jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adam S. Braegelman | Matthew J. Webber
Source: Theranostics (review, 2019-01-01)
Field: Materials Science — Biomaterials

Integrating Stimuli-Responsive Properties in Host-Guest Supramolecular Drug Delivery Systems Host-guest motifs are likely the most recognizable manifestation of supramolecular chemistry. These complexes are characterized by the organization of small molecules on the basis of preferential association of a guest within the portal of a host. In the context of their therapeutic use, the primary application of these complexes has been as excipients which enhance the solubility or improve the stability of drug formulations, primarily in a vial. However, there may be opportunities to go significantly beyond such a role and leverage key features of the affinity, specificity, and dynamics of the interaction itself toward "smarter" therapeutic designs. One approach in this regard would seek stimuli-responsive host-guest recognition, wherein a complex forms in a manner that is sensitive to, or can be governed by, externally applied triggers, disease-specific proteins and analytes, or the presence of a competing guest. This review will highlight the general and phenomenological design considerations governing host-guest recognition and the specific types of chemistry which have been used and are available for different applications. Finally, a discussion of the molecular engineering and design approaches which enable sensitivity to a variety of different stimuli are highlighted. Ultimately, these molecular-scale approaches offer an assortment of new chemistry and material design tools toward improving precision in drug delivery. Supramolecular chemistry ; Molecular recognition ; Drug delivery ; Nanotechnology ; Leverage (statistics) ; Context (archaeology) ; Computer science ; Chemistry ; Host (biology) ; Combinatorial chemistry ; Biochemical engineering ; Computational biology ; Materials science ; Molecule ; Artificial intelligence ; Biology ; Organic chemistry ; Engineering Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: Ling Zhang | Libo Li | Enlai Hu | Ling Yang | Kai Shao | Lijia Yao | Ke Jiang | Yuanjing Cui | Yu Yang | Bin Li | Banglin Chen | Guodong Qian
Source: Advanced Science (article, 2019-11-25)
Field: Chemistry — Inorganic Chemistry

Boosting Ethylene/Ethane Separation within Copper(I)‐Chelated Metal–Organic Frameworks through Tailor‐Made Aperture and Specific π‐Complexation The development of new materials for separating ethylene (C<sub>2</sub>H<sub>4</sub>) from ethane (C<sub>2</sub>H<sub>6</sub>) by adsorption is of great importance in the petrochemical industry, but remains very challenging owing to their close molecular sizes and physical properties. Using isoreticular chemistry in metal-organic frameworks (MOFs) enables the precise design and construction of target materials with suitable aperture sizes and functional sites for gas separations. Herein, it is described that fine-tuning of pore size and π-complexation simultaneously in microporous copper(I)-chelated MOFs can remarkably boost the C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> adsorption selectivity. The judicious choice of organic linkers with a different number of carboxyl groups in the UiO-66 framework not only allows the fine tuning of the pore size but also immobilizes copper(I) ions onto the framework. The tailor-made adsorbent, Cu<sup>I</sup>@UiO-66-(COOH)<sub>2</sub>, thus possesses the optimal pore window size and chelated Cu(I) ions to form π-complexation with C<sub>2</sub>H<sub>4</sub> molecules. It can rapidly adsorb C<sub>2</sub>H<sub>4</sub> driven by the strong π-complexation interactions, while effectively reducing C<sub>2</sub>H<sub>6</sub> uptake due to the selective size-sieving. Therefore, this material exhibits an ultrahigh C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> selectivity (80.8), outperforming most previously described benchmark materials. The exceptional separation performance of Cu<sup>I</sup>@UiO-66-(COOH)<sub>2</sub> is validated by breakthrough experiments for 50/50 v/v C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub> mixtures under ambient conditions. Selectivity ; Microporous material ; Adsorption ; Copper ; Metal-organic framework ; Ethylene ; Chelation ; Metal ions in aqueous solution ; Chemistry ; Molecule ; Petrochemical ; Metal ; Chemical engineering ; Materials science ; Inorganic chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Pengyan Wu | Yang Li | Jia‐Jia Zheng | Nobuhiko Hosono | Ken‐ichi Otake | Jian Wang | Yanhong Liu | Lingling Xia | Min Jiang | Shigeyoshi Sakaki | Susumu Kitagawa
Source: Nature Communications (article, 2019-09-25)
Field: Chemistry — Inorganic Chemistry

Carbon dioxide capture and efficient fixation in a dynamic porous coordination polymer Direct structural information of confined CO<sub>2</sub> in a micropore is important for elucidating its specific binding or activation mechanism. However, weak gas-binding ability and/or poor sample crystallinity after guest exchange hindered the development of efficient materials for CO<sub>2</sub> incorporation, activation and conversion. Here, we present a dynamic porous coordination polymer (PCP) material with local flexibility, in which the propeller-like ligands rotate to permit CO<sub>2</sub> trapping. This process can be characterized by X-ray structural analysis. Owing to its high affinity towards CO<sub>2</sub> and the confinement effect, the PCP exhibits high catalytic activity, rapid transformation dynamics, even high size selectivity to different substrates. Together with an excellent stability with turnover numbers (TON) of up to 39,000 per Zn<sub>1.5</sub> cluster of catalyst after 10 cycles for CO<sub>2</sub> cycloaddition to form value-added cyclic carbonates, these results demonstrate that such distinctive structure is responsible for visual CO<sub>2</sub> capture and size-selective conversion. Crystallinity ; Microporous material ; Carbon fixation ; Catalysis ; Porosity ; Polymer ; Carbon dioxide ; Selectivity ; Materials science ; Chemical engineering ; Cycloaddition ; Flexibility (engineering) ; Porous medium ; Chemistry ; Nanotechnology ; Crystallography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Yehia Amar | Artur M. Schweidtmann | Paul Deutsch | Liwei Cao | Alexei A. Lapkin
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)<sub>2</sub>(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories. Selection (genetic algorithm) ; Catalysis ; Solvent ; Computer science ; Chemistry ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Minwang Laipan | Lichen Xiang | Jingfang Yu | Benjamin Martin | Runliang Zhu | Jianxi Zhu | Hongping He | Abraham Clearfield | Luyi Sun
Source: Progress in Materials Science (article, 2019-12-10)
Field: Materials Science — Materials Chemistry

Layered intercalation compounds: Mechanisms, new methodologies, and advanced applications Intercalation (chemistry) ; Materials science ; Scope (computer science) ; Nanotechnology ; Field (mathematics) ; Computer science ; Chemistry ; Inorganic chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Ekin D. Cubuk | Austin D. Sendek | Evan J. Reed
Source: The Journal of Chemical Physics (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding. Computer science ; Lithium (medication) ; Artificial intelligence ; Machine learning ; Scale (ratio) ; Transfer of learning ; Database Machine Learning in Materials Science ; Advancements in Battery Materials ; Machine Learning and Algorithms
Authors: Marta Glavatskikh | Jules Leguy | Gilles Hunault | Thomas Cauchy | Benoit Da Mota
Source: Journal of Cheminformatics (article, 2019-11-12)
Field: Materials Science — Materials Chemistry

Dataset’s chemical diversity limits the generalizability of machine learning predictions The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations. Such ML models need to be tested and generalized on real data. PC9, a new QM9 equivalent dataset (only H, C, N, O and F and up to 9 "heavy" atoms) of the PubChemQC project is presented in this article. A statistical study of bonding distances and chemical functions shows that this new dataset encompasses more chemical diversity. Kernel Ridge Regression, Elastic Net and the Neural Network model provided by SchNet have been used on both datasets. The overall accuracy in energy prediction is higher for the QM9 subset. However, a model trained on PC9 shows a stronger ability to predict energies of the other dataset. Generalizability theory ; Chemical space ; Computer science ; Machine learning ; Artificial neural network ; Artificial intelligence ; Ridge ; Regression ; Data mining ; Mathematics ; Bioinformatics ; Statistics ; Drug discovery ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Christoph Schran | Jörg Behler | Dominik Marx
Source: Journal of Chemical Theory and Computation (article, 2019-11-19)
Field: Materials Science — Materials Chemistry

Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this requirement. Here, we show how high-dimensional neural network potentials can be employed to automatically generate the potential energy surface of finite sized clusters at coupled cluster accuracy, namely CCSD(T*)-F12a/aug-cc-pVTZ. The developed automated procedure utilizes the established intrinsic properties of the model such that the configurations for the training set are selected in an unbiased and efficient way to minimize the computational effort of expensive reference calculations. These ideas are applied to protonated water clusters from the hydronium cation, H<sub>3</sub>O<sup>+</sup>, up to the tetramer, H<sub>9</sub>O<sub>4</sub><sup>+</sup>, and lead to a single potential energy surface that describes all these systems at essentially converged coupled cluster accuracy with a fitting error of 0.06 kJ/mol per atom. The fit is validated in detail for all clusters up to the tetramer and yields reliable results not only for stationary points but also for reaction pathways and intermediate configurations as well as different sampling techniques. Per design, the neural network potentials (NNPs) constructed in this fashion can handle very different conditions including the quantum nature of the nuclei and enhanced sampling techniques covering very low as well as high temperatures. This enables fast and exhaustive exploration of the targeted protonated water clusters with essentially converged interactions. In addition, the automated process will allow one to tackle finite systems much beyond the present case. Cluster (spacecraft) ; Computer science ; Artificial neural network ; Coupled cluster ; Potential energy surface ; Ab initio ; Protonation ; Set (abstract data type) ; Algorithm ; Statistical physics ; Biological system ; Artificial intelligence ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Mihály Váradi | John M. Berrisford | Mandar Deshpande | Sreenath Nair | Aleksandras Gutmanas | David Armstrong | Lukáš Pravda | Bissan Al‐Lazikani | Stephen Anyango | Geoffrey J. Barton | Karel Berka | Tom L. Blundell | Neera Borkakoti | Jose M Dana | Sayoni Das | Sucharita Dey | Patrizio Di Micco | Franca Fraternali | Toby J. Gibson | Manuela Helmer‐Citterich | David Hoksza | Liang‐Chin Huang | Rishabh Jain | Harry Jubb | Christos Kannas | Natarajan Kannan | Jaroslav Koča | Radoslav Krivák | Manjeet Kumar | Emmanuel D. Levy | Fábio Madeira | M. S. Madhusudhan | Henry J. Martell | Stuart A. MacGowan | Jake E McGreig | Saqib Mir | Abhik Mukhopadhyay | Luca Parca | Typhaine Paysan‐Lafosse | Leandro Radusky | António J. M. Ribeiro | Luis Serrano | Ian Sillitoe | Gulzar Singh | Petr Škoda | Radka Svobodová Vařeková | Jonathan D. Tyzack | Alfonso Valencia | Eloy Villasclaras Fernandez | Wim Vranken | Mark N. Wass | Janet M. Thornton | Michael J.E. Sternberg | Christine Orengo | Sameer Velankar
Source: Nucleic Acids Research (article, 2019-10-01)
Field: Chemistry — Spectroscopy

PDBe-KB: a community-driven resource for structural and functional annotations The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession. UniProt ; Protein Data Bank ; Interoperability ; Collaboratory ; Context (archaeology) ; Protein Data Bank (RCSB PDB) ; Structural bioinformatics ; Computer science ; Resource (disambiguation) ; Data curation ; Ensembl ; Biology ; World Wide Web ; Information retrieval ; Data science ; Protein structure Advanced Proteomics Techniques and Applications ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Shuguang Wang | Zhongwu Wang | Jie Li | Liqiang Li | Wenping Hu
Source: Materials Chemistry Frontiers (article, 2019-12-02)
Field: Materials Science — Polymers and Plastics

Surface-grafting polymers: from chemistry to organic electronics This review comprehensively summarizes the recent progress in surface-grafting polymers, including their formation process and the utilization of surface-grafting polymers as functional materials of insulators, conductors and semiconductors in versatile organic electronic devices. Polymer ; Grafting ; Nanotechnology ; Materials science ; Electronics ; Conductive polymer ; Electronic materials ; Organic electronics ; Semiconductor ; Polymer science ; Chemistry ; Transistor ; Optoelectronics ; Composite material ; Physical chemistry ; Engineering ; Electrical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Paulina Jacek | Fernando Dourado | Miguel Gama | Stanisław Bielecki
Source: Microbial Biotechnology (review, 2019-03-18)
Field: Materials Science — Biomaterials

Molecular aspects of bacterial nanocellulose biosynthesis Bacterial nanocellulose (BNC) produced by aerobic bacteria is a biopolymer with sophisticated technical properties. Although the potential for economically relevant applications is huge, the cost of BNC still limits its application to a few biomedical devices and the edible product Nata de Coco, made available by traditional fermentation methods in Asian countries. Thus, a wider economic relevance of BNC is still dependent on breakthrough developments on the production technology. On the other hand, the development of modified strains able to overproduce BNC with new properties - e.g. porosity, density of fibres crosslinking, mechanical properties, etc. - will certainly allow to overcome investment and cost production issues and enlarge the scope of BNC applications. This review discusses current knowledge about the molecular basis of BNC biosynthesis, its regulations and, finally, presents a perspective on the genetic modification of BNC producers made possible by the new tools available for genetic engineering. Nanocellulose ; Biochemical engineering ; Biotechnology ; Synthetic biology ; Production (economics) ; Metabolic engineering ; Nanotechnology ; Computer science ; Business ; Computational biology ; Biology ; Engineering ; Materials science ; Biochemistry ; Cellulose ; Economics Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Enzyme Production and Characterization
Authors: Yuan Dong | Chuhan Wu | Chi Zhang | Yingda Liu | Jianlin Cheng | Jian Lin
Source: npj Computational Materials (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride Abstract It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R 2 of &gt;90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales. Graphene ; Band gap ; Materials science ; Supercell ; Boron nitride ; Dopant ; Convolutional neural network ; Artificial intelligence ; Ab initio ; Nanoscopic scale ; Materials informatics ; Machine learning ; Computer science ; Nanotechnology ; Optoelectronics ; Doping ; Physics ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices