jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: G. Jeffrey Snyder | Alemayouh H. Snyder | Max Wood | Ramya Gurunathan | Berhanu H. Snyder | Changning Niu
Source: Advanced Materials (article, 2020-05-14)
Field: Materials Science — Materials Chemistry

Weighted Mobility Engineering semiconductor devices requires an understanding of charge carrier mobility. Typically, mobilities are estimated using Hall effect and electrical resistivity meausrements, which are are routinely performed at room temperature and below, in materials with mobilities greater than 1 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> . With the availability of combined Seebeck coefficient and electrical resistivity measurement systems, it is now easy to measure the weighted mobility (electron mobility weighted by the density of electronic states). A simple method to calculate the weighted mobility from Seebeck coefficient and electrical resistivity measurements is introduced, which gives good results at room temperature and above, and for mobilities as low as 10<sup>-3</sup> cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> , [Formula: see text] Here, μ<sub>w</sub> is the weighted mobility, ρ is the electrical resistivity measured in mΩ cm, T is the absolute temperature in K, S is the Seebeck coefficient, and k<sub>B</sub> /e = 86.3 µV K<sup>-1</sup> . Weighted mobility analysis can elucidate the electronic structure and scattering mechanisms in materials and is particularly helpful in understanding and optimizing thermoelectric systems. Seebeck coefficient ; Electrical resistivity and conductivity ; Materials science ; Electron mobility ; Semiconductor ; Thermoelectric effect ; Hall effect ; Thermoelectric materials ; Charge carrier ; Condensed matter physics ; Electrical mobility ; Optoelectronics ; Thermodynamics ; Thermal conductivity ; Electric field ; Electrical engineering ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Transition Metal Oxide Nanomaterials
Authors: Frank Noé | Alexandre Tkatchenko | Klaus-Robert Müller | Cecilia Clementi
Source: Annual Review of Physical Chemistry (article, 2020-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning for Molecular Simulation Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for an ML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. Artificial neural network ; Generative grammar ; Focus (optics) ; Interface (matter) ; Molecular dynamics ; Energy (signal processing) ; Molecular binding Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Ana I. Pérez-Jiménez | Danya Lyu | Zhixuan Lu | Guokun Liu | Bin Ren
Source: Chemical Science (review, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy: benefits, trade-offs and future developments Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique with sensitivity down to the single molecule level that provides fine molecular fingerprints, allowing for direct identification of target analytes. Extensive theoretical and experimental research, together with continuous development of nanotechnology, has significantly broadened the scope of SERS and made it a hot research field in chemistry, physics, materials, biomedicine, and so on. However, SERS has not been developed into a routine analytical technique, and continuous efforts have been made to address the problems preventing its real-world application. The present minireview focuses on analyzing current and potential strategies to tackle problems and realize the SERS performance necessary for translation to practical applications. Nanotechnology ; Surface-enhanced Raman spectroscopy ; Scope (computer science) ; Raman spectroscopy ; Biomedicine ; Computer science ; Materials science ; Physics ; Raman scattering ; Bioinformatics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Zhonglei Ma | Songlei Kang | Jianzhong Ma | Liang Shao | Yali Zhang | Chao Liu | Ajing Wei | Xiaolian Xiang | Linfeng Wei | Junwei Gu
Source: ACS Nano (article, 2020-07-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultraflexible and Mechanically Strong Double-Layered Aramid Nanofiber–Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene/Silver Nanowire Nanocomposite Papers for High-Performance Electromagnetic Interference Shielding High-performance electromagnetic interference (EMI) shielding materials with ultraflexibility, outstanding mechanical properties, and superior EMI shielding performances are highly desirable for modern integrated electronic and telecommunication systems in areas such as aerospace, military, artificial intelligence, and smart and wearable electronics. Herein, ultraflexible and mechanically strong aramid nanofiber-Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene/silver nanowire (ANF-MXene/AgNW) nanocomposite papers with double-layered structures are fabricated <i>via</i> the facile two-step vacuum-assisted filtration followed by hot-pressing approach. The resultant double-layered nanocomposite papers with a low MXene/AgNW content of 20 wt % exhibit an excellent electrical conductivity of 922.0 S·cm<sup>-1</sup>, outstanding mechanical properties with a tensile strength of 235.9 MPa and fracture strain of 24.8%, superior EMI shielding effectiveness (EMI SE) of 48.1 dB, and high EMI SE/t of 10 688.9 dB·cm<sup>-1</sup>, benefiting from the highly efficient double-layered structures, high-performance ANF substrate, and extensive hydrogen-bonding interactions. Particularly, the nanocomposite papers show a maximum electrical conductivity of 3725.6 S·cm<sup>-1</sup> and EMI SE of ∼80 dB at a MXene/AgNW content of 80 wt % with an absorption-dominant shielding mechanism owing to the massive ohmic losses in the highly conductive MXene/AgNW layer, multiple internal reflections between Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene nanosheets and polarization relaxation of localized defects, and abundant terminal groups. Compared with the homogeneously blended ones, the double-layered nanocomposite papers possess greater advantages in electrical, mechanical, and EMI shielding performances. Moreover, the multifunctional double-layered nanocomposite papers exhibit excellent thermal management performances such as high Joule heating temperature at low supplied voltages, rapid response time, sufficient heating stability, and reliability. The results indicate that the double-layered nanocomposite papers have excellent potential for high-performance EMI shielding and thermal management applications in aerospace, military, artificial intelligence, and smart and wearable electronics. Materials science ; Nanocomposite ; Electromagnetic shielding ; EMI ; Nanofiber ; MXenes ; Composite material ; Electromagnetic interference ; Nanowire ; Optoelectronics ; Nanotechnology ; Electrical engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Aluminum Alloys Composites Properties
Authors: Yuzhi Zhang | Haidi Wang | Weijie Chen | Jinzhe Zeng | Linfeng Zhang | Han Wang | E Weinan
Source: Computer Physics Communications (article, 2020-02-11)
Field: Materials Science — Materials Chemistry

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models Deep learning ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Eugene Muratov | Jürgen Bajorath | Robert P. Sheridan | Igor V. Tetko | Dmitry Filimonov | Vladimir Poroikov | Tudor I. Oprea | Igor I. Baskin | Alexandre Varnek | Adrián E. Roitberg | Olexandr Isayev | Stefano Curtalolo | Denis Fourches | Yoram Cohen | Alán Aspuru‐Guzik | David A. Winkler | Dimitris K. Agrafiotis | Artem Cherkasov | Alexander Tropsha
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

QSAR without borders Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge. Quantitative structure–activity relationship ; Computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Eva Y. Andrei | Allan H. MacDonald
Source: Nature Materials (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

Graphene bilayers with a twist Bilayer graphene ; Graphene ; Twist ; Bilayer ; Fermi level ; Quantum Hall effect ; Density of states ; Electron Graphene research and applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: Gaofeng Shao | Dorian Hanaor | Xiaodong Shen | Aleksander Gurlo
Source: Advanced Materials (review, 2020-03-12)
Field: Materials Science — Biomaterials

Freeze Casting: From Low‐Dimensional Building Blocks to Aligned Porous Structures—A Review of Novel Materials, Methods, and Applications Freeze casting, also known as ice templating, is a particularly versatile technique that has been applied extensively for the fabrication of well-controlled biomimetic porous materials based on ceramics, metals, polymers, biomacromolecules, and carbon nanomaterials, endowing them with novel properties and broadening their applicability. The principles of different directional freeze-casting processes are described and the relationships between processing and structure are examined. Recent progress in freeze-casting assisted assembly of low dimensional building blocks, including graphene and carbon nanotubes, into tailored micro- and macrostructures is then summarized. Emerging trends relating to novel materials as building blocks and novel freeze-cast geometries-beads, fibers, films, complex macrostructures, and nacre-mimetic composites-are presented. Thereafter, the means by which aligned porous structures and nacre mimetic materials obtainable through recently developed freeze-casting techniques and low-dimensional building blocks can facilitate material functionality across multiple fields of application, including energy storage and conversion, environmental remediation, thermal management, and smart materials, are discussed. Materials science ; Nanotechnology ; Nanomaterials ; Carbon nanotube ; Casting ; Porosity ; Fabrication ; Polymer ; Ceramic ; Graphene ; Porous medium ; Composite material Calcium Carbonate Crystallization and Inhibition ; Solar-Powered Water Purification Methods ; nanoparticles nucleation surface interactions
Authors: Ye Chen | Zhuangchai Lai | Xiao Zhang | Zhanxi Fan | Qiyuan He | Chaoliang Tan | Hua Zhang
Source: Nature Reviews Chemistry (review, 2020-04-01)
Field: Materials Science — Materials Chemistry

