jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joseph C. A. Prentice | Jolyon Aarons | James C. Womack | Alice E. A. Allen | Λάμπρος Ανδρινόπουλος | Lucian Anton | Robert A. Bell | Arihant Bhandari | Gabriel A. Bramley | Robert J. Charlton | Rebecca J. Clements | Daniel J. Cole | Gabriel C. Constantinescu | Fabiano Corsetti | Simon M.‐M. Dubois | Kevin Kelsey Duff | José María Escartín | Andrea Greco | Quintin Hill | Louis P. Lee | Edward Linscott | David D. O’Regan | Maximillian J. S. Phipps | Laura E. Ratcliff | Álvaro Ruiz Serrano | Edward Tait | Gilberto Teobaldi | Valerio Vitale | Nelson Yeung | Tim J. Zuehlsdorff | Jacek Dziedzic | Peter D. Haynes | Nicholas D. M. Hine | Arash A. Mostofi | M. C. Payne | Chris‐Kriton Skylaris
Source: The Journal of Chemical Physics (article, 2020-05-06)
Field: Materials Science — Materials Chemistry

The <scp>ONETEP</scp> linear-scaling density functional theory program We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints. By taking advantage of localization, onetep is able to perform calculations including thousands of atoms with computational effort, which scales linearly with the number or atoms. The code has a large and diverse range of capabilities, explored in this paper, including different boundary conditions, various exchange-correlation functionals (with and without exact exchange), finite electronic temperature methods for metallic systems, methods for strongly correlated systems, molecular dynamics, vibrational calculations, time-dependent DFT, electronic transport, core loss spectroscopy, implicit solvation, quantum mechanical (QM)/molecular mechanical and QM-in-QM embedding, density of states calculations, distributed multipole analysis, and methods for partitioning charges and interactions between fragments. Calculations with onetep provide unique insights into large and complex systems that require an accurate atomic-level description, ranging from biomolecular to chemical, to materials, and to physical problems, as we show with a small selection of illustrative examples. onetep has always aimed to be at the cutting edge of method and software developments, and it serves as a platform for developing new methods of electronic structure simulation. We therefore conclude by describing some of the challenges and directions for its future developments and applications. Scaling ; Density functional theory ; Computer science ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Bohayra Mortazavi | Ivan S. Novikov | Evgeny V. Podryabinkin | Stephan Roche | Timon Rabczuk | Alexander V. Shapeev | Xiaoying Zhuang
Source: Applied Materials Today (article, 2020-05-13)
Field: Materials Science — Materials Chemistry

Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials Phonon ; Nanoporous ; Density functional theory ; Stability (learning theory) ; Dispersion (optics) ; Dispersion relation ; Thermal ; Interatomic potential Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Shu Huang | Jacqueline M. Cole
Source: Scientific Data (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

A database of battery materials auto-generated using ChemDataExtractor A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database. Computer science ; Battery (electricity) ; Property (philosophy) ; Database ; Interface (matter) ; Graphical user interface ; Information retrieval ; Data mining ; Programming language ; Power (physics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Run Hu | Sotaro Iwamoto | Lei Feng | Shenghong Ju | Shiqian Hu | Masato Ohnishi | Naomi Nagai | Kazuhiko Hirakawa | Junichiro Shiomi
Source: Physical Review X (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Machine-Learning-Optimized Aperiodic Superlattice Minimizes Coherent Phonon Heat Conduction Lattice heat conduction can be modulated via nanostructure interfaces. Although advances have been made by viewing phonons as particles, the controllability should be enhanced by fully utilizing their wave nature. By considering phonons as coherent waves, herein we design an optimized aperiodic superlattice that minimizes the coherent phonon heat conduction by alternatingly coupling coherent phonon transport calculations and machine learning. The thermal conductivity of the fabricated aperiodic superlattice agrees well with the calculations over a temperature range of 77-300 K, indicating that complex aperiodic wave interference of coherent phonons can be controlled. The thermal conductivity of the aperiodic superlattice is significantly smaller than the conventional periodic superlattice due to enhanced phonon localization. The optimized aperiodic structure is formed by connecting weakly correlated local structures that introduce interference over broad phonon frequencies. Controlling coherent phonons by aperiodic interferences opens a new route for phonon engineering. Phonon ; Superlattice ; Aperiodic graph ; Condensed matter physics ; Thermal conductivity ; Thermal conduction ; Materials science ; Physics ; Quantum mechanics ; Mathematics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Matthew Julian | Calum Williams | Stephen Borg | Scott M. Bartram | Hyun Jung Kim
Source: Optica (article, 2020-06-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reversible optical tuning of GeSbTe phase-change metasurface spectral filters for mid-wave infrared imaging Tunable narrowband spectral filtering across arbitrary optical wavebands is highly desirable in a plethora of applications, from chemical sensing and hyperspectral imaging to infrared astronomy. Yet, the ability to reconfigure the optical properties, with full reversibility, of a solid-state large-area narrowband filter remains elusive. Existing solutions require either moving parts, have slow response times, or provide limited spectral coverage. Here, we demonstrate a 1-inch diameter continuously tunable, fully reversible, all-solid-state, narrowband phase-change metasurface filter based on a GeSbTe-225 (GST)-embedded plasmonic nanohole array. The passband of the presented device is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo> </mml:mrow> <mml:mn>74</mml:mn> <mml:mspace></mml:mspace> <mml:mrow><mml:mrow><mml:mi>n</mml:mi> <mml:mi>m</mml:mi></mml:mrow> </mml:mrow> </mml:math> with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>∼</mml:mo> </mml:mrow> <mml:mn>70</mml:mn> <mml:mi>%</mml:mi> </mml:math> transmittance and operates across the 3-5 µm thermal imaging waveband. Continuous, reconfigurable tuning is achieved by exploiting intermediate GST phases via optical switching with a single nanosecond laser pulse, and material stability is verified through multiple switching cycles. We further demonstrate multispectral thermal imaging in the mid-wave infrared using our active phase-change metasurfaces. Our results pave the way for highly functional, reduced power, compact hyperspectral imaging systems and customizable optical filters for real-world system integration. Materials science ; Infrared ; Phase change ; Optoelectronics ; Optics ; Optical filter ; Metamaterial ; Phase (matter) ; Physics ; Engineering physics Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Optical and Acousto-Optic Technologies
Authors: Wuxin Sha | Yaqing Guo | Qing Yuan | Shun Tang | Xinfang Zhang | Songfeng Lu | Xin Guo | Yuan‐Cheng Cao | Shijie Cheng
Source: Advanced Intelligent Systems (article, 2020-02-11)
Field: Materials Science — Materials Chemistry

Artificial Intelligence to Power the Future of Materials Science and Engineering Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects. Computer science ; Artificial intelligence ; Realization (probability) ; Process (computing) ; Automation ; Machine learning ; Deep learning ; Calculator ; Productivity ; Applications of artificial intelligence ; Industrial engineering ; Engineering Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Fuel Cells and Related Materials
Authors: Jie Xiong | Tong‐Yi Zhang | San‐Qiang Shi
Source: Science China Technological Sciences (article, 2020-05-27)
Field: Materials Science — Metals and Alloys

Machine learning of mechanical properties of steels Tempering ; Symbolic regression ; Random forest ; Ultimate tensile strength ; Materials science ; Alloy ; Metallurgy ; Fracture (geology) ; Work (physics) ; Computer science ; Machine learning ; Composite material ; Mechanical engineering ; Engineering Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Shujing Jia | Huanglong Li | Tamihiro Gotoh | Christophe Longeaud | Bin Zhang | Juan Lyu | Shilong Lv | Min Zhu | Zhitang Song | Qi Liu | John Robertson | Ming Liu
Source: Nature Communications (article, 2020-09-15)
Field: Materials Science — Materials Chemistry

Ultrahigh drive current and large selectivity in GeS selector Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm<sup>-2</sup> and a ~10<sup>6</sup> high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field. Materials science ; Optoelectronics ; Neuromorphic engineering ; Dopant ; Semiconductor ; Non-volatile memory ; Transistor ; Current (fluid) ; Scalability ; Nanotechnology ; Electrical engineering ; Computer science ; Doping ; Voltage Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Photonic and Optical Devices
Authors: Volker L. Deringer | A. Miguel | Gábor Cśanyi
Source: Nature Communications (article, 2020-10-29)
Field: Materials Science — Materials Chemistry

A general-purpose machine-learning force field for bulk and nanostructured phosphorus Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an outstanding challenge. Here, we show that a universally applicable force field for phosphorus can be created by machine learning (ML) from a suitably chosen ensemble of quantum-mechanical results. Our model is fitted to density-functional theory plus many-body dispersion (DFT + MBD) data; its accuracy is demonstrated for the exfoliation of black and violet phosphorus (yielding monolayers of "phosphorene" and "hittorfene"); its transferability is shown for the transition between the molecular and network liquid phases. An application to a phosphorene nanoribbon on an experimentally relevant length scale exemplifies the power of accurate and flexible ML-driven force fields for next-generation materials modelling. The methodology promises new insights into phosphorus as well as other structurally complex, e.g., layered solids that are relevant in diverse areas of chemistry, physics, and materials science. Phosphorene ; Black phosphorus ; Exfoliation joint ; Density functional theory ; Phosphorus ; Transferability ; Monolayer ; Force field (fiction) ; Chemical physics ; Materials science ; Nanotechnology ; Field (mathematics) ; Computer science ; Physics ; Chemistry ; Computational chemistry ; Graphene ; Machine learning ; Artificial intelligence ; Optoelectronics ; Mathematics Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Yao Wu | Jie Guo | Rui Sun | Jie Min
Source: npj Computational Materials (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells Abstract Integrating artificial intelligence (AI) and computer science together with current approaches in material synthesis and optimization will act as an effective approach for speeding up the discovery of high-performance photoactive materials in organic solar cells (OSCs). Yet, like model selection in statistics, the choice of appropriate machine learning (ML) algorithms plays a vital role in the process of new material discovery in databases. In this study, we constructed five common algorithms, and introduced 565 donor/acceptor (D/A) combinations as training data sets to evaluate the practicalities of these ML algorithms and their application potential when guiding material design and D/A pairs screening. Thus, the best predictive capabilities are provided by using the random forest (RF) and boosted regression trees (BRT) approaches beyond other ML algorithms in the data set. Furthermore, &gt;32 million D/A pairs were screened and calculated by RF and BRT models, respectively. Among them, six photovoltaic D/A pairs are selected and synthesized to compare their predicted and experimental power conversion efficiencies. The outcome of ML and experiment verification demonstrates that the RF approach can be effectively applied to high-throughput virtual screening for opening new perspectives to design of materials and D/A pairs, thereby accelerating the development of OSCs. Random forest ; Organic solar cell ; Computer science ; Machine learning ; Throughput ; Artificial intelligence ; Virtual screening ; Photovoltaic system ; Set (abstract data type) ; Process (computing) ; Support vector machine ; Big data ; Power (physics) ; High-throughput screening ; Selection (genetic algorithm) ; Algorithm ; Data mining ; Drug discovery ; Engineering ; Chemistry ; Electrical engineering ; Physics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Mohamad Alhijazi | Qasim Zeeshan | Zhaoye Qin | Babak Safaei | Mohammed Asmael
Source: Nanotechnology Reviews (review, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Finite element analysis of natural fibers composites: A review Abstract Natural fiber composites (NFCs) also termed as biocomposites offer an alternative to the existing synthetic fiber composites, due to their advantages such as abundance in nature, relatively low cost, lightweight, high strength-to-weight ratio, and most importantly their environmental aspects such as biodegradability, renewability, recyclability, and sustainability. Researchers are investigating in depth the properties of NFC to identify their reliability and accessibility for being involved in aircrafts, automotive, marine, sports’ equipment, and other engineering fields. Modeling and simulation (M&amp;S) of NFCs is a valuable method that contributes in enhancing the design and performance of natural fibers composite. Recently many researchers have applied finite element analysis to analyze NFCs’ characteristics. This article aims to present a comprehensive review on recent developments in M&amp;S of NFCs through classifying the research according to the analysis type, NFC type, model type, simulation platform and parameters, and research outcomes, shedding the light on the main applicable theories and methods in this area, aiming to let more experts know the current research status and also provide some guidance for relevant researches. Finite element method ; Reliability (semiconductor) ; Automotive industry ; Sustainability ; Composite number ; Natural (archaeology) ; Computer science ; Fiber ; Biochemical engineering ; Fiber type ; Natural fiber ; Mechanical engineering ; Composite material ; Construction engineering ; Materials science ; Engineering ; Structural engineering ; Ecology ; Geology ; Aerospace engineering Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Cellular and Composite Structures
Authors: Sébastien Cueff | Jimmy John | Zhen Zhang | Jorge O. Parra | Jianing Sun | Régis Orobtchouk | Shriram Ramanathan | Pablo Sanchis
Source: APL Photonics (article, 2020-11-01)
Field: Materials Science — Polymers and Plastics

VO2 nanophotonics The intriguing physics of vanadium dioxide (VO2) makes it not only a fascinating object of study for fundamental research on solid-state physics but also an attractive means to actively modify the properties of integrated devices. In particular, the exceptionally large complex refractive index variation produced by the insulator-to-metal transition of this material opens up interesting opportunities to dynamically tune optical systems. This Perspective reviews some of the exciting work done on VO2 for nanophotonics in the last decade and suggests promising directions to explore for this burgeoning field. Vanadium dioxide ; Nanophotonics ; Nanotechnology ; Engineering physics ; Physics ; Materials science ; Thin film Transition Metal Oxide Nanomaterials ; Photonic Crystals and Applications ; Optical Wireless Communication Technologies
Authors: Camila Utsunomia | Qun Ren | Manfred Zinn
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-04-03)
Field: Materials Science — Biomaterials

Poly(4-Hydroxybutyrate): Current State and Perspectives By the end of 1980s, for the first time polyhydroxyalkanoate (PHA) copolymers with incorporated 4-hydroxybutyrate (4HB) units were produced in the bacterium <i>Cupriavidus necator</i> (formally <i>Ralstonia eutropha</i>) from structurally related carbon sources. After that, production of PHA copolymers composed of 3-hydroxybutyrate (3HB) and 4HB [P(3HB-<i>co</i>-4HB)] was demonstrated in diverse wild-type bacteria. The P4HB homopolymer, however, was hardly synthesized because existing bacterial metabolism on 4HB precursors also generate and incorporate 3HB. The resulting material assumes the properties of thermoplastics and elastomers depending on the 4HB fraction in the copolyester. Given the fact that P4HB is biodegradable and yield 4HB, which is a normal compound in the human body and proven to be biocompatible, P4HB has become a prospective material for medical applications, which is the only FDA approved PHA for medical applications since 2007. Different from other materials used in similar applications, high molecular weight P4HB cannot be produced via chemical synthesis. Thus, aiming at the commercial production of this type of PHA, genetic engineering was extensively applied resulting in various production strains, with the ability to convert unrelated carbon sources (e.g., sugars) to 4HB, and capable of producing homopolymeric P4HB. In 2001, Metabolix Inc. filed a patent concerning genetically modified and stable organisms, e.g., <i>Escherichia coli</i>, producing P4HB and copolymers from inexpensive carbon sources. The patent is currently hold by Tepha Inc., the only worldwide producer of commercial P4HB. To date, numerous patents on various applications of P4HB in the medical field have been filed. This review will comprehensively cover the historical evolution and the most recent publications on P4HB biosynthesis, material properties, and industrial and medical applications. Finally, perspectives for the research and commercialization of P4HB will be presented. Cupriavidus necator ; Polyhydroxyalkanoates ; Copolymer ; Materials science ; Bacteria ; Chemistry ; Biology ; Organic chemistry ; Polymer biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Electrospun Nanofibers in Biomedical Applications