jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Subash Adhikari | Edouard C. Nice | Eric W. Deutsch | Lydie Lane | Gilbert S. Omenn | Stephen R. Pennington | Young‐Ki Paik | Christopher M. Overall | Fernando J. Corrales | Ileana M. Cristea | Jennifer E. Van Eyk | Mathias Uhlén | Cecilia Lindskog | Daniel W. Chan | Amos Bairoch | J. Waddington | Joshua Justice | Joshua LaBaer | Henry Rodriguez | Fuchu He | Markus Kostrzewa | Peipei Ping | Rebekah L. Gundry | Peter Stewart | Sanjeeva Srivastava | Sudhir Srivastava | Fábio César Sousa Nogueira | Gilberto B. Domont | Yves Vandenbrouck | Maggie P. Y. Lam | Sara A. Wennersten | Juan Antonio Vizcaíno | Marc R. Wilkins | Jochen M. Schwenk | Emma Lundberg | Nuno Bandeira | György Marko‐Varga | Susan T. Weintraub | Charles Pineau | Ulrike Kusebauch | Robert L. Moritz | Seong Beom Ahn | Magnus Palmblad | M Snyder | Ruedi Aebersold | Mark S. Baker
Source: Nature Communications (review, 2020-10-16)
Field: Chemistry — Spectroscopy

A high-stringency blueprint of the human proteome The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases. Human proteome project ; Proteome ; Blueprint ; Computational biology ; Human genome ; Biology ; Data science ; Genome ; Bioinformatics ; Computer science ; Proteomics ; Genetics ; Gene ; Engineering Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Genetics, Bioinformatics, and Biomedical Research
Authors: Guangfu Li | Dongxia Zhu | Xinlong Wang | Zhongmin Su | Martin R. Bryce
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Dinuclear metal complexes: multifunctional properties and applications The development of metal complexes for optoelectronic applications is a fertile area of research. In contrast to the rigorous development of mononuclear metal complexes, dinuclear species have been less well studied and their fundamental chemistry and applications are under-explored. However, dinuclear species present special properties and functions compared with mononuclear species as a consequence of tuning the bridging ligands, the cyclometalated ligands or the two metal centers. More recently, dinuclear species have enabled important breakthroughs in the fields of OLEDs, photocatalytic water splitting and CO2 reduction, DSPEC, chemosensors, biosensors, PDT, smart materials and so on. Here we present an overview of recent developments of dinuclear metal complexes, their multifunctional properties and their various applications. The relationship between structure and property of dinuclear species and important factors which influence device performance are discussed. Finally, we illustrate some challenges and opportunities for future research into dinuclear metal complexes. This review aims to provide an up-to-date summary and outlook of functional dinuclear metal complexes and to stimulate more researchers to contribute to this exciting interdisciplinary field. Metal ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Organic Light-Emitting Diodes Research ; Luminescence and Fluorescent Materials
Authors: Peyman Z. Moghadam | Aurelia Li | Xiaowei Liu | Rocío Bueno-Pérez | Shudong Wang | Seth B. Wiggin | P.A. Wood | David Fairen‐Jiménez
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Targeted classification of metal–organic frameworks in the Cambridge structural database (CSD) Large-scale targeted exploration of metal-organic frameworks (MOFs) with characteristics such as specific surface chemistry or metal-cluster family has not been investigated so far. These definitions are particularly important because they can define the way MOFs interact with specific molecules (<i>e.g.</i> their hydrophilic/phobic character) or their physicochemical stability. We report here the development of algorithms to break down the overarching family of MOFs into a number of subgroups according to some of their key chemical and physical features. Available within the Cambridge Crystallographic Data Centre's (CCDC) software, we introduce new approaches to allow researchers to browse and efficiently look for targeted MOF families based on some of the most well-known secondary building units. We then classify them in terms of their crystalline properties: metal-cluster, network and pore dimensionality, surface chemistry (<i>i.e.</i> functional groups) and chirality. This dynamic database and family of algorithms allow experimentalists and computational users to benefit from the developed criteria to look for specific classes of MOFs but also enable users - and encourage them - to develop additional MOF queries based on desired chemistries. These tools are backed-up by an interactive web-based data explorer containing all the data obtained. We also demonstrate the usefulness of these tools with a high-throughput screening for hydrogen storage at room temperature. This toolbox, integrated in the CCDC software, will guide future exploration of MOFs and similar materials, as well as their design and development for an ever-increasing range of potential applications. Metal-organic framework ; Cluster (spacecraft) ; Nanotechnology ; Scale (ratio) ; Metal ; Chemistry ; Computer science ; Materials science ; Organic chemistry ; Geography ; Cartography ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Luigi Bonati | Valerio Rizzi | Michele Parrinello
Source: The Journal of Physical Chemistry Letters (article, 2020-04-02)
Field: Materials Science — Materials Chemistry

Data-Driven Collective Variables for Enhanced Sampling Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process. Discriminative model ; Linear discriminant analysis ; Artificial neural network ; Sampling (signal processing) ; Computer science ; Focus (optics) ; Artificial intelligence ; Set (abstract data type) ; Identification (biology) ; Mathematics ; Statistical physics ; Physics ; Filter (signal processing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Bartel, CJ | Trewartha, A | Wang, Q | Dunn, A | Jain, A | Ceder, G
Source: eScholarship (California Digital Library) (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

A critical examination of compound stability predictions from machine-learned formation energies Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests. Stability (learning theory) ; Computer science ; A priori and a posteriori ; Constraint (computer-aided design) ; Work (physics) ; Machine learning ; Set (abstract data type) ; Density functional theory ; Stoichiometry ; Artificial intelligence ; Biological system ; Chemistry ; Thermodynamics ; Computational chemistry ; Mathematics ; Physics ; Physical chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Olga Stamati | Edward Andò | Emmanuel Roubin | Rémi Cailletaud | Max Wiebicke | Gustavo Pinzón | Cyrille Couture | Ryan Hurley | Robert A. Caulk | Denis Caillerie | Takashi Matsushima | Pierre Bésuelle | Félix Bertoni | Tom Arnaud | Alejandro Ortega Laborin | Riccardo Rorato | Yue Sun | Alessandro Tengattini | Olumide Okubadejo | Jean‐Baptiste Colliat | Mohammad Saadatfar | Fernando E. Garcia | Christos Papazoglou | Ilija Vego | Sébastien Brisard | Jelke Dijkstra | Georgios Birmpilis
Source: The Journal of Open Source Software (article, 2020-07-13)
Field: Materials Science — Materials Chemistry

spam: Software for Practical Analysis of Materials International audience Library science ; Political science ; Humanities ; Computer science ; Philosophy Machine Learning in Materials Science ; Ion-surface interactions and analysis
Authors: Yongfei Juan | Yongbing Dai | Yang Yang | Jiao Zhang
Source: Journal of Material Science and Technology (article, 2020-12-24)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery using machine learning Computer science ; Big data ; Generalization ; Data science ; Artificial intelligence ; Nanotechnology ; Data mining ; Materials science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Mineral Processing and Grinding
Authors: Daisuke Miyashiro | Ryo Hamano | Kazuo Umemura
Source: Nanomaterials (review, 2020-01-21)
Field: Materials Science — Biomaterials

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes Carbon nanotubes (CNTs) have been extensively studied as one of the most interesting nanomaterials for over 25 years because they exhibit excellent mechanical, electrical, thermal, optical, and electrical properties. In the past decade, the number of publications and patents on cellulose and nanocellulose (NC) increased tenfold. Research on NC with excellent mechanical properties, flexibility, and transparency is accelerating due to the growing environmental problems surrounding us such as CO<sub>2</sub> emissions, the accumulation of large amounts of plastic, and the depletion of energy resources such as oil. Research on mixed materials of cellulose, NC, and CNTs has been expanding because these materials exhibit various characteristics that can be controlled by varying the combination of cellulose, NC to CNTs while also being biodegradable and recyclable. An understanding of these mixed materials is required because these characteristics are diverse and are expected to solve various environmental problems. Thus far, many review papers on cellulose, NC or CNTs have been published. Although guidance for the suitable application of these mixed materials is necessary, there are few reviews summarizing them. Therefore, this review introduces the application and feature on mixed materials of cellulose, NC and CNTs. Nanocellulose ; Cellulose ; Carbon nanotube ; Materials science ; Nanomaterials ; Nanotechnology ; Flexibility (engineering) ; Chemical engineering ; Engineering Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging
Authors: Xiyue Zhang | Xiaoqing Liu | Yinxiang Zeng | Yexiang Tong | Xihong Lu
Source: Small Methods (article, 2020-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Oxygen Defects in Promoting the Electrochemical Performance of Metal Oxides for Supercapacitors: Recent Advances and Challenges Abstract Metal oxides (MOs) with multiple active sites are regarded as one of the most potential active materials for high‐performance supercapacitor (SC) constructions. Engineering oxygen vacancies into MOs can effectively modulate their electronic properties, subtly induce impurity states in their bandgaps, and considerably optimize their electrical conductivity. Benefiting from these advantageous properties, the resultant oxygen‐defective electrodes generally embed more electrochemical active sites and present better charge storage capability in contrast to the pristine sample. Particular research attention has been given to the realization of precise oxygen vacancy introduction into MOs and the fine tuning of their properties. This review presents an overview of recent accomplishments in developing new oxygen‐defective MOs for advanced SC assembly. Different strategies for oxygen vacancy productions are summarized and compared. The specific roles of oxygen vacancies in the electrochemical performance enhancement are fully addressed and the relevant impact factors are discussed in detail. Finally, the key challenges and future opportunities in this rapidly developing field are highlighted. Supercapacitor ; Materials science ; Nanotechnology ; Oxygen ; Electrochemistry ; Oxygen evolution ; Metal ; Vacancy defect ; Impurity ; Electrode ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Yubo Wang | Jose Enrico Q. Quinsaat | Tomoko Ono | Masatoshi Maeki | Manabu Tokeshi | Takuya Isono | Kenji Tajima | Toshifumi Satoh | Shin‐ichiro Sato | Yutaka Miura | Takuya Yamamoto
Source: Nature Communications (article, 2020-11-30)
Field: Materials Science — Biomaterials

