jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ligang Sun | Xiaoqiao He | Jian Lü
Source: npj Computational Materials (review, 2018-01-19)
Field: Materials Science — Materials Chemistry

Nanotwinned and hierarchical nanotwinned metals: a review of experimental, computational and theoretical efforts Abstract The recent studies on nanotwinned (NT) and hierarchical nanotwinned (HNT) face-centered cubic (FCC) metals are presented in this review. The HNT structures have been supposed as a kind of novel structure to bring about higher strength/ductility than NT counterparts in crystalline materials. We primarily focus on the recent developments of the experimental, atomistic and theoretical studies on the NT and HNT structures in the metallic materials. Some advanced bottom-up and top-down techniques for the fabrication of NT and HNT structures are introduced. The deformation induced HNT structures are available by virtue of severe plastic deformation (SPD) based techniques while the synthesis of growth HNT structures is so far almost unavailable. In addition, some representative molecular dynamics (MD) studies on the NT and HNT FCC metals unveil that the nanoscale effects such as twin spacing, grain size and plastic anisotropy greatly alter the performance of NT and HNT metals. The HNT structures may initiate unique phenomena in comparison with the NT ones. Furthermore, based on the phenomena and mechanisms revealed by experimental and MD simulation observations, a series of theoretical models have been proposed. They are effective to describe the mechanical behaviors of NT and HNT metals within the applicable scope. So far the development of manufacturing technologies of HNT structures, as well as the studies on the effects of HNT structures on the properties of metals are still in its infancy. Further exploration is required to promote the design of advanced materials. Materials science ; Nanotechnology ; Ductility (Earth science) ; Fabrication ; Deformation (meteorology) ; Mechanical engineering ; Metallurgy ; Engineering ; Composite material Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Koushik Nagasubramanian | Sarah E. Jones | Soumik Sarkar | Asheesh K. Singh | Arti Singh | Baskar Ganapathysubramanian
Source: Plant Methods (article, 2018-10-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean. Hyperspectral imaging ; Support vector machine ; Identification (biology) ; Ideotype ; Biology ; Crop ; Biotechnology ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Agronomy ; Botany Spectroscopy and Chemometric Analyses ; Plant Disease Management Techniques ; Plant Pathogens and Fungal Diseases
Authors: Franck Le Vaillant | Marion Garreau | Stefano Nicolai | Ganna Gryn’ova | Clémence Corminbœuf | Jérôme Waser
Source: Chemical Science (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Fine-tuned organic photoredox catalysts for fragmentation-alkynylation cascades of cyclic oxime ethers Fine-tuned organic photoredox catalysts are introduced for the metal-free alkynylation of alkylnitrile radicals generated <i>via</i> oxidative ring opening of cyclic alkylketone oxime ethers. The redox properties of the dyes were determined by both cyclic voltammetry and computation and covered an existing gap in the oxidation potential of photoredox organocatalysts. Alkynylation ; Fragmentation (computing) ; Catalysis ; Chemistry ; Oxime ; Photochemistry ; Organic chemistry ; Computer science Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Advanced Photocatalysis Techniques
Authors: Thuong T. Nguyen | Eszter Székely | Giulio Imbalzano | Jörg Behler | Gábor Csányi | Michele Ceriotti | Andreas W. Götz | Francesco Paesani
Source: The Journal of Chemical Physics (article, 2018-04-09)
Field: Materials Science — Materials Chemistry

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enables routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body (MB) expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials (PIPs), neural networks, and Gaussian approximation potentials (GAPs) in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, Behler-Parrinello neural network-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms. Invariant (physics) ; Gaussian ; Representation (politics) ; Energy (signal processing) ; Artificial neural network ; Experimental data ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Abhijith Gopakumar | Prasanna V. Balachandran | Dezhen Xue | J. E. Gubernatis | Turab Lookman
Source: Scientific Reports (article, 2018-02-21)
Field: Materials Science — Materials Chemistry

Multi-objective Optimization for Materials Discovery via Adaptive Design Guiding experiments to find materials with targeted properties is a crucial aspect of materials discovery and design, and typically multiple properties, which often compete, are involved. In the case of two properties, new compounds are sought that will provide improvement to existing data points lying on the Pareto front (PF) in as few experiments or calculations as possible. Here we address this problem by using the concept and methods of optimal learning to determine their suitability and performance on three materials data sets; an experimental data set of over 100 shape memory alloys, a data set of 223 M<sub>2</sub>AX phases obtained from density functional theory calculations, and a computational data set of 704 piezoelectric compounds. We show that the Maximin and Centroid design strategies, based on value of information criteria, are more efficient in determining points on the PF from the data than random selection, pure exploitation of the surrogate model prediction or pure exploration by maximum uncertainty from the learning model. Although the datasets varied in size and source, the Maximin algorithm showed superior performance across all the data sets, particularly when the accuracy of the machine learning model fits were not high, emphasizing that the design appears to be quite forgiving of relatively poor surrogate models. Minimax ; Computer science ; Centroid ; Set (abstract data type) ; Selection (genetic algorithm) ; Multi-objective optimization ; Data point ; Data set ; Data mining ; Machine learning ; Mathematical optimization ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Qiang Zhu | Amit Samanta | Bingxi Li | Robert E. Rudd | Timofey Frolov
Source: Nature Communications (article, 2018-01-26)
Field: Materials Science — Materials Chemistry

Predicting phase behavior of grain boundaries with evolutionary search and machine learning The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon. Grain boundary ; Misorientation ; Bottleneck ; Cluster analysis ; Boundary (topology) ; Metastability ; Field (mathematics) ; Phase (matter) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Block Copolymer Self-Assembly
Authors: Dongfen Yuan | Hua He | Yun Wu | Jianghong Fan | Yanguang Cao
Source: Journal of Pharmaceutical Sciences (review, 2018-10-29)
Field: Materials Science — Biomaterials

Physiologically Based Pharmacokinetic Modeling of Nanoparticles Physiologically based pharmacokinetic modelling ; Pharmacokinetics ; Disposition ; Systemic circulation ; Pharmacology ; Nanoparticle ; Chemistry ; Pharmacodynamics ; In vivo ; Nanotechnology ; Materials science ; Medicine ; Biology ; Biotechnology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Field-Flow Fractionation Techniques
Authors: M. Clara Gonçalves
Source: Molecules (review, 2018-08-13)
Field: Materials Science — Materials Chemistry

Sol-Gel Silica Nanoparticles in Medicine: A Natural Choice. Design, Synthesis and Products Silica is one of the most abundant minerals in the Earth's crust, and over time it has been introduced first into human life and later into engineering. Silica is present in the food chain and in the human body. As a biomaterial, silica is widely used in dentistry, orthopedics, and dermatology. Recently amorphous sol-gel SiO₂ nanoparticles (NPs) have appeared as nanocarriers in a wide range of medical applications, namely in drug/gene target <i>delivery</i> and imaging <i>diagnosis</i>, where they stand out for their high biocompatibility, hydrophilicity, enormous flexibility for surface modification with a high payload capacity, and prolonged blood circulation time. The sol-gel process is an extremely versatile <i>bottom-up</i> methodology used in the synthesis of silica NPs, offering a great variety of chemical possibilities, such as high homogeneity and purity, along with full scale pH processing. By introducing organic functional groups or surfactants during the sol-gel process, <i>ORMOSIL</i> NPs or mesoporous NPs are produced. Colloidal route, biomimetic synthesis, solution route and template synthesis (the main sol-gel methods to produce monosized silica nanoparticles) are compared and discussed. This short review goes over some of the emerging approaches in the field of non-porous sol-gel silica NPs aiming at medical applications, centered on the syntheses processes used. Nanotechnology ; Materials science ; Drug delivery ; Mesoporous silica ; Sol-gel ; Nanoparticle ; Biocompatibility ; Nanocarriers ; Chemical engineering ; Mesoporous material ; Chemistry ; Organic chemistry ; Catalysis Mesoporous Materials and Catalysis ; Diatoms and Algae Research ; Bone Tissue Engineering Materials
Authors: Esther Cazares-Cortes | Sonia Engroba Cabana | Charlotte Boitard | Émilie Nehlig | Nébéwia Griffete | Jérôme Fresnais | Claire Wilhelm | Ali Abou‐Hassan | Christine Ménager
Source: Advanced Drug Delivery Reviews (review, 2018-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent insights in magnetic hyperthermia: From the “hot-spot” effect for local delivery to combined magneto-photo-thermia using magneto-plasmonic hybrids Photothermal therapy ; Hyperthermia ; Magnetic hyperthermia ; Superparamagnetism ; Magnetic nanoparticles ; Materials science ; Hot spot (computer programming) ; Magneto ; Nanotechnology ; Drug delivery ; Plasmon ; Plasmonic nanoparticles ; Nanoparticle ; Magnetic field ; Optoelectronics ; Magnet ; Medicine ; Magnetization ; Physics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles
Authors: F. Guillou | Arjun K. Pathak | Durga Paudyal | Yaroslav Mudryk | F. Wilhelm | Andreï Rogalev | V. K. Pecharsky
Source: Nature Communications (article, 2018-07-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-hysteretic first-order phase transition with large latent heat and giant low-field magnetocaloric effect Abstract First-order magnetic transitions (FOMTs) with a large discontinuity in magnetization are highly sought in the development of advanced functional magnetic materials. Isosymmetric magnetoelastic FOMTs that do not perturb crystal symmetry are especially rare, and only a handful of material families, almost exclusively transition metal-based, are known to exhibit them. Yet, here we report a surprising isosymmetric FOMT in a rare-earth intermetallic, Eu 2 In. What makes this transition in Eu 2 In even more remarkable is that it is associated with a large latent heat and an exceptionally high magnetocaloric effect in low magnetic fields, but with tiny lattice discontinuities and negligible hysteresis. An active role of the Eu-5 d and In-4 p states and a rather unique electronic structure borne by In to Eu charge transfer, altogether result in an unusual exchange mechanism that both sets the transition in motion and unveils an approach toward developing specific magnetic functionalities ad libitum. Magnetic refrigeration ; Condensed matter physics ; Magnetization ; Phase transition ; Intermetallic ; Classification of discontinuities ; Magnetic field ; Discontinuity (linguistics) ; Materials science ; Translational symmetry ; Latent heat ; Hysteresis ; Physics ; Thermodynamics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Rare-earth and actinide compounds ; Advanced Condensed Matter Physics
Authors: Lindsay Bassman Oftelie | Pankaj Rajak | Rajiv K. Kalia | Aiichiro Nakano | Fei Sha | Jifeng Sun | David J. Singh | Muratahan Aykol | Patrick Huck | Kristin A. Persson | Priya Vashishta
Source: npj Computational Materials (article, 2018-12-04)
Field: Materials Science — Materials Chemistry

Active learning for accelerated design of layered materials Abstract Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source. Python (programming language) ; Bayesian optimization ; Computer science ; Material properties ; Ab initio ; Band gap ; Electronic structure ; Computation ; Computational science ; Algorithm ; Materials science ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Krishna Feron | Rebecca Lim | Connor P. Sherwood | Angela Keynes | Alan M. Brichta | Paul C. Dastoor
Source: International Journal of Molecular Sciences (review, 2018-08-13)
Field: Materials Science — Polymers and Plastics

Organic Bioelectronics: Materials and Biocompatibility Organic electronic materials have been considered for a wide-range of technological applications. More recently these organic (semi)conductors (encompassing both conducting and semi-conducting organic electronic materials) have received increasing attention as materials for bioelectronic applications. Biological tissues typically comprise soft, elastic, carbon-based macromolecules and polymers, and communication in these biological systems is usually mediated via mixed electronic and ionic conduction. In contrast to hard inorganic semiconductors, whose primary charge carriers are electrons and holes, organic (semi)conductors uniquely match the mechanical and conduction properties of biotic tissue. Here, we review the biocompatibility of organic electronic materials and their implementation in bioelectronic applications. Bioelectronics ; Biocompatibility ; Electronic materials ; Nanotechnology ; Materials science ; Conductive polymer ; Organic semiconductor ; Charge carrier ; Electrical conductor ; Polymer ; Biosensor ; Optoelectronics ; Composite material Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Andrea Flores | Eric Cots | Julien Bergès | Kilian Muñiz
Source: Advanced Synthesis & Catalysis (article, 2018-05-23)
Field: Chemistry — Organic Chemistry

Enantioselective Iodine(I/III) Catalysis in Organic Synthesis Abstract Chiral aryliodine(III) reagents have provided an advanced concept for enantioselective synthesis and catalysis. With the advent of chiral iodine(I/III) catalysis, many different structures have been explored in the area. The currently most prominent catalyst design is based on a resorcinol core and the attachment of two lactic side chains bearing ester or amide groups. This enables a privileged modular catalyst synthesis, in which fine‐tuning with respect to the specific reaction requirement is straightforward. The present overview summarizes the structural variation and optimization of such chiral aryliodine catalysts and discusses structural properties of the active iodine(III) catalyst states. The status quo of enantioselective iodine(I/III) oxidation catalysis with respect to intramolecular and intermolecular reaction control is reviewed, and specific aspects of the individual catalytic cycles are discussed. magnified image Enantioselective synthesis ; Chemistry ; Catalysis ; Combinatorial chemistry ; Intramolecular force ; Organic chemistry ; Resorcinol ; Organic synthesis Oxidative Organic Chemistry Reactions ; Alkaloids: synthesis and pharmacology ; Asymmetric Synthesis and Catalysis
Authors: Aditya Nandy | Chenru Duan | Jon Paul Janet | Stefan Gugler | Heather J. Kulik
Source: Industrial & Engineering Chemistry Research (article, 2018-09-24)
Field: Materials Science — Materials Chemistry

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. Here, we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO–LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO–LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20–30 features. Analysis of the essential descriptors for HOMO level and HOMO–LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveal the paramount importance of nonlocal, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO–LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require. HOMO/LUMO ; Test set ; Artificial intelligence ; Machine learning ; Computer science ; Chemical space ; Artificial neural network ; Chemistry ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions