jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chi Chen | Yunxing Zuo | Weike Ye | Xiangguo Li | Zhi Deng | Shyue Ping Ong
Source: Advanced Energy Materials (review, 2020-01-29)
Field: Materials Science — Materials Chemistry

A Critical Review of Machine Learning of Energy Materials Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field. Photovoltaics ; Thermoelectric materials ; Nanotechnology ; Materials science ; Field (mathematics) ; Energy (signal processing) ; Engineering physics ; Computer science ; Engineering ; Physics ; Photovoltaic system ; Electrical engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Kang Rui Garrick Lim | Albertus D. Handoko | Srinivasa Kartik Nemani | Brian C. Wyatt | Haiying Jiang | Junwang Tang | Babak Anasori | Zhi Wei Seh
Source: ACS Nano (article, 2020-08-12)
Field: Materials Science — Materials Chemistry

Rational Design of Two-Dimensional Transition Metal Carbide/Nitride (MXene) Hybrids and Nanocomposites for Catalytic Energy Storage and Conversion Electro-, photo-, and photoelectrocatalysis play a critical role toward the realization of a sustainable energy economy. They facilitate numerous redox reactions in energy storage and conversion systems, enabling the production of chemical feedstock and clean fuels from abundant resources like water, carbon dioxide, and nitrogen. One major obstacle for their large-scale implementation is the scarcity of cost-effective, durable, and efficient catalysts. A family of two-dimensional transition metal carbides, nitrides, and carbonitrides (MXenes) has recently emerged as promising earth-abundant candidates for large-area catalytic energy storage and conversion due to their unique properties of hydrophilicity, high metallic conductivity, and ease of production by solution processing. To take full advantage of these desirable properties, MXenes have been combined with other materials to form MXene hybrids with significantly enhanced catalytic performances beyond the sum of their individual components. MXene hybridization tunes the electronic structure toward optimal binding of redox active species to improve intrinsic activity while increasing the density and accessibility of active sites. This review outlines recent strategies in the design of MXene hybrids for industrially relevant electrocatalytic, photocatalytic, and photoelectrocatalytic applications such as water splitting, metal-air/sulfur batteries, carbon dioxide reduction, and nitrogen reduction. By clarifying the roles of individual material components in the MXene hybrids, we provide design strategies to synergistically couple MXenes with associated materials for highly efficient and durable catalytic applications. We conclude by highlighting key gaps in the current understanding of MXene hybrids to guide future MXene hybrid designs in catalytic energy storage and conversion applications. Rational design ; Materials science ; Nitride ; Catalysis ; Carbide ; Nanocomposite ; Transition metal ; Nanotechnology ; Energy storage ; Metal ; Energy transformation ; Chemical engineering ; Metallurgy ; Chemistry ; Organic chemistry ; Thermodynamics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques
Authors: Jungho Mun | Minkyung Kim | Younghwan Yang | Trevon Badloe | Jincheng Ni | Yang Chen | Cheng‐Wei Qiu | Junsuk Rho
Source: Light Science & Applications (review, 2020-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic chirality: from fundamentals to nontraditional chiroptical phenomena Chirality arises universally across many different fields. Recent advancements in artificial nanomaterials have demonstrated chiroptical responses that far exceed those found in natural materials. Chiroptical phenomena are complicated processes that involve transitions between states with opposite parities, and solid interpretations of these observations are yet to be clearly provided. In this review, we present a comprehensive overview of the theoretical aspects of chirality in light, nanostructures, and nanosystems and their chiroptical interactions. Descriptions of observed chiroptical phenomena based on these fundamentals are intensively discussed. We start with the strong intrinsic and extrinsic chirality in plasmonic nanoparticle systems, followed by enantioselective sensing and optical manipulation, and then conclude with orbital angular momentum-dependent responses. This review will be helpful for understanding the mechanisms behind chiroptical phenomena based on underlying chiral properties and useful for interpreting chiroptical systems for further studies. Chirality (physics) ; Nanotechnology ; Optical phenomena ; Plasmon ; Metamaterial ; Physics ; Enantioselective synthesis ; Materials science ; Chemistry ; Optics ; Quantum mechanics ; Symmetry breaking Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Cheng Shen | Yanbang Chu | Quansheng Wu | Na Li | Shuopei Wang | Yanchong Zhao | Jian Tang | Jieying Liu | Jinpeng Tian | Kenji Watanabe | Takashi Taniguchi | Rong Yang | Zi Yang Meng | Dongxia Shi | Oleg V. Yazyev | Guangyu Zhang
Source: Nature Physics (article, 2020-03-30)
Field: Materials Science — Materials Chemistry

Correlated states in twisted double bilayer graphene Bilayer graphene ; Condensed matter physics ; Graphene ; Physics ; Zeeman effect ; Bilayer ; Monolayer ; Magnetic field ; Electronic band structure ; Materials science ; Nanotechnology ; Quantum mechanics ; Chemistry Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Blaithín McAdam | Margaret Brennan Fournet | Paul McDonald | Marija Mojićević
Source: Polymers (review, 2020-12-04)
Field: Materials Science — Biomaterials

Production of Polyhydroxybutyrate (PHB) and Factors Impacting Its Chemical and Mechanical Characteristics Plastic pollution is fueling the grave environmental threats currently facing humans, the animal kingdom, and the planet. The pursuit of renewable resourced biodegradable materials commenced in the 1970s with the need for carbon neutral fully sustainable products driving important progress in recent years. The development of bioplastic materials is highlighted as imperative to the solutions to our global environment challenges and to the restoration of the wellbeing of our planet. Bio-based plastics are becoming increasingly sustainable and are expected to substitute fossil-based plastics. Bioplastics currently include both, nondegradable and biodegradable compositions, depending on factors including the origins of production and post-use management and conditions. Among the most promising materials being developed and evaluated is polyhydroxybutyrate (PHB), a microbial bioprocessed polyester belonging to the polyhydroxyalkanoate (PHA) family. This biocompatible and non-toxic polymer is biosynthesized and accumulated by a number of specialized bacterial strains. The favorable mechanical properties and amenability to biodegradation when exposed to certain active biological environments, earmark PHB as a high potential replacement for petrochemical based polymers such as ubiquitous high density polyethylene (HDPE). To date, high production costs, minimal yields, production technology complexities, and difficulties relating to downstream processing are limiting factors for its progression and expansion in the marketplace. This review examines the chemical, mechanical, thermal, and crystalline characteristics of PHB, as well as various fermentation processing factors which influence the properties of PHB materials. Bioplastic ; Polyhydroxybutyrate ; Polyhydroxyalkanoates ; Biodegradable plastic ; High-density polyethylene ; Biodegradation ; Biochemical engineering ; Petrochemical ; Production (economics) ; Materials science ; Waste management ; Environmental science ; Polymer science ; Business ; Polyethylene ; Engineering ; Chemistry ; Composite material biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Microplastics and Plastic Pollution
Authors: Andrej Kitanovski
Source: Advanced Energy Materials (article, 2020-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy Applications of Magnetocaloric Materials Abstract The need for energy‐efficient and environmentally friendly refrigeration, heat pumping, air conditioning, and thermal energy harvesting systems is currently more urgent than ever. Magnetocaloric energy conversion is among the best available alternatives for achieving these technological goals and has been the subject of substantial basic and applied research over the last two decades. The subject is strongly interdisciplinary, requiring proper understanding and efficient integration of knowledge in different specialized fields. This review article presents a historical and up‐to‐date account of the energy‐related applications of magnetocaloric materials and information about their processing and magnetic fields, thermodynamics, heat transfer, and other relevant characteristics. The article also discusses the conceptual design of magnetocaloric refrigeration and power generation systems and some guidelines for future research in the field. Magnetic refrigeration ; Refrigeration ; Air conditioning ; Materials science ; Process engineering ; Energy transformation ; Field (mathematics) ; Energy (signal processing) ; Efficient energy use ; Thermodynamics ; Systems engineering ; Engineering physics ; Mechanical engineering ; Magnetic field ; Engineering ; Electrical engineering ; Physics Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Kevin Maik Jablonka | Daniele Ongari | Seyed Mohamad Moosavi | Berend Smit
Source: Chemical Reviews (article, 2020-06-10)
Field: Materials Science — Materials Chemistry

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years. Field (mathematics) ; Feature (linguistics) ; Interpretation (philosophy) ; Stability (learning theory) ; Deep learning Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Block Copolymer Self-Assembly
Authors: Alberto García | Nick Papior | Arsalan Akhtar | Emilio Artacho | Volker Blum | Emanuele Bosoni | Pedro Brandimarte | Mads Brandbyge | J. I. Cerdá | Fabiano Corsetti | Ramón Cuadrado | Vladimir Dikan | Jaime Ferrer | Julian Gale | Pablo García-Fernández | V. M. García-Suárez | Sandra García | Georg Huhs | Sergio Illera | Richard Korytár | Peter Koval | Irina Lebedeva | Lin Lin | Pablo López-Tarifa | Sara G. Mayo | Stephan Mohr | Pablo Ordejón | Andrei Postnikov | Yann Pouillon | Miguel Pruneda | Roberto Robles | Daniel Sánchez-Portal | Jose M. Soler | Rafi Ullah | Victor Wen-zhe Yu | Javier Junquera
Source: The Journal of Chemical Physics (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

S<scp>iesta</scp>: Recent developments and applications A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta's flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments. SIESTA (computer program) ; Interfacing ; Interoperability ; Workflow ; Interface (matter) ; Data structure Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Siavash Iravani | Rajender S. Varma
Source: Environmental Chemistry Letters (review, 2020-03-10)
Field: Materials Science — Materials Chemistry

