One-dimensional conductive polymers are attractive materials because of their potential in flexible and transparent electronics. Despite years of research, on the macro- and nano-scale, structural disorder represents the major hurdle in achieving high conductivities. Here we report measurements of highly ordered metal-organic nanoribbons, whose intrinsic (defect-free) conductivity is found to be 104 S m−1, three orders of magnitude higher than that of our macroscopic crystals. This magnitude is preserved for distances as large as 300 nm. Above this length, the presence of structural defects (~ 0.5%) gives rise to an inter-fibre-mediated charge transport similar to that of macroscopic crystals. We provide the first direct experimental evidence of the gapless electronic structure predicted for these compounds. Our results postulate metal-organic molecular wires as good metallic interconnectors in nanodevices.
We describe a method, that we call data projection onto parameter space (DPPS), to optimize an energy functional of the electron density, so that it reproduces a dataset of experimental magnitudes. Our scheme, based on Bayes theorem, constrains the optimized functional not to depart unphysically from existing ab initio functionals. The resulting functional maximizes the probability of being the "correct" parametrization of a given functional form, in the sense of Bayes theory. The application of DPPS to water sheds new light on why density functional theory has performed rather poorly for liquid water, on what improvements are needed, and on the intrinsic limitations of the generalized gradient approximation to electron exchange and correlation. Finally, we present tests of our water-optimized functional, that we call vdW-DF-w, showing that it performs very well for a variety of condensed water systems.
It is now established that nuclear quantum motion plays an important role in determining water's hydrogen bonding, structure, and dynamics. Such effects are important to include in density functional theory (DFT) based molecular dynamics simulation of water. The standard way of treating nuclear quantum effects, path integral molecular dynamics (PIMD), multiplies the number of energy/force calculations by the number of beads required. In this work we introduce a method whereby PIMD can be incorporated into a DFT simulation with little extra cost and little loss in accuracy. The method is based on the many body expansion of the energy and has the benefit of including a monomer level correction to the DFT energy. Our method calculates intramolecular forces using the highly accurate monomer potential energy surface developed by Partridge-Schwenke, which is cheap to evaluate. Intermolecular forces and energies are calculated with DFT only once per timestep using the centroid positions. We show how our method may be used in conjunction with a multiple time step algorithm for an additional speedup and how it relates to ring polymer contraction and other schemes that have been introduced recently to speed up PIMD simulations. We show that our method, which we call "monomer PIMD", correctly captures changes in the structure of water found in a full PIMD simulation but at much lower computational cost.There is great interest in being able to accurately simulate liquid water at the quantum mechanical level. [1][2][3][4] The most widely used methodology for this is density functional theory. However, many density functionals fail to accurately reproduce all of the key properties of water such as its density, compressibility, and diffusion constant. Moreover, different density functionals fail in different ways. For instance, PBE creates a overstructured liquid, while many van der Waals (vdW) functionals create an understructured liquid. 5,6 There are nonetheless new meta-GGA functionals such as SCAN 7 or empirically optimized hybrid functionals such as B97M-rV 8 which are producing promising results for liquid water.Most ab initio techniques are based on the Born-Oppenheimer approximation and the assumption that nuclear dynamics can be treated classically. However, over the past two decades a wide range of studies have demonstrated that this is not a good assumption for water because the OH stretching mode of water is very quantum mechanical (zero point temperature T z = ω/2k b = 2600 K), and hydrogen nuclei are delocalized, leading to a large number of nonnegligible nuclear quantum effects (NQEs) -for a recent review, see Ceriotti, et al. 9 In the primary isotope effect, the OH distance is observed to be longer than the OD distance. In the secondary isotope effect, also called the Ubbelöhde effect, the H-bond donoracceptor (oxygen-oxygen) distance R changes upon isotopic substitution. The magnitude and direction of the change depends on the strength of the hydrogen bond, due to competing a) Electronic quantum effects. [10...
Background: Machine learning (ML) methods are becoming more feasible for use in clinical and epidemiologic research of breast cancer, particularly when characterizing histopathology. Compared to supervised ML methods, unsupervised approaches represent an opportunity to distinguish features heretofore unknown. The purpose of this study was to use unsupervised deep learning methods to identify histopathological features in diagnostic breast cancer hematoxylin and eosin (H&E) slides that are associated with clinical characteristics and patient outcomes. Methods: One H&E slide was scanned (Leica Biosystems Aperio Versa scanner) at 20x magnification for each of 1,716 women diagnosed with breast cancer from the Cancer Prevention Study-II Nutrition Cohort. In the pre-processing phase, the scanned images underwent color normalization, artifact detection, and tiling. We then used an un-pretrained VGG16 autoencoder with data augmentation for feature learning and extraction from tiles. These features were two-tiered clustered using the K-means algorithm. Each tile was assigned the cluster with the highest probability. The tiles were reassembled into whole slide images. For each slide, the proportion of tiles in each cluster was calculated. We will associate clusters with clinical features and 5- and 10-year breast cancer-specific survival using multivariable logistic and Cox proportional hazards regression models, respectively. Results: Mean age at baseline enrollment (1992-1993) and breast cancer diagnosis for the cases was 60.6 years (SD=6.0) and 71.5 years (SD=7.0), respectively. The majority of cancer diagnoses occurred after 1999 (79%) and 81% of women included were diagnosed invasive breast cancer. The final pipeline for the full set of images is currently being built. Preliminary runs at the 1x magnification level with 100 cases (N=21,472 tiles) have shown clustering based on macro-level features such as adipose, stromal and epithelial content. Second-tier clustering (clustering within clusters) shows further delineation of groups within clusters of interest (i.e. epithelial-cell rich regions). The final output with all 1,716 slides will be based on analysis at the 5x magnification level. Discussion: We expect that some histopathological features identified by ML models will be associated with conventional pathology features, clinical features, and breast cancer-specific survival. Utilization of ML methods for analyzing histology slides provides additional data that can be integrated into epidemiological studies. Future directions include analyzing images at higher magnifications (10x or 20x) and assessing the association between ML histopathological characteristics and breast cancer risk factors and incorporating these characteristics into prognostic models. Citation Format: Samantha Puvanesarajah, James M. Hodge, Jacob L. Evans, William Seo, Michelle Yi, Michelle M. Fritz, Mary Macheski-Preston, Ted Gansler, Susan M. Gapstur, Mia M. Gaudet. Unsupervised deep-learning to identify histopathological features among breast cancers in the Cancer Prevention Study-II Nutrition Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2417.
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