jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moya A. Hay | Colette Boskovic
Source: Chemistry - A European Journal (review, 2020-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Lanthanoid Complexes as Molecular Materials: The Redox Approach The development of molecular materials with novel functionality offers promise for technological innovation. Switchable molecules that incorporate redox-active components are enticing candidate compounds due to their potential for electronic manipulation. Lanthanoid metals are most prevalent in their trivalent state and usually redox-activity in lanthanoid complexes is restricted to the ligand. The unique electronic and physical properties of lanthanoid ions have been exploited for various applications, including in magnetic and luminescent materials as well as in catalysis. Lanthanoid complexes are also promising for applications reliant on switchability, where the physical properties can be modulated by varying the oxidation state of a coordinated ligand. Lanthanoid-based redox activity is also possible, encompassing both divalent and tetravalent metal oxidation states. Thus, utilization of redox-active lanthanoid metals offers an attractive opportunity to further expand the capabilities of molecular materials. This review surveys both ligand and lanthanoid centered redox-activity in pre-existing molecular systems, including tuning of lanthanoid magnetic and photophysical properties by modulating the redox states of coordinated ligands. Ultimately the combination of redox-activity at both ligands and metal centers in the same molecule can afford novel electronic structures and physical properties, including multiconfigurational electronic states and valence tautomerism. Further targeted exploration of these features is clearly warranted, both to enhance understanding of the underlying fundamental chemistry, and for the generation of a potentially important new class of molecular material. Lanthanide ; Redox ; Chemistry ; Ligand (biochemistry) ; Molecule ; Combinatorial chemistry ; Oxidation state ; Metal ; Non-innocent ligand ; Tautomer ; Nanotechnology ; Photochemistry ; Materials science ; Ion ; Inorganic chemistry ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Santanu Ghosh | Sayantani Das | Chandra Kanta De | Diana Yepes | Frank Neese | Giovanni Bistoni | Markus Leutzsch | Benjamin List
Source: Angewandte Chemie International Edition (article, 2020-03-11)
Field: Chemistry — Organic Chemistry

Strong and Confined Acids Control Five Stereogenic Centers in Catalytic Asymmetric Diels–Alder Reactions of Cyclohexadienones with Cyclopentadiene We describe a highly enantioselective Diels-Alder reaction of cross-conjugated cyclohexadienones with cyclopentadiene, in which five stereocenters are effectively controlled by a strongly acidic and confined imidodiphosphorimidate catalyst. Our approach provides tricyclic products in excellent stereoselectivity. We also report methods to convert the obtained products into useful intermediates and a computational study that aids in gaining deeper insight into the reaction mechanism and origin of stereoselectivity. Cyclopentadiene ; Stereocenter ; Catalysis ; Chemistry ; Diels–Alder reaction ; Alder ; Organic chemistry ; Biology ; Enantioselective synthesis ; Botany Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Axial and Atropisomeric Chirality Synthesis
Authors: Trevon Badloe | Inki Kim | Junsuk Rho
Source: Scientific Reports (article, 2020-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Moth-eye shaped on-demand broadband and switchable perfect absorbers based on vanadium dioxide Two biomimetic, moth-eye structure, perfect absorbers in the visible and near infrared regions are introduced and investigated. The moth-eye structure is made up of vanadium oxide (VO<sub>2</sub>), which is a phase change material that changes from an insulator state to a metallic state at around 85 °C. The VO<sub>2</sub> structure sits on top of a sapphire (Al<sub>2</sub>O<sub>3</sub>) dielectric spacer layer, above a gold (Au) back reflector. Two perfect absorbers are designed, one with perfect absorption over an ultra-broadband range between 400 and 1,600 nm, for both the insulating and metallic phases, while the second can switch between being a perfect absorber or not in the range 1,000 and 1,600 nm. The absorption profiles and electric and magnetic fields are examined and discussed to provide insight into how absorbers function in the four different situations. Materials science ; Broadband ; Sapphire ; Absorption (acoustics) ; Optoelectronics ; Metal ; Sesquioxide ; Optics ; Composite material ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Materials and Mechanics
Authors: Yongqiang Kang | Lejing Li | Baohua Li
Source: Journal of Energy Chemistry (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry Simple (philosophy) ; Computer science ; Fidelity ; Field (mathematics) ; Property (philosophy) ; Electronic structure ; Energy (signal processing) ; Statistical physics ; Algorithm ; Theoretical computer science ; Chemistry ; Physics ; Mathematics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Zhilong Song | Xiwen Chen | Fanbin Meng | Guanjian Cheng | Chen Wang | Zhongti Sun | Wan‐Jian Yin
Source: Chinese Physics B (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

Machine learning in materials design: Algorithm and application* Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications. Interpretability ; Computer science ; Generalizability theory ; Big data ; Field (mathematics) ; Algorithm ; Artificial intelligence ; Machine learning ; Inverse ; Property (philosophy) ; Curse of dimensionality ; Data mining ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Vadim Korolev | Artem Mitrofanov | Ekaterina I. Marchenko | Nickolay N. Eremin | Valery Tkachenko | Stepan N. Kalmykov
Source: Chemistry of Materials (article, 2020-08-25)
Field: Chemistry — Inorganic Chemistry

Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to access the performance of thousands of candidates, but its accuracy is highly dependent on a partial charge assignment method. In this study, we propose a machine learning model that can reconcile the benefits of two main approaches: the high accuracy of density-derived electrostatic and chemical charge (DDEC) method and the scalability of charge equilibration (Qeq) method. The mean absolute deviation of predicted partial charges from the original DDEC counterparts achieves an excellent level of 0.01 e. The model, initially designed for metal–organic frameworks (MOFs), is also capable of assigning charges to another class of nanoporous materials, covalent organic frameworks, with acceptable accuracy. Adsorption properties of carbon dioxide, calculated by means of machine learning-derived charges, are consistent with the reference data obtained with DDEC charges. We also provide the first virtually complete set of partial charges for the publicly available subset of the Computation-Ready, Experimental (CoRE) MOF 2019 database. Nanoporous ; Scalability ; Charge (physics) ; Adsorption ; Partial charge ; Relative standard deviation ; Computer science ; Charge density ; Electrostatics ; Absolute deviation ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Physical chemistry ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Han‐Chun Wu | Jingdong Guo | De’an Yang
Source: Journal of Material Science and Technology (article, 2020-02-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Facile autoreduction synthesis of core-shell Bi-Bi2O3/CNT with 3-dimensional neural network structure for high-rate performance supercapacitor Core-shell Bi-Bi2O3/CNT (carbon nanotube) with 3-dimensional neural network structure where Bi-Bi2O3 nanospheres act as cell bodies supported by a 3-dimensional network of CNTs acting as synapses is designed and prepared by simple solvothermal method and subsequent annealing autoreduction treatment, and this structure facilitates the efficient transport of electrons. It can provide two electron transfer paths due to the double contact of Bi2O3 shell with CNT and metal Bi core which enhances the efficiency of the electrochemical reaction. The Bi-Bi2O3/CNT electrode shows a high gravimetric capacitance of 850 F g−1 (1 A g−1), and the specific capacitance of Bi-Bi2O3/CNT can be still 714 F g−1 at 30 A g−1 indicating excellent rate performance. The asymmetric supercapacitor is assembled with Bi-Bi2O3/CNT as the negative electrode and Ni(OH)2/CNT as the positive electrode, delivering a high energy density of 36.7 Wh kg−1 and a maximum power density of 8000 W kg−1. Therefore, the core-shell Bi-Bi2O3/CNT with 3-dimensional neural network structure as the negative electrode of supercapacitor shows great potential in the field of energy storage in the future. Supercapacitor ; Materials science ; Capacitance ; Electrode ; Carbon nanotube ; Electrochemistry ; Nanotechnology ; Chemical engineering ; Annealing (glass) ; Gravimetric analysis ; Electron transfer ; Composite material ; Photochemistry ; Chemistry ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced battery technologies research
Authors: Nicholas E. Jackson
Source: The Journal of Physical Chemistry B (article, 2020-12-28)
Field: Materials Science — Materials Chemistry

Coarse-Graining Organic Semiconductors: The Path to Multiscale Design Four decades of molecular theory and computation have helped form the modern understanding of the physical chemistry of organic semiconductors. Whereas these efforts have historically centered around characterizations of electronic structure at the single-molecule or dimer scale, emerging trends in noncrystalline molecular and polymeric semiconductors are motivating the need for modeling techniques capable of morphological and electronic structure predictions at the mesoscale. Provided the challenges associated with these prediction tasks, the community has begun to evolve a computational toolkit for organic semiconductors incorporating techniques from the fields of soft matter, coarse-graining, and machine learning. Here, we highlight recent advances in coarse-grained methodologies aimed at the multiscale characterization of noncrystalline organic semiconductors. As organic semiconductor performance is dependent on the interplay of mesoscale morphology and molecular electronic structure, specific emphasis is placed on coarse-grained modeling approaches capable of both structural and electronic predictions without recourse to all-atom representations. Granularity ; Semiconductor ; Organic semiconductor ; Multiscale modeling ; Computation ; Nanotechnology ; Materials science ; Electronic structure ; Computer science ; Characterization (materials science) ; Statistical physics ; Engineering physics ; Physics ; Computational chemistry ; Chemistry ; Optoelectronics ; Algorithm Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Block Copolymer Self-Assembly
Authors: Ruggero Lot | Franco Pellegrini | Yusuf Shaidu | Emine Küçükbenli
Source: Computer Physics Communications (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

PANNA: Properties from Artificial Neural Network Architectures Computer science ; Artificial neural network ; Flexibility (engineering) ; Artificial intelligence ; Field (mathematics) ; Software ; Machine learning ; Computer architecture ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Jeong Rae Kim | Jinhyuk Jang | Kyoung-June Go | Se Young Park | Chang Jae Roh | John Bonini | Jinkwon Kim | Han Gyeol Lee | Karin M. Rabe | Jong Seok Lee | Si‐Young Choi | Tae Won Noh | Daesu Lee
Source: Nature Communications (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

Stabilizing hidden room-temperature ferroelectricity via a metastable atomic distortion pattern Nonequilibrium atomic structures can host exotic and technologically relevant properties in otherwise conventional materials. Oxygen octahedral rotation forms a fundamental atomic distortion in perovskite oxides, but only a few patterns are predominantly present at equilibrium. This has restricted the range of possible properties and functions of perovskite oxides, necessitating the utilization of nonequilibrium patterns of octahedral rotation. Here, we report that a designed metastable pattern of octahedral rotation leads to robust room-temperature ferroelectricity in CaTiO<sub>3</sub>, which is otherwise nonpolar down to 0 K. Guided by density-functional theory, we selectively stabilize the metastable pattern, distinct from the equilibrium pattern and cooperative with ferroelectricity, in heteroepitaxial films of CaTiO<sub>3</sub>. Atomic-scale imaging combined with deep neural network analysis confirms a close correlation between the metastable pattern and ferroelectricity. This work reveals a hidden but functional pattern of oxygen octahedral rotation and opens avenues for designing multifunctional materials. Metastability ; Ferroelectricity ; Octahedron ; Perovskite (structure) ; Materials science ; Rotation (mathematics) ; Non-equilibrium thermodynamics ; Chemical physics ; Condensed matter physics ; Atomic units ; Density functional theory ; Nanotechnology ; Crystallography ; Physics ; Crystal structure ; Chemistry ; Thermodynamics ; Computational chemistry ; Optoelectronics ; Computer science ; Dielectric Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Yinan Shu | Donald G. Truhlar
Source: Journal of Chemical Theory and Computation (article, 2020-09-04)
Field: Materials Science — Materials Chemistry

Diabatization by Machine Intelligence Understanding nonadiabatic dynamics is important for chemical and physical processes involving multiple electronic states. Direct nonadiabatic dynamics simulations are often employed to observe such processes on a femtosecond time scale. One often needs to do the simulation on a longer time scale, but direct simulation based on electronic structure calculations of the surfaces and couplings is expensive due to the large number of electronic structure calculations needed for ensemble averaging or simulation of longer-time processes. An alternative approach is to construct an analytical representation of potential energy surfaces (PESs) and couplings, which allows for faster dynamics calculations. Diabatic representations are preferred for such purposes because of the smoothness of the surfaces and couplings and the scalar nature of the couplings. However, many diabatization procedures are complicated by the need to consider orbitals or vector coupling elements, and these can make the process very labor-intensive. To circumvent these difficulties, we here propose diabatization by a deep neural network (DDNN) based on a new architecture for a deep neural network that requires neither orbital input nor vector input. The DDNN method allows convenient and semiautomatic diabatization, and it is demonstrated here for a model problem and for producing diabatic potential energy matrices for thiophenol. Diabatic ; Computer science ; Electronic structure ; Statistical physics ; Scalar (mathematics) ; Potential energy ; Coupling (piping) ; Representation (politics) ; Classical mechanics ; Physics ; Adiabatic process ; Quantum mechanics ; Mathematics ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ahmed Belaadi | Messaouda Boumaaza | Salah Amroune | Mostefa Bourchak
Source: The International Journal of Advanced Manufacturing Technology (article, 2020-10-22)
Field: Materials Science — Polymers and Plastics

Mechanical characterization and optimization of delamination factor in drilling bidirectional jute fibre-reinforced polymer biocomposites Delamination (geology) ; Materials science ; Drill ; Response surface methodology ; Drilling ; Composite material ; Epoxy ; Design of experiments ; Mathematics ; Metallurgy ; Computer science ; Geology ; Machine learning Natural Fiber Reinforced Composites ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Song Wang | Yi Li | Sheng Dai | De‐en Jiang
Source: Angewandte Chemie International Edition (article, 2020-06-02)
Field: Chemistry — Inorganic Chemistry

Prediction by Convolutional Neural Networks of CO<sub>2</sub>/N<sub>2</sub> Selectivity in Porous Carbons from N<sub>2</sub> Adsorption Isotherm at 77 K Porous carbons are an important class of porous materials with many applications, including gas separation. An N<sub>2</sub> adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N<sub>2</sub> adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N<sub>2</sub> isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO<sub>2</sub> /N<sub>2</sub> as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO<sub>2</sub> /N<sub>2</sub> selectivity. Porous carbons with a bimodal pore-size distribution of well-separated mesopores (3-7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications. Porosity ; Adsorption ; Selectivity ; Mesoporous material ; Materials science ; Porous medium ; Sorption isotherm ; Characterisation of pore space in soil ; Chemical engineering ; Chemistry ; Physical chemistry ; Composite material ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: K. R. Sumesh | K. Kanthavel
Source: Journal of Industrial Textiles (article, 2020-06-03)
Field: Materials Science — Polymers and Plastics

Grey relational optimization for factors influencing tensile, flexural, and impact properties of hybrid sisal banana fiber epoxy composites This research investigates factors contributing mechanical stability of epoxy based composites using Taguchi optimization based grey relational approach. Natural fibers of sisal, banana reinforcement along with epoxy was used as the matrix material for this compression molding technique. The grey relational results observed 7th and 14th trials were having first and second position in mechanical properties: 10% sisal, 15% banana, 8% NaOH, 10 MPa pressure, and 100°C temperature composites; 20% sisal, 10% banana, 5% NaOH, 10 MPa pressure, and 120°C temperature were the combinations. The signal to noise ratio showed the optimized data as 20% sisal, 15% banana, 5% NaOH, 10 MPa pressure, and 100°C temperature. The relation between experimental and predicted grey relational grade using artificial neural network showed good correlation stating the network topology of 5-5-1 with good predictability for analyzing factors influencing mechanical properties. SEM analysis observed pullouts of fiber, breakage in the matrix, and these two causing voids in the single reinforcement substitution using sisal and banana. Hybridization using both the fiber reduces the surface abnormalities and adds to the properties. SISAL ; Materials science ; Epoxy ; Composite material ; Grey relational analysis ; Ultimate tensile strength ; Taguchi methods ; Flexural strength ; Compression molding ; Fiber ; Molding (decorative) ; Natural fiber ; Mathematics ; Mold Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis