Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available

The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer models are becoming stochastic, yielding different outputs each time they are run, even if the same input values are used. Stochastic computer models are more difficult to analyse and more difficult to emulate - often requiring substantially more computer model runs to fit. We present a method of using deterministic approximations of the computer model to better construct an emulator. The method is applied to numerous toy examples, as well as an idealistic epidemiology model, and a model from the building performance field

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.Comment: NAACL 201

Process optimization for microstructure-dependent properties in thin film organic electronics

The processing conditions during solvent-based fabrication of thin film organic electronics significantly determine the ensuing microstructure. The microstructure, in turn, is one of the key determinants of device performance. In recent years, one of the foci in organic electronics has been to identify processing conditions for enhanced performance. This has traditionally involved either trial-and-error exploration, or a parametric sweep of a large space of processing conditions, both of which are time and resource intensive. This is especially the case when the process → structure and structure → property simulators are computationally expensive to evaluate. In this work, we integrate an adaptive-sampling based, gradient-free, Bayesian optimization routine with a phase-field morphology evolution framework that models solvent-based fabrication of thin film polymer blends (process → structure simulator) and a graph-based morphology characterization framework that evaluates the photovoltaic performance of a given morphology (structure → property simulator). The Bayesian optimization routine adaptively adjusts the processing parameters to rapidly identify optimal processing configurations, thus reducing the computational effort in process → structure → property explorations. This serves as a modular, parallel ‘wrapper’ framework that facilitates swapping-in other process simulators and device simulators for general process → structure → property optimization. We showcase this framework by identifying two processing parameters, the solvent evaporation rate and the substrate patterning wavelength, in a model system that results in a device with enhanced photovoltaic performance evaluated as the short-circuit current of the device. The methodology presented here provides a modular, scalable and extensible approach towards the rational design of tailored microstructures with enhanced functionalities

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