Green synthesis, biomedical and biotechnological applications of carbon and graphene quantum dots. A review Nanotechnology ; Graphene ; Quantum dot ; Nanomaterials ; Carbon quantum dots ; Materials science ; Carbon fibers ; Biosensor ; Raw material ; Chemistry ; Composite number Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Sebastian Spicher | Stefan Grimme
Source: Angewandte Chemie International Edition (article, 2020-04-28)
Field: Materials Science — Materials Chemistry

Robust Atomistic Modeling of Materials, Organometallic, and Biochemical Systems Modern chemistry seems to be unlimited in molecular size and elemental composition. Metal-organic frameworks or biological macromolecules involve complex architectures and a large variety of elements. Yet, a general and broadly applicable theoretical method to describe the structures and interactions of molecules beyond the 1000-atom size regime semi-quantitatively is not self-evident. For this purpose, a generic force field named GFN-FF is presented, which is completely newly developed to enable fast structure optimizations and molecular-dynamics simulations for basically any chemical structure consisting of elements up to radon. The freely available computer program requires only starting coordinates and elemental composition as input from which, fully automatically, all potential-energy terms are constructed. GFN-FF outperforms other force fields in terms of generality and accuracy, approaching the performance of much more elaborate quantum-mechanical methods in many cases. Nanotechnology ; Materials science ; Biochemical engineering ; Computer science ; Chemistry ; Biological system ; Engineering ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Nengneng Luo | Kai Han | Matthew J. Cabral | Xiaozhou Liao | Shujun Zhang | Changzhong Liao | Guangzu Zhang | Xiyong Chen | Qin Feng | Jing‐Feng Li | Yuezhou Wei
Source: Nature Communications (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Constructing phase boundary in AgNbO3 antiferroelectrics: pathway simultaneously achieving high energy density and efficiency Dielectric capacitors with high energy storage density (W<sub>rec</sub>) and efficiency (η) are in great demand for high/pulsed power electronic systems, but the state-of-the-art lead-free dielectric materials are facing the challenge of increasing one parameter at the cost of the other. Herein, we report that high W<sub>rec</sub> of 6.3 J cm<sup>-3</sup> with η of 90% can be simultaneously achieved by constructing a room temperature M2-M3 phase boundary in (1-x)AgNbO<sub>3</sub>-xAgTaO<sub>3</sub> solid solution system. The designed material exhibits high energy storage stability over a wide temperature range of 20-150 °C and excellent cycling reliability up to 10<sup>6</sup> cycles. All these merits achieved in the studied solid solution are attributed to the unique relaxor antiferroelectric features relevant to the local structure heterogeneity and antiferroelectric ordering, being confirmed by scanning transmission electron microscopy and synchrotron X-ray diffraction. This work provides a good paradigm for developing new lead-free dielectrics for high-power energy storage applications. Materials science ; Antiferroelectricity ; Dielectric ; Phase boundary ; Capacitor ; Energy storage ; Synchrotron ; Work (physics) ; Chemical physics ; Power density ; Optoelectronics ; Phase (matter) ; Condensed matter physics ; Power (physics) ; Thermodynamics ; Optics ; Voltage ; Electrical engineering ; Ferroelectricity ; Chemistry ; Physics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials
Authors: Κateryna Loza | Marc Heggen | Matthias Epple
Source: Advanced Functional Materials (article, 2020-03-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis, Structure, Properties, and Applications of Bimetallic Nanoparticles of Noble Metals Abstract Bimetallic nanoparticles of noble metals are of high interest in imaging, biomedical devices, including nanomedicine, and heterogeneous catalysis. Synthesis, properties, characterization, biological properties, and practical applicability of nanoparticles on the basis of platinum group metals and the coin metals Ag and Au are discussed, also in comparison with the corresponding monometallic nanoparticles. In addition to the parameters that are required to characterize monometallic nanoparticles (mainly size, size distribution, shape, crystallographic nature, surface functionalization, charge), further information is required for a full characterization of bimetallic nanoparticles. This concerns the overall elemental composition of a bimetallic nanoparticle population (ratio of the two metals) and the internal distribution of the elements in individual nanoparticles (e.g., the presence of homogeneous alloys, core–shell systems, and possible intermediate stages). It is also important to ensure that all particles are identical in terms of elemental composition, that is, that the homogeneity of the particle population is given. Macroscopic properties like light absorption, antibacterial effects, and catalytic activity depend on these properties. The currently available methods for a full characterization of bimetallic nanoparticles are discussed, and future developments in this field are outlined. Bimetallic strip ; Materials science ; Nanoparticle ; Noble metal ; Characterization (materials science) ; Catalysis ; Nanotechnology ; Nanomedicine ; Population ; Particle size ; Chemical engineering ; Metal ; Metallurgy ; Chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Pavlo O. Dral
Source: The Journal of Physical Chemistry Letters (article, 2020-03-03)
Field: Materials Science — Materials Chemistry

Quantum Chemistry in the Age of Machine Learning As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning. Quantum machine learning ; Perspective (graphical) ; Field (mathematics) ; Computer science ; Artificial intelligence ; Machine learning ; Generalization ; Quantum ; Quantum chemical ; Chemistry ; Quantum computer ; Mathematics ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies