jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiwen Lai | Yahui Sun | Ting Wang | Poojan Modi | Claudio Cazorla | Umit B. Demirci | J.R. Ares | Fabrice Leardini | Kondo‐François Aguey‐Zinsou
Source: Advanced Sustainable Systems (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

How to Design Hydrogen Storage Materials? Fundamentals, Synthesis, and Storage Tanks Abstract As the world shifts toward renewable energy, the need for an effective energy carrier is pressing. Hydrogen has often been touted as a universal clean energy vector and the fuel of the future. Unfortunately, mass adoption of the hydrogen economy is slow due to a lack of incentives and technical difficulties in storing hydrogen. Better materials capable of reversible hydrogen uptake/release with hydrogen capacity surpassing 5 mass% at the ambient must emerge. So far, finding such materials has been elusive; alloys capable of ambient hydrogen uptake/release have a low storage capacity while high capacity hydrides have a very high hydrogen release temperature. From metal alloys to complex hydrides, a better understanding of the behavior of hydrogen in hydrides is essential to fine‐tune their properties toward application. Herein, the latest approaches to design hydrogen storage materials based on known hydrides are reviewed with the aim to facilitate the emergence of alternative thinking toward the design of better hydrogen storage materials. Synthetic methods and conceptual approaches to achieve particular hydrogen thermodynamics and kinetics are discussed. These include metallurgical alloying, mechanochemical modification, chemical destabilization, the nanosizing approach, and theoretical modeling and machine learning techniques to guide experimental work. Hydrogen storage ; Hydrogen ; Hydrogen economy ; Energy carrier ; Renewable energy ; Work (physics) ; Materials science ; Hydrogen fuel ; Hydrogen technologies ; Energy storage ; Process engineering ; Nanotechnology ; Chemistry ; Mechanical engineering ; Engineering ; Thermodynamics ; Physics Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Andrew G. M. Rankin | Julien Trébosc | Frédérique Pourpoint | Jean‐Paul Amoureux | Olivier Lafon
Source: Solid State Nuclear Magnetic Resonance (review, 2019-05-27)
Field: Chemistry — Spectroscopy

Recent developments in MAS DNP-NMR of materials Nuclear magnetic resonance spectroscopy ; Magic angle spinning ; Spectroscopy ; Transverse relaxation-optimized spectroscopy ; Characterization (materials science) ; NMR spectra database ; Solid-state nuclear magnetic resonance ; Materials science ; Fluorine-19 NMR ; Analytical Chemistry (journal) ; Chemistry ; Nuclear magnetic resonance ; Spectral line ; Chemical physics ; Nanotechnology ; Physics ; Organic chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; NMR spectroscopy and applications
Authors: Mhd Anas Tomeh | Roja Hadianamrei | Xiubo Zhao
Source: Pharmaceutics (review, 2019-09-26)
Field: Materials Science — Biomaterials

Silk Fibroin as a Functional Biomaterial for Drug and Gene Delivery Silk is a natural polymer with unique physicochemical and mechanical properties which makes it a desirable biomaterial for biomedical and pharmaceutical applications. Silk fibroin (SF) has been widely used for preparation of drug delivery systems due to its biocompatibility, controllable degradability and tunable drug release properties. SF-based drug delivery systems can encapsulate and stabilize various small molecule drugs as well as large biological drugs such as proteins and DNA to enhance their shelf lives and control the release to enhance their circulation time in the blood and thus the duration of action. Understanding the properties of SF and the potential ways of manipulating its structure to modify its physicochemical and mechanical properties allows for preparation of modulated drug delivery systems with desirable efficacies. This review will discuss the properties of SF material and summarize the recent advances of SF-based drug and gene delivery systems. Furthermore, conjugation of the SF to other biomolecules or polymers for tissue-specific drug delivery will also be discussed. Fibroin ; Drug delivery ; Biocompatibility ; Biomaterial ; Nanotechnology ; Drug ; Gene delivery ; Biomolecule ; Chemistry ; Materials science ; Biomedical engineering ; SILK ; Pharmacology ; Genetic enhancement ; Medicine ; Organic chemistry ; Biochemistry Silk-based biomaterials and applications ; Biochemical and Structural Characterization ; Electrospun Nanofibers in Biomedical Applications
Authors: Maykel Hernández‐Mesa | David Ropartz | Ana M. García‐Campaña | Hélène Rogniaux | Gaud Dervilly | Bruno Le Bizec
Source: Molecules (review, 2019-07-25)
Field: Chemistry — Spectroscopy

Ion Mobility Spectrometry in Food Analysis: Principles, Current Applications and Future Trends In the last decade, ion mobility spectrometry (IMS) has reemerged as an analytical separation technique, especially due to the commercialization of ion mobility mass spectrometers. Its applicability has been extended beyond classical applications such as the determination of chemical warfare agents and nowadays it is widely used for the characterization of biomolecules (e.g., proteins, glycans, lipids, etc.) and, more recently, of small molecules (e.g., metabolites, xenobiotics, etc.). Following this trend, the interest in this technique is growing among researchers from different fields including food science. Several advantages are attributed to IMS when integrated in traditional liquid chromatography (LC) and gas chromatography (GC) mass spectrometry (MS) workflows: (1) it improves method selectivity by providing an additional separation dimension that allows the separation of isobaric and isomeric compounds; (2) it increases method sensitivity by isolating the compounds of interest from background noise; (3) and it provides complementary information to mass spectra and retention time, the so-called collision cross section (CCS), so compounds can be identified with more confidence, either in targeted or non-targeted approaches. In this context, the number of applications focused on food analysis has increased exponentially in the last few years. This review provides an overview of the current status of IMS technology and its applicability in different areas of food analysis (i.e., food composition, process control, authentication, adulteration and safety). Ion-mobility spectrometry ; Mass spectrometry ; Context (archaeology) ; Chemistry ; Chromatography ; Nanotechnology ; Biochemical engineering ; Materials science Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development
Authors: Yahui Liu | Song Lu | Kefu Liu | Sheng Wang | Luqi Huang | Lanping Guo
Source: Plant Methods (review, 2019-11-18)
Field: Chemistry — Spectroscopy

Proteomics: a powerful tool to study plant responses to biotic stress In recent years, mass spectrometry-based proteomics has provided scientists with the tremendous capability to study plants more precisely than previously possible. Currently, proteomics has been transformed from an isolated field into a comprehensive tool for biological research that can be used to explain biological functions. Several studies have successfully used the power of proteomics as a discovery tool to uncover plant resistance mechanisms. There is growing evidence that indicates that the spatial proteome and post-translational modifications (PTMs) of proteins directly participate in the plant immune response. Therefore, understanding the subcellular localization and PTMs of proteins is crucial for a comprehensive understanding of plant responses to biotic stress. In this review, we discuss current approaches to plant proteomics that use mass spectrometry, with particular emphasis on the application of spatial proteomics and PTMs. The purpose of this paper is to investigate the current status of the field, discuss recent research challenges, and encourage the application of proteomics techniques to further research. Proteomics ; Proteome ; Biotic stress ; Computational biology ; Biology ; Field (mathematics) ; Quantitative proteomics ; Posttranslational modification ; Computer science ; Data science ; Bioinformatics ; Abiotic stress ; Biochemistry Advanced Proteomics Techniques and Applications ; Plant-Microbe Interactions and Immunity ; Mosquito-borne diseases and control
Authors: Katalin Barkovits | Sandra Pacharra | Kathy Pfeiffer | Simone Steinbach | Martin Eisenacher | Katrin Marcus | Julian Uszkoreit
Source: Molecular & Cellular Proteomics (article, 2019-11-07)
Field: Chemistry — Analytical Chemistry

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956. Reproducibility ; Computer science ; Computational biology ; Chromatography ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Tânia Cova | Alberto A. C. C. Pais
Source: Frontiers in Chemistry (review, 2019-11-26)
Field: Materials Science — Materials Chemistry

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns Computational Chemistry is currently a synergistic assembly between <i>ab initio</i> calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly. Deep learning ; Chemistry ; Environmental chemistry ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ben Blaiszik | Logan Ward | Marcus Schwarting | Jonathon Gaff | Ryan Chard | Daniel W. Pike | Kyle Chard | Ian Foster
Source: MRS Communications (article, 2019-09-20)
Field: Materials Science — Materials Chemistry

A data ecosystem to support machine learning in materials science Materials science ; Ecosystem ; Engineering physics ; Ecology ; Biology ; Engineering Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods
Authors: Ganesh Sivaraman | Anand Narayanan Krishnamoorthy | Matthias Baur | Christian Holm | Marius Stan | Gábor Cśanyi | Chris J. Benmore | Álvaro Vázquez‐Mayagoitia
Source: Cambridge University Engineering Department Publications Database (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide Abstract: We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies. Molecular dynamics ; Ab initio ; Amorphous solid ; Atom (system on chip) ; Computer science ; Gaussian ; Statistical physics ; Ab initio quantum chemistry methods ; Materials science ; Active learning (machine learning) ; Computational physics ; Algorithm ; Machine learning ; Molecular physics ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Molecule ; Crystallography Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Runhai Ouyang | Emre Ahmetcik | Christian Carbogno | Matthias Scheffler | Luca M. Ghiringhelli
Source: Journal of Physics Materials (article, 2019-02-15)
Field: Materials Science — Materials Chemistry

Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO The identification of descriptors of materials properties and functions that capture the underlying physical mechanisms is a critical goal in data-driven materials science. Only such descriptors will enable a trustful and efficient scanning of materials spaces and possibly the discovery of new materials. Recently, the sure-independence screening and sparsifying operator (SISSO) has been introduced and was successfully applied to a number of materials-science problems. SISSO is a compressed-sensing based methodology yielding predictive models that are expressed in form of analytical formulas, built from simple physical properties. These formulas are systematically selected from an immense number (billions or more) of candidates. In this work, we describe a powerful extension of the methodology to a 'multi-task learning' approach, which identifies a single descriptor capturing multiple target materials properties at the same time. This approach is specifically suited for a heterogeneous materials database with scarce or partial data, e.g., in which not all properties are reported for all materials in the training set. As showcase examples, we address the construction of materials-properties maps for the relative stability of octet-binary compounds, considering several crystal phases simultaneously, and the metal/insulator classification of binary materials distributed over many crystal-prototypes. Computer science ; Binary number ; Set (abstract data type) ; Task (project management) ; Operator (biology) ; Binary classification ; Database ; Stability (learning theory) ; Data mining ; Machine learning ; Mathematics ; Support vector machine Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhipeng Liu | Zhiyong Jiang | Ming Yan | Xiaoqing Wang
Source: Frontiers in Chemistry (review, 2019-10-25)
Field: Materials Science — Materials Chemistry

Recent Progress of BODIPY Dyes With Aggregation-Induced Emission With the development of organic optoelectronic materials and bioimaging technology, to exploit organic luminescent materials with high luminescent efficiency in aggregation-state has become a research hotspot. BODIPYs have become one of the research objects of this kind of material because of their obvious advantages. This review focuses on the design and synthesis of AIE-type BODIPYs, the mechanism of AIE properties and their applications in recent years. Through classification, analysis, and summary, this review aims to explore the structure-activity relationship of AIE-type BODIPYs and to provide ideas for the further design and potential applications of AIE-active fluorescent materials. BODIPY ; Aggregation-induced emission ; Nanotechnology ; Luminescence ; Fluorescence ; Computer science ; Materials science ; Biochemical engineering ; Engineering ; Optoelectronics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Nanoplatforms for cancer theranostics
Authors: Wang, Wujie | Gomez-Bombarelli, Rafael
Source: DSpace@MIT (Massachusetts Institute of Technology) (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

Coarse-graining auto-encoders for molecular dynamics Granularity ; Dynamics (music) ; Computer science ; Molecular dynamics ; Encoder ; Statistical physics ; Biological system ; Physics ; Biology ; Acoustics ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Alexander Pöthig | Angela Casini
Source: Theranostics (review, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Recent Developments of Supramolecular Metal-based Structures for Applications in Cancer Therapy and Imaging The biomedical application of discrete supramolecular metal-based structures, including supramolecular coordination complexes (SCCs), is still an emergent field of study. However, pioneering studies over the last 10 years demonstrated the potential of these supramolecular compounds as novel anticancer drugs, endowed with different mechanisms of action compared to classical small-molecules, often related to their peculiar molecular recognition properties. In addition, the robustness and modular composition of supramolecular metal-based structures allows for an incorporation of different functionalities in the same system to enable imaging in cells <i>via</i> different modalities, but also active tumor targeting and stimuli-responsiveness. Although most of the studies reported so far exploit these systems for therapy, supramolecular metal-based structures may also constitute ideal scaffolds to develop multimodal <i>theranostic</i> agents. Of note, the host-guest chemistry of 3D self-assembled supramolecular structures - within the metallacages family - can also be exploited to design novel drug delivery systems for anticancer chemotherapeutics. In this review, we aim at summarizing the pivotal concepts in this fascinating research area, starting with the main design principles and illustrating representative examples while providing a critical discussion of the state-of-the-art. A section is also included on supramolecular organometallic complexes (SOCs) whereby the (organic) linker is forming the organometallic bond to the metal node, whose biological applications are still to be explored. Certainly, the myriad of possible supramolecular metal-based structures and their almost limitless modularity and tunability suggests that the biomedical applications of such complex chemical entities will continue along this already promising path. Supramolecular chemistry ; Nanotechnology ; Modularity (biology) ; Cancer therapy ; Linker ; Modular design ; Supramolecular polymers ; Computer science ; Chemistry ; Materials science ; Cancer ; Molecule ; Medicine ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: J. M. Rickman | Turab Lookman | Sergei V. Kalinin
Source: Acta Materialia (article, 2019-01-30)
Field: Materials Science — Materials Chemistry

Materials informatics: From the atomic-level to the continuum Materials informatics ; Data science ; Computer science ; Big data ; Informatics ; Engineering informatics ; Health informatics ; Engineering physics ; Data mining ; Physics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques