Archiving and disseminating integrative structure models.

Limitations in the applicability, accuracy, and precision of individual structure characterization methods can sometimes be overcome via an integrative modeling approach that relies on information from all available sources, including all available experimental data and prior models. The open-source Integrative Modeling Platform (IMP) is one piece of software that implements all computational aspects of integrative modeling. To maximize the impact of integrative structures, the coordinates should be made publicly available, as is already the case for structures based on X-ray crystallography, NMR spectroscopy, and electron microscopy. Moreover, the associated experimental data and modeling protocols should also be archived, such that the original results can easily be reproduced. Finally, it is essential that the integrative structures are validated as part of their publication and deposition. A number of research groups have already developed software to implement integrative modeling and have generated a number of structures, prompting the formation of an Integrative/Hybrid Methods Task Force. Following the recommendations of this task force, the existing PDBx/mmCIF data representation used for atomic PDB structures has been extended to address the requirements for archiving integrative structural models. This IHM-dictionary adds a flexible model representation, including coarse graining, models in multiple states and/or related by time or other order, and multiple input experimental information sources. A prototype archiving system called PDB-Dev ( https://pdb-dev.wwpdb.org ) has also been created to archive integrative structural models, together with a Python library to facilitate handling of integrative models in PDBx/mmCIF format

Multi-functional Optoelectronic Substrates

New bio-inspiration, micro-/nanomaterials, and micro-/nanomanufacturing processes offer unprecedented opportunities in engineering optoelectronic substrates for novel photon management strategies, difficult-to-realize material–property combinations, and new multi-functionality. In the past decade, discoveries in the multi-functional properties of micro-/nanostructured surfaces have led to a renaissance of activity in surface engineering, which have transformed substrates for a wide variety of rigid and flexible optoelectronic devices. The most important properties are related to photon management, such as high transparency, antireflection, and haze control. Transparency is the most important property as this determines the amount of light that either goes into or out of the active region of the device. In addition, haze control is an important property for various devices. Displays and touch screens require low optical haze, as high haze can contribute to the blurriness of text and images viewed. In contrast, applications such as solar cells and light emitting diodes (LEDs) would benefit from substrates with both high transparency and high haze. Substrates with high haze can increase how much light scatters into or out of the photoactive layers and may increase the solar cell power conversion efficiency and display or LED extraction efficiency, respectively. In addition to photon management properties, a wide variety of other properties are important that are related to the reliability of the optical properties under a variety of stressors. This includes wettability-related properties such as anti-soiling, self-cleaning, stain-resistance, fog resistance, where it is beneficial for the substrate to maintain its optical properties after exposure to various particulates or liquids. Durability under abrasion, hydrostatic pressure, and repeated bending are also important. Finally, properties such as optical switching may also be useful for various applications. In this study, we summarize our recent research progress in the micro-/nanostructuring of various optoelectronic substrate materials while discussing sources of bio-inspiration, advances in micro-/nanomanufacturing and machine learning strategies we used for fabrication of multi-functional optoelectronic substrates. These engineered surfaces have broad application to a wide variety of substrates for applications such as displays, solar cells, smartphones, light emitting diodes (LEDs), and e-paper, as well as new wearables, RF-ID tags, artificial skin, and medical/health sensors

Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data

Identifying parameters of computational models from experimental data, or model calibration, is fundamental for assessing and improving the predictability and reliability of computer simulations. In this work, we propose a method for Bayesian calibration of models that predict morphological patterns of diblock copolymer (Di-BCP) thin film self-assembly while accounting for various sources of uncertainties in pattern formation and data acquisition. This method extracts the azimuthally-averaged power spectrum (AAPS) of the top-down microscopy characterization of Di-BCP thin film patterns as summary statistics for Bayesian inference of model parameters via the pseudo-marginal method. We derive the analytical and approximate form of a conditional likelihood for the AAPS of image data. We demonstrate that AAPS-based image data reduction retains the mutual information, particularly on important length scales, between image data and model parameters while being relatively agnostic to the aleatoric uncertainties associated with the random long-range disorder of Di-BCP patterns. Additionally, we propose a phase-informed prior distribution for Bayesian model calibration. Furthermore, reducing image data to AAPS enables us to efficiently build surrogate models to accelerate the proposed Bayesian model calibration procedure. We present the formulation and training of two multi-layer perceptrons for approximating the parameter-to-spectrum map, which enables fast integrated likelihood evaluations. We validate the proposed Bayesian model calibration method through numerical examples, for which the neural network surrogate delivers a fivefold reduction of the number of model simulations performed for a single calibration task

Computational methods to engineer process-structure-property relationships in organic electronics: The case of organic photovoltaics

Ever since the Nobel prize winning work by Heeger and his colleagues, organic electronics enjoyed increasing attention from researchers all over the world. While there is a large potential for organic electronics in areas of transistors, solar cells, diodes, flexible displays, RFIDs, smart textiles, smart tattoos, artificial skin, bio-electronics, medical devices and many more, there have been very few applications that reached the market. Organic photovoltaics especially can utilize large market of untapped solar power -- portable and affordable solar conversion devices. While there are several reasons for their unavailability, a major one is the challenge of controlling device morphology at several scales, simultaneously. The morphology is intricately related to the processing of the device and strongly influences performance. Added to this is the unending development of new polymeric materials in search of high power conversion efficiencies. Fully understanding this intricate relationship between materials, processing conditions and power conversion is highly resource and time intensive. The goal of this work is to provide tightly coupled computational routes to these expensive experiments, and demonstrate process control using in-silico experiments. This goal is achieved in multiple stages and is commonly called the process-structure-property loop in material science community. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) towards this end. Two open-source software packages were developed: GRATE and PARyOpt. GRATE provides a means to reliably and repeatably quantify TEM images for identifying transport characteristics. It solves the problem of manually quantifying large number of large images with fine details. PARyOpt is a Gaussian process based optimization library that is especially useful for optimizing expensive phenomena. Both these are highly modular and designed to be easily integrated with existing software. It is anticipated that the organic electronics community will use these tools to accelerate discovery and development of new-age devices

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1