jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jayanta Kumar Patra | Gitishree Das | Leonardo Fernandes Fraceto | Estefânia Vangelie Ramos Campos | María del Pilar Rodríguez-Torres | Laura Susana Acosta‐Torres | L.A. Dı́az-Torres | Renato Grillo | Mallappa Kumara Swamy | Shivesh Sharma | Solomon Habtemariam | Han-Seung Shin
Source: Journal of Nanobiotechnology (review, 2018-09-19)
Field: Materials Science — Biomaterials

Nano based drug delivery systems: recent developments and future prospects Nanomedicine and nano delivery systems are a relatively new but rapidly developing science where materials in the nanoscale range are employed to serve as means of diagnostic tools or to deliver therapeutic agents to specific targeted sites in a controlled manner. Nanotechnology offers multiple benefits in treating chronic human diseases by site-specific, and target-oriented delivery of precise medicines. Recently, there are a number of outstanding applications of the nanomedicine (chemotherapeutic agents, biological agents, immunotherapeutic agents etc.) in the treatment of various diseases. The current review, presents an updated summary of recent advances in the field of nanomedicines and nano based drug delivery systems through comprehensive scrutiny of the discovery and application of nanomaterials in improving both the efficacy of novel and old drugs (e.g., natural products) and selective diagnosis through disease marker molecules. The opportunities and challenges of nanomedicines in drug delivery from synthetic/natural sources to their clinical applications are also discussed. In addition, we have included information regarding the trends and perspectives in nanomedicine area. Nanomedicine ; Drug delivery ; Nanotechnology ; Medicine ; Computer science ; Risk analysis (engineering) ; Materials science Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Nanoparticles: synthesis and applications
Authors: Keith T. Butler | Daniel W. Davies | Hugh Cartwright | Olexandr Isayev | Aron Walsh
Source: Nature (review, 2018-07-01)
Field: Materials Science — Materials Chemistry

Machine learning for molecular and materials science Computer science ; Field (mathematics) ; Data science ; Characterization (materials science) ; Domain (mathematical analysis) ; Artificial intelligence ; Cognitive science ; Nanotechnology ; Machine learning ; Psychology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Rafael Gómez-Bombarelli | Jennifer N. Wei | David Duvenaud | José Miguel Hernández-Lobato | Benjamín Sánchez-Lengeling | Dennis Sheberla | Jorge Aguilera-Iparraguirre | Timothy D. Hirzel | Ryan P. Adams | Alán Aspuru-Guzik
Source: ACS Central Science (article, 2018-01-12)
Field: Materials Science — Materials Chemistry

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms. Representation (politics) ; Encoder ; Set (abstract data type) ; Simple (philosophy) ; Chemical space ; Continuous modelling ; Chemical process ; Continuous optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Tian Xie | Jeffrey C. Grossman
Source: Physical Review Letters (article, 2018-04-06)
Field: Materials Science — Materials Chemistry

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10^{4} data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design. Computer science ; Convolutional neural network ; Crystal (programming language) ; Representation (politics) ; Graph ; Crystal structure ; Graph rewriting ; Artificial neural network ; Crystal structure prediction ; Atom (system on chip) ; Transformation (genetics) ; Artificial intelligence ; Theoretical computer science ; Algorithm ; Chemistry ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Kristof T. Schütt | Huziel E. Sauceda | Pieter-Jan Kindermans | Alexandre Tkatchenko | K. Müller
Source: The Journal of Chemical Physics (article, 2018-03-29)
Field: Materials Science — Materials Chemistry

SchNet – A deep learning architecture for molecules and materials Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C<sub>20</sub>-fullerene that would have been infeasible with regular ab initio molecular dynamics. Deep learning ; Chemical space ; Computer science ; Artificial intelligence ; Ab initio ; Molecular dynamics ; Architecture ; Space (punctuation) ; Quantum ; Physics ; Quantum mechanics ; Bioinformatics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Linfeng Zhang | Jiequn Han | Handong Wang | Roberto Car | E Weinan
Source: Physical Review Letters (article, 2018-04-04)
Field: Materials Science — Materials Chemistry

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size. Molecular dynamics ; Quantum dynamics ; Statistical physics ; Dynamics (music) ; Physics ; Quantum ; Quantum mechanics ; Classical mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Quantum, superfluid, helium dynamics
Authors: Sudipta Senapati | Arun Kumar Mahanta | Sunil Kumar | Pralay Maiti
Source: Signal Transduction and Targeted Therapy (article, 2018-03-14)
Field: Materials Science — Biomaterials

Controlled drug delivery vehicles for cancer treatment and their performance Although conventional chemotherapy has been successful to some extent, the main drawbacks of chemotherapy are its poor bioavailability, high-dose requirements, adverse side effects, low therapeutic indices, development of multiple drug resistance, and non-specific targeting. The main aim in the development of drug delivery vehicles is to successfully address these delivery-related problems and carry drugs to the desired sites of therapeutic action while reducing adverse side effects. In this review, we will discuss the different types of materials used as delivery vehicles for chemotherapeutic agents and their structural characteristics that improve the therapeutic efficacy of their drugs and will describe recent scientific advances in the area of chemotherapy, emphasizing challenges in cancer treatments. Drug ; Medicine ; Drug delivery ; Chemotherapy ; Adverse effect ; Cancer chemotherapy ; Intensive care medicine ; Pharmacology ; Cancer ; Drug resistance ; Internal medicine ; Biology ; Nanotechnology Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; RNA Interference and Gene Delivery
Authors: Benjamín Sánchez-Lengeling | Alán Aspuru‐Guzik
Source: Science (review, 2018-07-26)
Field: Materials Science — Materials Chemistry

Inverse molecular design using machine learning: Generative models for matter engineering The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials. Generative grammar ; Context (archaeology) ; Computer science ; Variety (cybernetics) ; Pace ; Artificial intelligence ; Generative Design ; Biochemical engineering ; Machine learning ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Han Wang | Linfeng Zhang | Jiequn Han | E Weinan
Source: Computer Physics Communications (article, 2018-03-21)
Field: Materials Science — Materials Chemistry

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics Molecular dynamics ; Representation (politics) ; Computer science ; Statistical physics ; Dynamics (music) ; Energy (signal processing) ; Computational science ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Maria S. Lohse | Thomas Bein
Source: Advanced Functional Materials (article, 2018-07-08)
Field: Materials Science — Materials Chemistry

Covalent Organic Frameworks: Structures, Synthesis, and Applications Abstract Covalent organic frameworks (COFs) are crystalline porous polymers formed by a bottom‐up approach from molecular building units having a predesigned geometry that are connected through covalent bonds. They offer positional control over their building blocks in two and three dimensions. This control enables the synthesis of rigid porous structures with a high regularity and the ability to fine‐tune the chemical and physical properties of the network. This Feature Article provides a comprehensive overview over the structures realized to date in the fast growing field of covalent organic framework development. Different synthesis strategies to meet diverse demands, such as high crystallinity, straightforward processability, or the formation of thin films are discussed. Furthermore, insights into the growing fields of COF applications, including gas storage and separations, sensing, electrochemical energy storage, and optoelectronics are provided. Covalent bond ; Materials science ; Nanotechnology ; Crystallinity ; Porosity ; Polymer ; Covalent organic framework ; Electrochemical energy storage ; Porous medium ; Electrochemistry ; Organic chemistry ; Supercapacitor ; Composite material ; Chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: David C. Blakemore | Luis C. Misal Castro | Ian Churcher | David C. Rees | Andrew W. Thomas | David M. Wilson | Anthony Wood
Source: Nature Chemistry (review, 2018-03-22)
Field: Chemistry — Organic Chemistry

Organic synthesis provides opportunities to transform drug discovery Drug discovery ; Chemistry ; Organic synthesis ; Limiting ; Nanotechnology ; Biochemical engineering ; Organic molecules ; Chemical space ; Combinatorial chemistry ; Organic chemistry ; Molecule ; Catalysis ; Engineering Catalytic C–H Functionalization Methods ; Chemical Synthesis and Analysis ; Radical Photochemical Reactions
Authors: Kanit Hantanasirisakul | Yury Gogotsi
Source: Advanced Materials (review, 2018-11-19)
Field: Materials Science — Materials Chemistry

Electronic and Optical Properties of 2D Transition Metal Carbides and Nitrides (MXenes) 2D transition metal carbides, carbonitrides, and nitrides, known as MXenes, are a rapidly growing family of 2D materials with close to 30 members experimentally synthesized, and dozens more studied theoretically. They exhibit outstanding electronic, optical, mechanical, and thermal properties with versatile transition metal and surface chemistries. They have shown promise in many applications, such as energy storage, electromagnetic interference shielding, transparent electrodes, sensors, catalysis, photothermal therapy, etc. The high electronic conductivity and wide range of optical absorption properties of MXenes are the key to their success in the aforementioned applications. However, relatively little is currently known about their fundamental electronic and optical properties, limiting their use to their full potential. Here, MXenes' electronic and optical properties from both theoretical and experimental perspectives, as well as applications related to those properties, are discussed, providing a guide for researchers who are exploring those properties of MXenes. MXenes ; Materials science ; Carbide ; Nitride ; Transition metal ; Metal ; Nanotechnology ; Metallurgy ; Layer (electronics) MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Shihui Wen | Jiajia Zhou | Kezhi Zheng | Artur Bednarkiewicz | Xiaogang Liu | Dayong Jin
Source: Nature Communications (review, 2018-06-14)
Field: Materials Science — Materials Chemistry

Advances in highly doped upconversion nanoparticles Lanthanide-doped upconversion nanoparticles (UCNPs) are capable of converting near-infra-red excitation into visible and ultraviolet emission. Their unique optical properties have advanced a broad range of applications, such as fluorescent microscopy, deep-tissue bioimaging, nanomedicine, optogenetics, security labelling and volumetric display. However, the constraint of concentration quenching on upconversion luminescence has hampered the nanoscience community to develop bright UCNPs with a large number of dopants. This review surveys recent advances in developing highly doped UCNPs, highlights the strategies that bypass the concentration quenching effect, and discusses new optical properties as well as emerging applications enabled by these nanoparticles. Photon upconversion ; Nanotechnology ; Materials science ; Nanomedicine ; Nanoparticle ; Quenching (fluorescence) ; Dopant ; Doping ; Fluorescence ; Luminescence ; Optoelectronics ; Optics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Perovskite Materials and Applications
Authors: Li Ding | Yanying Wei | Libo Li | Tao Zhang | Haihui Wang | Jian Xue | Liang-Xin Ding | Suqing Wang | Jürgen Caro | Yury Gogotsi
Source: Nature Communications (article, 2018-01-05)
Field: Materials Science — Materials Chemistry

MXene molecular sieving membranes for highly efficient gas separation Molecular sieving membranes with sufficient and uniform nanochannels that break the permeability-selectivity trade-off are desirable for energy-efficient gas separation, and the arising two-dimensional (2D) materials provide new routes for membrane development. However, for 2D lamellar membranes, disordered interlayer nanochannels for mass transport are usually formed between randomly stacked neighboring nanosheets, which is obstructive for highly efficient separation. Therefore, manufacturing lamellar membranes with highly ordered nanochannel structures for fast and precise molecular sieving is still challenging. Here, we report on lamellar stacked MXene membranes with aligned and regular subnanometer channels, taking advantage of the abundant surface-terminating groups on the MXene nanosheets, which exhibit excellent gas separation performance with H<sub>2</sub> permeability >2200 Barrer and H<sub>2</sub>/CO<sub>2</sub> selectivity >160, superior to the state-of-the-art membranes. The results of molecular dynamics simulations quantitatively support the experiments, confirming the subnanometer interlayer spacing between the neighboring MXene nanosheets as molecular sieving channels for gas separation. Membrane ; Lamellar structure ; Gas separation ; Materials science ; Barrer ; Selectivity ; Molecular dynamics ; Nanotechnology ; Chemical engineering ; Permeability (electromagnetism) ; Chemistry ; Catalysis ; Computational chemistry ; Composite material ; Organic chemistry MXene and MAX Phase Materials ; Graphene research and applications ; Membrane Separation and Gas Transport
Authors: Derek T. Ahneman | Jesús G. Estrada | Shishi Lin | Spencer D. Dreher | Abigail G. Doyle
Source: Science (article, 2018-02-15)
Field: Chemistry — Organic Chemistry

Predicting reaction performance in C–N cross-coupling using machine learning Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. We demonstrated that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives. Using these descriptors as inputs and reaction yield as output, we showed that a random forest algorithm provides significantly improved predictive performance over linear regression analysis. The random forest model was also successfully applied to sparse training sets and out-of-sample prediction, suggesting its value in facilitating adoption of synthetic methodology. Coupling (piping) ; Artificial intelligence ; Computer science ; Machine learning ; Chemistry ; Materials science ; Composite material Catalytic Cross-Coupling Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalytic C–H Functionalization Methods
Authors: Anna Sorushanova | Luis M. Delgado | Zhuning Wu | Naledi Shologu | Aniket Kshirsagar | Rufus Raghunath | Anne Maria Mullen | Yves Bayon | Abhay Pandit | Michael Raghunath | Dimitrios I. Zeugolis
Source: Advanced Materials (review, 2018-08-20)
Field: Materials Science — Biomaterials

The Collagen Suprafamily: From Biosynthesis to Advanced Biomaterial Development Collagen is the oldest and most abundant extracellular matrix protein that has found many applications in food, cosmetic, pharmaceutical, and biomedical industries. First, an overview of the family of collagens and their respective structures, conformation, and biosynthesis is provided. The advances and shortfalls of various collagen preparations (e.g., mammalian/marine extracted collagen, cell-produced collagens, recombinant collagens, and collagen-like peptides) and crosslinking technologies (e.g., chemical, physical, and biological) are then critically discussed. Subsequently, an array of structural, thermal, mechanical, biochemical, and biological assays is examined, which are developed to analyze and characterize collagenous structures. Lastly, a comprehensive review is provided on how advances in engineering, chemistry, and biology have enabled the development of bioactive, 3D structures (e.g., tissue grafts, biomaterials, cell-assembled tissue equivalents) that closely imitate native supramolecular assemblies and have the capacity to deliver in a localized and sustained manner viable cell populations and/or bioactive/therapeutic molecules. Clearly, collagens have a long history in both evolution and biotechnology and continue to offer both challenges and exciting opportunities in regenerative medicine as nature's biomaterial of choice. Biomaterial ; Materials science ; Biosynthesis ; Nanotechnology ; Polymer science ; Biochemistry ; Biology ; Enzyme Collagen: Extraction and Characterization ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials