jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Clive A. Randall | Zhongming Fan | Ian M. Reaney | Long‐Qing Chen | Susan Trolier‐McKinstry
Source: Journal of the American Ceramic Society (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Antiferroelectrics: History, fundamentals, crystal chemistry, crystal structures, size effects, and applications Abstract Antiferroelectric (AFE) materials are of great interest owing to their scientific richness and their utility in high‐energy density capacitors. Here, the history of AFEs is reviewed, and the characteristics of antiferroelectricity and the phase transition of an AFE material are described. AFEs are energetically close to ferroelectric (FE) phases, and thus both the electric field strength and applied stress (pressure) influence the nature of the transition. With the comparable energetics between the AFE and FE phases, there can be a competition and frustration of these phases, and either incommensurate and/or a glassy (relaxor) structures may be observed. The phase transition in AFEs can also be influenced by the crystal/grain size, particularly at nanometric dimensions, and may be tuned through the formation of solid solutions. There have been extensive studies on the perovskite family of AFE materials, but many other crystal structures host AFE behavior, such as CuBiP 2 Se 6 . AFE applications include DC‐link capacitors for power electronics, defibrillator capacitors, pulse power devices, and electromechanical actuators. The paper concludes with a perspective on the future needs and opportunities with respect to discovery, science, and applications of AFE. Capacitor ; Ferroelectricity ; Antiferroelectricity ; Materials science ; Phase transition ; Crystal (programming language) ; Perovskite (structure) ; Nanotechnology ; Condensed matter physics ; Dielectric ; Crystallography ; Chemistry ; Optoelectronics ; Electrical engineering ; Voltage ; Physics ; Computer science ; Engineering Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Microwave Dielectric Ceramics Synthesis
Authors: Charles Babin | Rainer Stöhr | Naoya Morioka | Tobias Linkewitz | Timo Steidl | Raphael Wörnle | Di Liu | Erik Hesselmeier | Vadim Vorobyov | Andrej Denisenko | Mario Hentschel | Christian Gobert | Patrick Berwian | G. V. Astakhov | Wolfgang Knolle | Sridhar Majety | Pranta Saha | Marina Radulaski | Nguyên Tiên Són | Jawad Ul‐Hassan | Florian Kaiser | Jörg Wrachtrup
Source: Nature Materials (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Fabrication and nanophotonic waveguide integration of silicon carbide colour centres with preserved spin-optical coherence Silicon carbide ; Nanophotonics ; Materials science ; Optoelectronics ; Qubit ; Fabrication ; Coherence (philosophical gambling strategy) ; Spin (aerodynamics) ; Nanolithography ; Photon ; Quantum technology ; Silicon ; Waveguide ; Quantum ; Optics ; Physics ; Open quantum system ; Quantum mechanics Diamond and Carbon-based Materials Research ; Quantum Information and Cryptography ; Quantum and electron transport phenomena
Authors: Manyi Yang | Luigi Bonati | Daniela Polino | Michele Parrinello
Source: Catalysis Today (article, 2021-03-24)
Field: Materials Science — Materials Chemistry

Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water Metadynamics ; Metastability ; Molecular dynamics ; Chemistry ; Artificial neural network ; Ab initio ; Quantum ; Decomposition ; Computational chemistry ; Gibbs free energy ; Computer science ; Biological system ; Statistical physics ; Chemical physics ; Thermodynamics ; Machine learning ; Physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Chi Chen | Yunxing Zuo | Weike Ye | Xiangguo Li | Shyue Ping Ong
Source: Nature Computational Science (article, 2021-01-14)
Field: Materials Science — Materials Chemistry

Learning properties of ordered and disordered materials from multi-fidelity data Graph ; Bridging (networking) ; Band gap ; Materials informatics ; Material properties ; Graph theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Siavash Iravani | Rajender S. Varma
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

MXenes and MXene-based materials for tissue engineering and regenerative medicine: recent advances Tissue engineering and regenerative medicine applications of MXenes are described along with present challenges and future perspectives. MXenes ; Regenerative medicine ; Tissue engineering ; Engineering ; Engineering ethics ; Nanotechnology ; Materials science ; Biomedical engineering ; Biology ; Cell biology ; Stem cell MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications
Authors: S.K. Burley | Charmi Bhikadiya | Chunxiao Bi | Sebastian Bittrich | Li Chen | G.V. Crichlow | José M. Duarte | Shuchismita Dutta | Maryam Fayazi | Zukang Feng | Justin W. Flatt | Sai J. Ganesan | David S. Goodsell | Sutapa Ghosh | Rachel Kramer Green | Vladimir Guranović | Jeremy Henry | Brian P. Hudson | Catherine L. Lawson | Yu‐He Liang | Robert Lowe | Ezra Peisach | Irina Persikova | Dennis W. Piehl | Yana Rose | Andrej Săli | Joan Segura | Monica Sekharan | Chenghua Shao | Brinda Vallat | Maria Voigt | John Westbrook | Shamara Whetstone | Jasmine Young | Christine Zardecki
Source: Protein Science (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

<scp>RCSB</scp> Protein Data Bank: Celebrating 50 years of the <scp>PDB</scp> with new tools for understanding and visualizing biological macromolecules in <scp>3D</scp> The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal. Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Collaboratory ; Structural bioinformatics ; Data curation ; World Wide Web ; Computer science ; Bioinformatics ; Chemistry ; Biology ; Protein structure ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Áron Péter | Soumitra Agasti | Oliver J. Knowles | Emma Pye | David J. Procter
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

Recent advances in the chemistry of ketyl radicals Ketyl radicals are valuable reactive intermediates for synthesis and are used extensively to construct complex, functionalized products from carbonyl substrates. Single electron transfer (SET) reduction of the C[double bond, length as m-dash]O bond of aldehydes and ketones is the classical approach for the formation of ketyl radicals and metal reductants are the archetypal reagents employed. The past decade has, however, witnessed significant advances in the generation and harnessing of ketyl radicals. This tutorial review highlights recent, exciting developments in the chemistry of ketyl radicals by comparing the varied contemporary - for example, using photoredox catalysts - and more classical approaches for the generation and use of ketyl radicals. The review will focus on different strategies for ketyl radical generation, their creative use in new synthetic protocols, strategies for the control of enantioselectivity, and detailed mechanisms where appropriate. Ketyl ; Radical ; Chemistry ; Photochemistry ; Reagent ; Combinatorial chemistry ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Max Pinheiro | Fuchun Ge | Nicolas Ferré | Pavlo O. Dral | Mario Barbatti
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Choosing the right molecular machine learning potential Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one. Computer science ; Artificial intelligence ; Machine learning ; Observable ; Energy (signal processing) ; Yield (engineering) ; Potential energy ; Quantum ; Energy cost ; Data science ; Environmental economics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Pablo del Campo | Cristina Martı́nez | Avelino Corma
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Activation and conversion of alkanes in the confined space of zeolite-type materials Microporous zeolite-type materials, with crystalline porous structures formed by well-defined channels and cages of molecular dimensions, have been widely employed as heterogeneous catalysts since the early 1960s, due to their wide variety of framework topologies, compositional flexibility and hydrothermal stability. The possible selection of the microporous structure and of the elements located in framework and extraframework positions enables the design of highly selective catalysts with well-defined active sites of acidic, basic or redox character, opening the path to their application in a wide range of catalytic processes. This versatility and high catalytic efficiency is the key factor enabling their use in the activation and conversion of different alkanes, ranging from methane to long chain n-paraffins. Alkanes are highly stable molecules, but their abundance and low cost have been two main driving forces for the development of processes directed to their upgrading over the last 50 years. However, the availability of advanced characterization tools combined with molecular modelling has enabled a more fundamental approach to the activation and conversion of alkanes, with most of the recent research being focused on the functionalization of methane and light alkanes, where their selective transformation at reasonable conversions remains, even nowadays, an important challenge. In this review, we will cover the use of microporous zeolite-type materials as components of mono- and bifunctional catalysts in the catalytic activation and conversion of C<sub>1</sub><sup>+</sup> alkanes under non-oxidative or oxidative conditions. In each case, the alkane activation will be approached from a fundamental perspective, with the aim of understanding, at the molecular level, the role of the active sites involved in the activation and transformation of the different molecules and the contribution of shape-selective or confinement effects imposed by the microporous structure. Zeolite ; Microporous material ; Selectivity ; Confined space ; Diffusion ; Chemistry ; Chemical engineering ; Molecular sieve ; Nanotechnology ; Catalysis ; Physical chemistry ; Organic chemistry ; Chemical physics ; Materials science ; Physics ; Thermodynamics Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Joshua Nicks | Kezia Sasitharan | Ram R. R. Prasad | David J. Ashworth | Jonathan A. Foster
Source: Advanced Functional Materials (article, 2021-07-17)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework Nanosheets: Programmable 2D Materials for Catalysis, Sensing, Electronics, and Separation Applications Metal–organic framework nanosheets (MONs) have recently emerged as a distinct class of 2D materials with programmable structures that make them useful in diverse applications. In this review, the breadth of applications that have so far been investigated are surveyed, thanks to the distinct combination of properties afforded by MONs. How: 1) The high surface areas and readily accessible active sites of MONs mean they have been exploited for a variety of heterogeneous, photo‐, and electro‐catalytic applications; 2) their diverse surface chemistry and wide range of optical and electronic responses have been harnessed for the sensing of small molecules, biological molecules, and ions; 3) MONs tunable optoelectronic properties and nanoscopic dimensions have enabled them to be harnessed in light harvesting and emission, energy storage, and other electronic devices; 4) the anisotropic structure and porous nature of MONs mean they have shown great promise in a variety of gas separation and water purification applications; are discussed. The aim is to draw links between the uses of MONs in these different applications in order to highlight the common opportunities and challenges presented by this promising class of nanomaterials. Nanotechnology ; Materials science ; Nanomaterials ; Electronics ; Nanoscopic scale ; Metal-organic framework ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Nanomaterials in Catalysis
Authors: Brianna M. Vickerman | Emilia M. Zywot | Teresa K. Tarrant | David S. Lawrence
Source: Nature Reviews Chemistry (review, 2021-10-06)
Field: Materials Science — Materials Chemistry

Taking phototherapeutics from concept to clinical launch Variety (cybernetics) ; Computer science ; Nanotechnology ; Computational biology ; Biochemical engineering ; Biology ; Artificial intelligence ; Materials science ; Engineering Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Nanoplatforms for cancer theranostics
Authors: Wuxin Sha | Yan Li | Shun Tang | Jie Tian | Yuming Zhao | Yaqing Guo | Weixin Zhang | Xinfang Zhang | Songfeng Lu | Yuan‐Cheng Cao | Shijie Cheng
Source: InfoMat (article, 2021-01-25)
Field: Materials Science — Materials Chemistry

Machine learning in polymer informatics Abstract Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields. image Informatics ; Materials informatics ; Computer science ; Health informatics ; Machine learning ; Process (computing) ; Artificial intelligence ; Data science ; Engineering informatics ; Aerospace ; Engineering ; Health care Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: Chemical Reviews (review, 2021-08-13)
Field: Materials Science — Materials Chemistry

Ab Initio Machine Learning in Chemical Compound Space Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics. Chemical space ; Ab initio ; Universality (dynamical systems) ; Quantum ; Transferability ; Statistical physics ; Computer science ; Chemistry ; Artificial intelligence ; Machine learning ; Quantum mechanics ; Physics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Fatirah Fadil | Nor Dalila Nor Affandi | Mohd Iqbal Misnon | Noor Najmi Bonnia | Ahmad Mukifza Harun | Mohammad Khursheed Alam
Source: Polymers (review, 2021-06-24)
Field: Materials Science — Biomaterials

Review on Electrospun Nanofiber-Applied Products Electrospinning technology, which was previously known as a scientific interdisciplinary research approach, is now ready to move towards a practice-based interdisciplinary approach in a variety of fields, progressively. Electrospun nanofiber-applied products are made directly from a nonwoven fabric-based membranes prepared from polymeric liquids involving the application of sufficiently high voltages during electrospinning. Today, electrospun nanofiber-based materials are of remarkable interest across multiple fields of applications, such as in electronics, sensors, functional garments, sound proofing, filters, wound dressing and scaffolds. This article presents such a review for summarizing the current progress on the manufacturing scalability of electrospun nanofibers and the commercialization of electrospun nanofiber products by dedicated companies globally. Despite the clear potential and limitless possibilities for electrospun nanofiber applications, the uptake of electrospinning by the industry is still limited due to the challenges in the manufacturing and turning of electrospun nanofibers into physical products. The recent developments in the field of electrospinning, such as the prominent nonwoven technology, personal views and the potential path forward for the growth of commercially applied products based on electrospun nanofibers, are also highlighted. Electrospinning ; Nanofiber ; Materials science ; Nanotechnology ; Wound dressing ; Commercialization ; Polymer ; Composite material ; Business Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Robin Rajan | Sana Ahmed | Neha Sharma | Nishant Kumar | Alisha Debas | Kazuaki Matsumura
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Biomaterials

Review of the current state of protein aggregation inhibition from a materials chemistry perspective: special focus on polymeric materials This review discusses various aspects of protein aggregation and inhibition strategies, emphasizing the use of polymers, which is one of the most promising approaches to combat various aggregation-induced complications in neurodegenerative diseases and protein therapeutics. Perspective (graphical) ; Focus (optics) ; Protein aggregation ; Nanotechnology ; Chemistry ; Current (fluid) ; Neuroscience ; Biophysics ; Computer science ; Materials science ; Biochemistry ; Engineering ; Biology ; Physics ; Artificial intelligence biodegradable polymer synthesis and properties ; Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications
Authors: Florence H. Vermeire | William H. Green
Source: Chemical Engineering Journal (article, 2021-03-11)
Field: Materials Science — Materials Chemistry

Transfer learning for solvation free energies: From quantum chemistry to experiments Solvation ; Chemistry ; Quantum chemistry ; Quantum ; Chemical physics ; Quantum chemical ; Computational chemistry ; Statistical physics ; Quantum mechanics ; Physics ; Organic chemistry ; Supramolecular chemistry ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Aashutosh Mistry | Alejandro A. Franco | Samuel J. Cooper | Scott Alan Roberts | Venkatasubramanian Viswanathan
Source: ACS Energy Letters (review, 2021-03-23)
Field: Materials Science — Materials Chemistry

How Machine Learning Will Revolutionize Electrochemical Sciences Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the <i>trial-and-error</i> approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can <i>revolutionize</i> the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute. Computer science ; Process (computing) ; Implementation ; Function (biology) ; Frame (networking) ; Electrochemistry ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Nanotechnology ; Materials science ; Software engineering ; Chemistry ; Engineering ; Electrode ; Telecommunications Machine Learning in Materials Science ; Fuel Cells and Related Materials ; CO2 Reduction Techniques and Catalysts