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

Interpretable deep learning for guided structure-property explorations in photovoltaics

The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems

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

Plasma chemokines as immune biomarkers for diagnosis of pediatric tuberculosis

Abstract Background Diagnosing tuberculosis (TB) in children is challenging due to paucibacillary disease, and lack of ability for microbiologic confirmation. Hence, we measured the plasma chemokines as biomarkers for diagnosis of pediatric tuberculosis. Methods We conducted a prospective case control study using children with confirmed, unconfirmed and unlikely TB. Multiplex assay was performed to examine the plasma CC and CXC levels of chemokines. Results Baseline levels of CCL1, CCL3, CXCL1, CXCL2 and CXCL10 were significantly higher in active TB (confirmed TB and unconfirmed TB) in comparison to unlikely TB children. Receiver operating characteristics curve analysis revealed that CCL1, CXCL1 and CXCL10 could act as biomarkers distinguishing confirmed or unconfirmed TB from unlikely TB with the sensitivity and specificity of more than 80%. In addition, combiROC exhibited more than 90% sensitivity and specificity in distinguishing confirmed and unconfirmed TB from unlikely TB. Finally, classification and regression tree models also offered more than 90% sensitivity and specificity for CCL1 with a cutoff value of 28 pg/ml, which clearly classify active TB from unlikely TB. The levels of CCL1, CXCL1, CXCL2 and CXCL10 exhibited a significant reduction following anti-TB treatment. Conclusion Thus, a baseline chemokine signature of CCL1/CXCL1/CXCL10 could serve as an accurate biomarker for the diagnosis of pediatric tuberculosis

Coordination logic of the sensing machinery in the transcriptional regulatory network of Escherichia coli

The active and inactive state of transcription factors in growing cells is usually directed by allosteric physicochemical signals or metabolites, which are in turn either produced in the cell or obtained from the environment by the activity of the products of effector genes. To understand the regulatory dynamics and to improve our knowledge about how transcription factors (TFs) respond to endogenous and exogenous signals in the bacterial model, Escherichia coli, we previously proposed to classify TFs into external, internal and hybrid sensing classes depending on the source of their allosteric or equivalent metabolite. Here we analyze how a cell uses its topological structures in the context of sensing machinery and show that, while feed forward loops (FFLs) tightly integrate internal and external sensing TFs connecting TFs from different layers of the hierarchical transcriptional regulatory network (TRN), bifan motifs frequently connect TFs belonging to the same sensing class and could act as a bridge between TFs originating from the same level in the hierarchy. We observe that modules identified in the regulatory network of E. coli are heterogeneous in sensing context with a clear combination of internal and external sensing categories depending on the physiological role played by the module. We also note that propensity of two-component response regulators increases at promoters, as the number of TFs regulating a target operon increases. Finally we show that evolutionary families of TFs do not show a tendency to preserve their sensing abilities. Our results provide a detailed panorama of the topological structures of E. coli TRN and the way TFs they compose off, sense their surroundings by coordinating responses

Shorter treatment for minimal tuberculosis (TB) in children (SHINE): a study protocol for a randomised controlled trial

Background: Tuberculosis (TB) in children is frequently paucibacillary and non-severe forms of pulmonary TB are common. Evidence for tuberculosis treatment in children is largely extrapolated from adult studies. Trials in adults with smear-negative tuberculosis suggest that treatment can be effectively shortened from 6 to 4 months. New paediatric, fixed-dose combination anti-tuberculosis treatments have recently been introduced in many countries, making the implementation of World Health Organisation (WHO)-revised dosing recommendations feasible. The safety and efficacy of these higher drug doses has not been systematically assessed in large studies in children, and the pharmacokinetics across children representing the range of weights and ages should be confirmed. Methods/design: SHINE is a multicentre, open-label, parallel-group, non-inferiority, randomised controlled, two-arm trial comparing a 4-month vs the standard 6-month regimen using revised WHO paediatric anti-tuberculosis drug doses. We aim to recruit 1200 African and Indian children aged below 16 years with non-severe TB, with or without HIV infection. The primary efficacy and safety endpoints are TB disease-free survival 72 weeks post randomisation and grade 3 or 4 adverse events. Nested pharmacokinetic studies will evaluate anti-tuberculosis drug concentrations, providing model-based predictions for optimal dosing, and measure antiretroviral exposures in order to describe the drug-drug interactions in a subset of HIV-infected children. Socioeconomic analyses will evaluate the cost-effectiveness of the intervention and social science studies will further explore the acceptability and palatability of these new paediatric drug formulations. Discussion: Although recent trials of TB treatment-shortening in adults with sputum-positivity have not been successful, the question has never been addressed in children, who have mainly paucibacillary, non-severe smearnegative disease. SHINE should inform whether treatment-shortening of drug-susceptible TB in children, regardless of HIV status, is efficacious and safe. The trial will also fill existing gaps in knowledge on dosing and acceptability of new anti-tuberculosis formulations and commonly used HIV drugs in settings with a high burden of TB. A positive result from this trial could simplify and shorten treatment, improve adherence and be cost-saving for many children with TB. Recruitment to the SHINE trial begun in July 2016; results are expected in 2020. Trial registration: International Standard Randomised Controlled Trials Number: ISRCTN63579542, 14 October 2014. Pan African Clinical Trials Registry Number: PACTR201505001141379, 14 May 2015. Clinical Trial Registry-India, registration number: CTRI/2017/07/009119, 27 July 2017