Enhanced dispersion stability of gold nanoparticles by the physisorption of cyclic poly(ethylene glycol) Nano-sized metal particles are attracting much interest in industrial and biomedical applications due to the recent progress and development of nanotechnology, and the surface-modifications by appropriate polymers are key techniques to stably express their characteristics. Herein, we applied cyclic poly(ethylene glycol) (c-PEG), having no chemical inhomogeneity, to provide a polymer topology-dependent stabilization for the surface-modification of gold nanoparticles (AuNPs) through physisorption. By simply mixing c-PEG, but not linear counterparts, enables AuNPs to maintain dispersibility through freezing, lyophilization, or heating. Surprisingly, c-PEG endowed AuNPs with even better dispersion stability than thiolated PEG (HS-PEG-OMe). The stronger affinity of c-PEG was confirmed by DLS, ζ-potential, and FT-IR. Furthermore, the c-PEG system exhibited prolonged blood circulation and enhanced tumor accumulation in mice. Our data suggests that c-PEG induces physisorption on AuNPs, supplying sufficient stability toward bio-medical applications, and would be an alternative approach to the gold-sulfur chemisorption. Physisorption ; Ethylene glycol ; Colloidal gold ; PEG ratio ; Chemisorption ; Dispersion (optics) ; Dispersion stability ; Polymer ; Nanoparticle ; Chemical engineering ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Rabbee G. Mahmudunnabi | Fatema Zerin Farhana | Navid Kashaninejad | Shakhawat H. Firoz | Yoon‐Bo Shim | Muhammad J. A. Shiddiky
Source: The Analyst (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Nanozyme-based electrochemical biosensors for disease biomarker detection In recent years, a new group of nanomaterials named nanozymes that exhibit enzyme-mimicking catalytic activity has emerged as a promising alternative to natural enzymes. Nanozymes can address some of the intrinsic limitations of natural enzymes such as high cost, low stability, difficulty in storage, and specific working conditions (i.e., narrow substrate, temperature and pH ranges). Thus, synthesis and applications of hybrid and stimuli-responsive advanced nanozymes could revolutionize the current practice in life sciences and biosensor applications. On the other hand, electrochemical biosensors have long been used as an efficient way for quantitative detection of analytes (biomarkers) of interest. As such, the use of nanozymes in electrochemical biosensors is particularly important to achieve low cost and stable biosensors for prognostics, diagnostics, and therapeutic monitoring of diseases. Herein, we summarize the recent advances in the synthesis and classification of common nanozymes and their application in electrochemical biosensor development. After briefly overviewing the applications of nanozymes in non-electrochemical-based biomolecular sensing systems, we thoroughly discuss the state-of-the-art advances in nanozyme-based electrochemical biosensors, including genosensors, immunosensors, cytosensors and aptasensors. The applications of nanozymes in microfluidic-based assays are also discussed separately. We also highlight the challenges of nanozyme-based electrochemical biosensors and provide some possible strategies to address these limitations. Finally, future perspectives on the development of nanozyme-based electrochemical biosensors for disease biomarker detection are presented. We envisage that standardization of nanozymes and their fabrication process may bring a paradigm shift in biomolecular sensing by fabricating highly specific, multi-enzyme mimicking nanozymes for highly sensitive, selective, and low-biofouling electrochemical biosensors. Biomarker ; Biosensor ; Disease ; Nanotechnology ; Computational biology ; Medicine ; Chemistry ; Materials science ; Biology ; Internal medicine ; Biochemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Mortazavi, Bohayra | Podryabinkin, Evgeny V. | Roche, Stephan | Rabczuk, Timon | Zhuang, Xiaoying | Shapeev, Alexander V.
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and the finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv., 2019, 5, eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures. Borophene ; Graphene ; Thermal conductivity ; Molecular dynamics ; Materials science ; Multiscale modeling ; Density functional theory ; Heterojunction ; Interatomic potential ; Lattice (music) ; Finite element method ; Nanotechnology ; Statistical physics ; Computer science ; Computational chemistry ; Physics ; Thermodynamics ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: Julia Westermayr | Michael Gastegger | Philipp Marquetand
Source: The Journal of Physical Chemistry Letters (article, 2020-04-20)
Field: Materials Science — Materials Chemistry

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems. Representation (politics) ; Polyatomic ion ; Computer science ; Covariant transformation ; Excited state ; Field (mathematics) ; Molecular dynamics ; Deep learning ; Quantum ; Artificial intelligence ; Molecule ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies