jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Markus J. Kalmutzki | Nikita Hanikel | Omar M. Yaghi
Source: Science Advances (review, 2018-10-05)
Field: Chemistry — Inorganic Chemistry

Secondary building units as the turning point in the development of the reticular chemistry of MOFs The secondary building unit (SBU) approach was a turning point in the discovery of permanently porous metal-organic frameworks (MOFs) and in launching the field of reticular chemistry. In contrast to the single-metal nodes known in coordination networks, the polynuclear nature of SBUs allows these structures to serve as rigid, directional, and stable building units in the design of robust crystalline materials with predetermined structures and properties. This concept has also enabled the development of MOFs with ultra-high porosity and structural complexity. The architectural, mechanical, and chemical stability of MOFs imparted by their SBUs also gives rise to unique framework chemistry. All of this chemistry -including ligand, linker, metal exchange, and metallation reactions, as well as precisely controlled formation of ordered vacancies- is carried out with full retention of the MOF structure, crystallinity, and porosity. The unique chemical nature of SBUs makes MOFs useful in many applications including gas and vapor adsorption, separation processes, and SBU-mediated catalysis. In essence, the SBU approach realizes a long-standing dream of scientists by bringing molecular chemistry (both organic and inorganic) to extended solid-state structures. This contribution highlights the importance of the SBUs in the development of MOFs and points to the tremendous potential still to be harnessed. SBus ; Porosity ; Metal-organic framework ; Nanotechnology ; Crystallinity ; Materials science ; Reticular connective tissue ; Chemistry ; Adsorption ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Machine Learning in Materials Science
Authors: Chris Holland | Keiji Numata | Jelena Rnjak‐Kovacina | F. Philipp Seib
Source: Advanced Healthcare Materials (review, 2018-09-20)
Field: Materials Science — Biomaterials

The Biomedical Use of Silk: Past, Present, Future Humans have long appreciated silk for its lustrous appeal and remarkable physical properties, yet as the mysteries of silk are unraveled, it becomes clear that this outstanding biopolymer is more than a high-tech fiber. This progress report provides a critical but detailed insight into the biomedical use of silk. This journey begins with a historical perspective of silk and its uses, including the long-standing desire to reverse engineer silk. Selected silk structure-function relationships are then examined to appreciate past and current silk challenges. From this, biocompatibility and biodegradation are reviewed with a specific focus of silk performance in humans. The current clinical uses of silk (e.g., sutures, surgical meshes, and fabrics) are discussed, as well as clinical trials (e.g., wound healing, tissue engineering) and emerging biomedical applications of silk across selected formats, such as silk solution, films, scaffolds, electrospun materials, hydrogels, and particles. The journey finishes with a look at the roadmap of next-generation recombinant silks, especially the development pipeline of this new industry for clinical use. SILK ; Nanotechnology ; Fibroin ; Self-healing hydrogels ; Biocompatibility ; Textile ; Polymer science ; Biopolymer ; Materials science ; Composite material ; Polymer chemistry Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Silkworms and Sericulture Research
Authors: Daniel P. Tabor | Loı̈c M. Roch | Semion K. Saikin | Christoph Kreisbeck | Dennis Sheberla | Joseph H. Montoya | Shyam Dwaraknath | Muratahan Aykol | C. Ortiz | Hermann Tribukait | Carlos Amador‐Bedolla | Christoph J. Brabec | Benji Maruyama | Kristin A. Persson | Alán Aspuru‐Guzik
Source: Nature Reviews Materials (review, 2018-04-26)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of materials for clean energy in the era of smart automation The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development. Software deployment ; Pace ; Automation ; Robotics ; Computer science ; Throughput ; Artificial intelligence ; Manufacturing engineering ; Systems engineering ; Nanotechnology ; Risk analysis (engineering) ; Robot ; Engineering ; Telecommunications ; Software engineering ; Mechanical engineering ; Business Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalysis and Oxidation Reactions
Authors: Jarosław M. Granda | Liva Donina | Vincenza Dragone | De‐Liang Long | Leroy Cronin
Source: Nature (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Controlling an organic synthesis robot with machine learning to search for new reactivity Reactivity (psychology) ; Computer science ; Artificial intelligence ; Machine learning ; Chemical space ; Chemical reaction ; Robot ; Reagent ; Chemistry ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Various Chemistry Research Topics
Authors: Markus J. Kalmutzki | Christian S. Diercks | Omar M. Yaghi
Source: Advanced Materials (review, 2018-04-19)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks for Water Harvesting from Air Water harvesting from air in passive, adsorption-based devices holds great potential for delivering drinking water to arid regions of the world. This technology requires adsorbents that can be tailored for a maximum working capacity, temperature response, and the relative pressure range in which reversible adsorption occurs. In this respect, metal-organic frameworks (MOFs) are promising, owing to their structural diversity and the precision of their functionalization for adjusting both pore size and hydrophilicity, thereby facilitating the rational design of their water-sorption characteristics. Here, chemical and structural factors crucial for the design of hydrolytically stable MOFs for water adsorption are discussed. Prevalent water adsorption mechanisms in micro- and mesoporous MOFs alongside strategies for fine-tuning of their adsorption behavior by means of reticular chemistry are presented. Finally, an approach for the selection of promising MOFs with respect to water harvesting from air is proposed and design concepts for next-generation MOFs for application in passive adsorption-based water-harvesting devices are outlined. Adsorption ; Materials science ; Metal-organic framework ; Rational design ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation Technologies
Authors: Christian S. Diercks | Yuzhong Liu | Kyle E. Cordova | Omar M. Yaghi
Source: Nature Materials (review, 2018-02-23)
Field: Chemistry — Inorganic Chemistry

The role of reticular chemistry in the design of CO2 reduction catalysts Reticular connective tissue ; Selectivity ; Nanotechnology ; Chemistry ; Reduction (mathematics) ; Catalysis ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; CO2 Reduction Techniques and Catalysts ; Covalent Organic Framework Applications
Authors: Ying Zhang | Chen Ling
Source: npj Computational Materials (article, 2018-05-08)
Field: Materials Science — Materials Chemistry

A strategy to apply machine learning to small datasets in materials science Abstract There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision–DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset. Machine learning ; Artificial intelligence ; Computer science ; Generality ; Predictive modelling ; Property (philosophy) ; Feature (linguistics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Wengong Jin | Regina Barzilay | Tommi Jaakkola
Source: arXiv (Cornell University) (preprint, 2018-02-12)
Field: Materials Science — Materials Chemistry

Junction Tree Variational Autoencoder for Molecular Graph Generation We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin. Autoencoder ; Graph ; Tree (set theory) ; Computer science ; Theoretical computer science ; Combinatorics ; Artificial intelligence ; Algorithm ; Mathematics ; Deep learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Stefano Dissegna | Konstantin Epp | Werner R. Heinz | Gregor Kieslich | Roland A. Fischer
Source: Advanced Materials (review, 2018-01-24)
Field: Chemistry — Inorganic Chemistry

Defective Metal‐Organic Frameworks The targeted incorporation of defects into crystalline matter allows for the manipulation of many properties and has led to relevant discoveries for optimized and even novel technological applications of materials. It is therefore exciting to see that defects are now recognized to be similarly useful in tailoring properties of metal-organic frameworks (MOFs). For instance, heterogeneous catalysis crucially depends on the number of active catalytic sites as well as on diffusion limitations. By the incorporation of missing linker and missing node defects into MOFs, both parameters can be accessed, improving the catalytic properties. Furthermore, the creation of defects allows for adding properties such as electronic conductivity, which are inherently absent in the parent MOFs. Herein, progress of the rapidly evolving field of the past two years is overviewed, putting a focus on properties that are altered by the incorporation and even tailoring of defects in MOFs. A brief account is also given on the emerging quantitative understanding of defects and heterogeneity in MOFs based on scale-bridging computational modeling and simulations. Metal-organic framework ; Materials science ; Nanotechnology ; Bridging (networking) ; Linker ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Ying Wang | Bingbing Zhang | Zhihua Yang | Shilie Pan
Source: Angewandte Chemie International Edition (article, 2018-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cation‐Tuned Synthesis of Fluorooxoborates: Towards Optimal Deep‐Ultraviolet Nonlinear Optical Materials Abstract The development of new nonlinear optical (NLO) materials for deep‐ultraviolet (DUV) applications is in great demand. However, the synthesis of an ideal DUV NLO crystal is a serious challenge. Herein, three new alkali‐metal fluorooxoborates, AB 4 O 6 F (A=K, Rb, and Cs, and a mixed cation between two of them), were successfully synthesized by cation regulation. It is found that all reported compounds exhibit short UV absorption edges (&lt;190 nm), and show second harmonic generation (SHG) responses ranging from 0.8 to 1.9 KH 2 PO 4 (KDP). Interestingly, by judicious selection of the A‐site alkali‐metal cations, the arrangement of NLO‐active structural units is fine‐tuned to an optimal configuration, which contributes to large SHG responses. Ultraviolet ; Alkali metal ; Nonlinear optical ; Second-harmonic generation ; Nonlinear optics ; Materials science ; Absorption (acoustics) ; Crystal (programming language) ; Ideal (ethics) ; Metal ; Optoelectronics ; Chemistry ; Nanotechnology ; Optics ; Nonlinear system ; Organic chemistry ; Physics ; Laser Crystal Structures and Properties ; Inorganic Fluorides and Related Compounds ; Luminescence Properties of Advanced Materials
Authors: Connor W. Coley | Wengong Jin | Luke Rogers | Timothy F. Jamison | Tommi Jaakkola | William H. Green | Regina Barzilay | Klavs F. Jensen
Source: Chemical Science (article, 2018-11-26)
Field: Materials Science — Materials Chemistry

A graph-convolutional neural network model for the prediction of chemical reactivity We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions <i>via</i> the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches. Reagent ; Graph ; Reactivity (psychology) ; Computer science ; Convolutional neural network ; Artificial intelligence ; Artificial neural network ; Organic solvent ; Machine learning ; Chemistry ; Biochemical engineering ; Organic chemistry ; Theoretical computer science ; Engineering ; Chemical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Itzik Malkiel | Michael Mrejen | Achiya Nagler | Uri Arieli | Lior Wolf | Haim Suchowski
Source: Light Science & Applications (article, 2018-09-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic nanostructure design and characterization via Deep Learning Nanophotonics, the field that merges photonics and nanotechnology, has in recent years revolutionized the field of optics by enabling the manipulation of light-matter interactions with subwavelength structures. However, despite the many advances in this field, the design, fabrication and characterization has remained widely an iterative process in which the designer guesses a structure and solves the Maxwell's equations for it. In contrast, the inverse problem, i.e., obtaining a geometry for a desired electromagnetic response, remains a challenging and time-consuming task within the boundaries of very specific assumptions. Here, we experimentally demonstrate that a novel Deep Neural Network trained with thousands of synthetic experiments is not only able to retrieve subwavelength dimensions from solely far-field measurements but is also capable of directly addressing the inverse problem. Our approach allows the rapid design and characterization of metasurface-based optical elements as well as optimal nanostructures for targeted chemicals and biomolecules, which are critical for sensing, imaging and integrated spectroscopy applications. Nanophotonics ; Plasmon ; Characterization (materials science) ; Photonics ; Inverse problem ; Computer science ; Field (mathematics) ; Nanotechnology ; Nanostructure ; Metamaterial ; Electromagnetic field ; Physics ; Materials science ; Optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Stefka Tyanova | Jüergen Cox
Source: Methods in molecular biology (article, 2018-01-01)
Field: Chemistry — Spectroscopy

Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research Mass spectrometry-based proteomics is a continuously growing field marked by technological and methodological improvements. Cancer proteomics is aimed at pursuing goals such as accurate diagnosis, patient stratification, and biomarker discovery, relying on the richness of information of quantitative proteome profiles. Translating these high-dimensional data into biological findings of clinical importance necessitates the use of robust and powerful computational tools and methods. In this chapter, we provide a detailed description of standard analysis steps for a clinical proteomics dataset performed in Perseus, a software for functional analysis of large-scale quantitative omics data. Proteomics ; Biomarker discovery ; Data science ; Proteome ; Omics ; Computer science ; Profiling (computer programming) ; Computational biology ; Bioinformatics ; Biology Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Gene expression and cancer classification
Authors: Stefan Chmiela | Huziel E. Sauceda | Klaus-Robert Müller | Alexandre Tkatchenko
Source: Nature Communications (article, 2018-09-18)
Field: Materials Science — Materials Chemistry

Towards exact molecular dynamics simulations with machine-learned force fields Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations. Molecular dynamics ; Force field (fiction) ; Interatomic potential ; Ab initio ; Quantum ; Field (mathematics) ; Work (physics) ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Hou Wang | Yan Wu | Xingzhong Yuan | Guangming Zeng | Jin Zhou | Xin Wang | Jia Wei Chew
Source: Advanced Materials (review, 2018-01-22)
Field: Materials Science — Materials Chemistry

Clay‐Inspired MXene‐Based Electrochemical Devices and Photo‐Electrocatalyst: State‐of‐the‐Art Progresses and Challenges MXene, an important and increasingly popular category of postgraphene 2D nanomaterials, has been rigorously investigated since early 2011 because of advantages including flexible tunability in element composition, hydrophobicity, metallic nature, unique in-plane anisotropic structure, high charge-carrier mobility, tunable band gap, and favorable optical and mechanical properties. To fully exploit these potentials and further expand beyond the existing boundaries, novel functional nanostructures spanning monolayer, multilayer, nanoparticles, and composites have been developed by means of intercalation, delamination, functionalization, hybridization, among others. Undeniably, the cutting-edge developments and applications of clay-inspired 2D MXene platform as electrochemical electrode or photo-electrocatalyst have conferred superior performance and have made significant impact in the field of energy and advanced catalysis. This review provides an overview of the fundamental properties and synthesis routes of pure MXene, functionalized MXene and their hybrids, highlights the state-of-the-art progresses of MXene-based applications with respect to supercapacitors, batteries, electrocatalysis and photocatalysis, and presents the challenges and prospects in the burgeoning field. Electrocatalyst ; Materials science ; Electrochemistry ; Nanotechnology ; Electrode ; Physical chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; 2D Materials and Applications