chemistry ; Fluorescence ; Supramolecular chemistry ; Biomolecule ; Combinatorial chemistry ; Drug delivery ; Molecular recognition ; Nanotechnology ; Molecule ; Organic chemistry ; Materials science Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Luminescence and Fluorescent Materials
Authors: Michael Meyer
Source: BioMedical Engineering OnLine (review, 2019-03-18)
Field: Materials Science — Biomaterials

Processing of collagen based biomaterials and the resulting materials properties Collagen, the most abundant extracellular matrix protein in animal kingdom belongs to a family of fibrous proteins, which transfer load in tissues and which provide a highly biocompatible environment for cells. This high biocompatibility makes collagen a perfect biomaterial for implantable medical products and scaffolds for in vitro testing systems. To manufacture collagen based solutions, porous sponges, membranes and threads for surgical and dental purposes or cell culture matrices, collagen rich tissues as skin and tendon of mammals are intensively processed by physical and chemical means. Other tissues such as pericardium and intestine are more gently decellularized while maintaining their complex collagenous architectures. Tissue processing technologies are organized as a series of steps, which are combined in different ways to manufacture structurally versatile materials with varying properties in strength, stability against temperature and enzymatic degradation and cellular response. Complex structures are achieved by combined technologies. Different drying techniques are performed with sterilisation steps and the preparation of porous structures simultaneously. Chemical crosslinking is combined with casting steps as spinning, moulding or additive manufacturing techniques. Important progress is expected by using collagen based bio-inks, which can be formed into 3D structures and combined with live cells. This review will give an overview of the technological principles of processing collagen rich tissues down to collagen hydrolysates and the methods to rebuild differently shaped products. The effects of the processing steps on the final materials properties are discussed especially with regard to the thermal and the physical properties and the susceptibility to enzymatic degradation. These properties are key features for biological and clinical application, handling and metabolization. Decellularization ; Biocompatibility ; Materials science ; Biomaterial ; Biomedical engineering ; Extracellular matrix ; Biocompatible material ; Self-healing hydrogels ; Tissue engineering ; Genipin ; Nanotechnology ; Chemistry ; Biochemistry Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research
Authors: Esteban Beltrán-Gracia | Adolfo López-Camacho | Inocencio Higuera‐Ciapara | Jesús Bernardino Velázquez-Fernández | Alba Adriana Vallejo‐Cardona
Source: Cancer Nanotechnology (article, 2019-12-01)
Field: Materials Science — Biomaterials

Nanomedicine review: clinical developments in liposomal applications Abstract Background In recent years, disease treatment has evolved strategies that require increase in pharmaceutical agent’s efficacy and selectivity while decreasing their toxicity in normal tissues. These requirements have led to the development of nanoscale liposome systems for drug release. This review focuses on lipid features, pharmacological properties of liposomal formulations and the clinical studies of their application. Main body Several lipids are available, but their properties could affect pharmacological or clinical efficiency of drug formulations. Many liposomal formulations have been developed and are currently on the market. Proper selection of lipid is essential for the pharmacological effect to be improved. Most of the formulations use mainly zwitterionic, cationic or anionic lipids, PEG and/or cholesterol, which have different effects on stability, pharmacokinetics and delivery of the drug formulation. Clinical trials have shown that liposomes are pharmacologically and pharmacokinetically more efficient than drug-alone formulations in treating acute myeloid leukemia, hepatitis A, pain management, ovary, gastric breast and lung cancer, among others. Conclusion Liposomal formulations are less toxic than drugs alone and have better pharmacological parameters. Although they seem to be the first choice for drug delivery systems for various diseases, further research about dosage regimen regarding dose and time needs to be carried out. Liposome ; Medicine ; Pharmacology ; Drug delivery ; Drug ; Nanomedicine ; Pharmacokinetics ; PEGylation ; Clinical trial ; Nanotechnology ; Polyethylene glycol ; Chemistry ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; Advanced Drug Delivery Systems
Authors: Wei Ting Chen | Alexander Y. Zhu | Jared Sisler | Zameer Bharwani | Federico Capasso
Source: Nature Communications (article, 2019-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A broadband achromatic polarization-insensitive metalens consisting of anisotropic nanostructures Metasurfaces have attracted widespread attention due to an increasing demand of compact and wearable optical devices. For many applications, polarization-insensitive metasurfaces are highly desirable, and appear to limit the choice of their constituent elements to isotropic nanostructures. This greatly restricts the number of geometric parameters available in design. Here, we demonstrate a polarization-insensitive metalens using otherwise anisotropic nanofins which offer additional control over the dispersion and phase of the output light. As a result, we can render a metalens achromatic and polarization-insensitive across nearly the entire visible spectrum from wavelength λ = 460 nm to 700 nm, while maintaining diffraction-limited performance. The metalens is comprised of just a single layer of TiO<sub>2</sub> nanofins and has a numerical aperture of 0.2 with a diameter of 26.4 µm. The generality of our polarization-insensitive design allows it to be implemented in a plethora of other metasurface devices with applications ranging from imaging to virtual/augmented reality. Achromatic lens ; Polarization (electrochemistry) ; Broadband ; Optics ; Anisotropy ; Holography ; Materials science ; Optoelectronics ; Isotropy ; Numerical aperture ; Metamaterial ; Wavelength ; Nanostructure ; Physics ; Nanotechnology ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Stephen Wu | Yukiko Kondo | Masa‐aki Kakimoto | Bin Yang | H. Yamada | Isao Kuwajima | G. Lambard | Kenta Hongo | Yibin Xu | Junichiro Shiomi | Christoph Schick | Junko Morikawa | Ryo Yoshida
Source: npj Computational Materials (article, 2019-06-21)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm Abstract The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics. Polymer ; Monomer ; Thermal conductivity ; Materials science ; Polymerization ; Computer science ; Nanotechnology ; Algorithm ; Machine learning ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Linfeng Zhang | De-Ye Lin | Han Wang | Roberto Car | Weinan E
Source: Physical Review Materials (article, 2019-02-25)
Field: Materials Science — Materials Chemistry

Active learning of uniformly accurate interatomic potentials for materials simulation An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. Generator (circuit theory) ; Interatomic potential ; Surface (topology) ; Active learning (machine learning) ; Energy (signal processing) ; Molecular dynamics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Electron Microscopy Techniques and Applications