Phase engineering of nanomaterials Nanomaterials ; Nanotechnology ; Materials science ; Phase (matter) ; Chemistry Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications ; ZnO doping and properties
Authors: Mario Krenn | Florian Häse | AkshatKumar Nigam | Pascal Friederich | Alán Aspuru‐Guzik
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on human intuition and high-throughput virtual screening. The last few years have seen the emergence of significant interest in computer-inspired designs based on evolutionary or deep learning methods. The major challenge here is that the standard strings molecular representation SMILES shows substantial weaknesses in that task because large fractions of strings do not correspond to valid molecules. Here, we solve this problem at a fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100\% robust. Every SELFIES string corresponds to a valid molecule, and SELFIES can represent every molecule. SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid. In our experiments, the model's internal memory stores two orders of magnitude more diverse molecules than a similar test with SMILES. Furthermore, as all molecules are valid, it allows for explanation and interpretation of the internal working of the generative models. Computer science ; String (physics) ; Representation (politics) ; Intuition ; Theoretical computer science ; Generative model ; Artificial intelligence ; Generative grammar ; Machine learning ; Cognitive science ; Physics ; Theoretical physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Giovanni Bussi | Alessandro Laio
Source: Nature Reviews Physics (review, 2020-03-06)
Field: Materials Science — Materials Chemistry

Using metadynamics to explore complex free-energy landscapes Metadynamics ; Complex system ; Computer science ; Scope (computer science) ; Molecular dynamics ; Energy (signal processing) ; Statistical physics ; Energy landscape ; Set (abstract data type) ; Artificial intelligence ; Chemistry ; Mathematics ; Computational chemistry ; Physics ; Statistics ; Thermodynamics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Protein Structure and Dynamics
Authors: Parnia Forouzandeh | Vignesh Kumaravel | Suresh C. Pillai
Source: Catalysts (review, 2020-08-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrode Materials for Supercapacitors: A Review of Recent Advances The advanced electrochemical properties, such as high energy density, fast charge–discharge rates, excellent cyclic stability, and specific capacitance, make supercapacitor a fascinating electronic device. During recent decades, a significant amount of research has been dedicated to enhancing the electrochemical performance of the supercapacitors through the development of novel electrode materials. In addition to highlighting the charge storage mechanism of the three main categories of supercapacitors, including the electric double-layer capacitors (EDLCs), pseudocapacitors, and the hybrid supercapacitors, this review describes the insights of the recent electrode materials (including, carbon-based materials, metal oxide/hydroxide-based materials, and conducting polymer-based materials, 2D materials). The nanocomposites offer larger SSA, shorter ion/electron diffusion paths, thus improving the specific capacitance of supercapacitors (SCs). Besides, the incorporation of the redox-active small molecules and bio-derived functional groups displayed a significant effect on the electrochemical properties of electrode materials. These advanced properties provide a vast range of potential for the electrode materials to be utilized in different applications such as in wearable/portable/electronic devices such as all-solid-state supercapacitors, transparent/flexible supercapacitors, and asymmetric hybrid supercapacitors. Supercapacitor ; Pseudocapacitor ; Materials science ; Capacitance ; Nanotechnology ; Electrode ; Energy storage ; Electrochemistry ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: E. Aprà | E. J. Bylaska | W. A. de Jong | N. Govind | K. Kowalski | T. P. Straatsma | M. Valiev | H. J. J. van Dam | Y. Alexeev | J. Anchell | V. Anisimov | F. W. Aquino | R. Atta-Fynn | J. Autschbach | N. P. Bauman | J. C. Becca | D. E. Bernholdt | K. Bhaskaran-Nair | S. Bogatko | P. Borowski | J. Boschen | J. Brabec | A. Bruner | E. Cauët | Y. Chen | G. N. Chuev | C. J. Cramer | J. Daily | M. J. O. Deegan | T. H. Dunning | M. Dupuis | K. G. Dyall | G. I. Fann | S. A. Fischer | A. Fonari | H. Früchtl | L. Gagliardi | J. Garza | N. Gawande | S. Ghosh | K. Glaesemann | A. W. Götz | J. Hammond | V. Helms | E. D. Hermes | K. Hirao | S. Hirata | M. Jacquelin | L. Jensen | B. G. Johnson | H. Jónsson | R. A. Kendall | M. Klemm | R. Kobayashi | V. Konkov | S. Krishnamoorthy | M. Krishnan | Z. Lin | R. D. Lins | R. J. Littlefield | A. J. Logsdail | K. Lopata | W. Ma | A. V. Marenich | J. Martin del Campo | D. Mejia-Rodriguez | J. E. Moore | J. M. Mullin | T. Nakajima | D. R. Nascimento | J. A. Nichols | P. J. Nichols | J. Nieplocha | A. Otero-de-la-Roza | B. Palmer | A. Panyala | T. Pirojsirikul | B. Peng | R. Peverati | J. Pittner | L. Pollack | R. M. Richard | P. Sadayappan | G. C. Schatz | W. A. Shelton | D. W. Silverstein | D. M. A. Smith | T. A. Soares | D. Song | M. Swart | H. L. Taylor | G. S. Thomas | V. Tipparaju | D. G. Truhlar | K. Tsemekhman | T. Van Voorhis | Á. Vázquez-Mayagoitia | P. Verma | O. Villa | A. Vishnu
Source: The Journal of Chemical Physics (article, 2020-05-11)
Field: Materials Science — Materials Chemistry

NWChem: Past, present, and future Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook. Computational model ; Electronic structure ; Scaling ; Complex system ; Electronic systems ; Computational complexity theory ; Power (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Wei‐Rong Cui | Cheng-Rong Zhang | Wei Jiang | Fangfang Li | Ru‐Ping Liang | Juewen Liu | Jian‐Ding Qiu
Source: Nature Communications (article, 2020-01-23)
Field: Materials Science — Materials Chemistry

Regenerable and stable sp2 carbon-conjugated covalent organic frameworks for selective detection and extraction of uranium Uranium is a key element in the nuclear industry, but its unintended leakage has caused health and environmental concerns. Here we report a sp<sup>2</sup> carbon-conjugated fluorescent covalent organic framework (COF) named TFPT-BTAN-AO with excellent chemical, thermal and radiation stability is synthesized by integrating triazine-based building blocks with amidoxime-substituted linkers. TFPT-BTAN-AO shows an exceptional UO<sub>2</sub><sup>2+</sup> adsorption capacity of 427 mg g<sup>-1</sup> attributable to the abundant selective uranium-binding groups on the highly accessible pore walls of open 1D channels. In addition, it has an ultra-fast response time (2 s) and an ultra-low detection limit of 6.7 nM UO<sub>2</sub><sup>2+</sup> suitable for on-site and real-time monitoring of UO<sub>2</sub><sup>2+</sup>, allowing not only extraction but also monitoring the quality of the extracted water. This study demonstrates great potential of fluorescent COFs for radionuclide detection and extraction. By rational designing target ligands, this strategy can be extended to the detection and extraction of other contaminants. Uranium ; Detection limit ; Covalent bond ; Conjugated system ; Extraction (chemistry) ; Fluorescence ; Adsorption ; Covalent organic framework ; Triazine ; Chemistry ; Combinatorial chemistry ; Chromatography ; Materials science ; Organic chemistry ; Polymer Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications