Computationally Connecting Organic Photovoltaic Performance to Atomistic Arrangements and Bulk Morphology

Rationally designing roll-to-roll printed organic photovoltaics requires a fundamental understanding of active layer morphologies optimized for charge separation and transport, and which ingredients can be used to self-assemble those morphologies. In this review article we discuss advances in three areas of computational modeling that provide insight into active layer morphology and the charge transport properties that result. We explain the computational bottlenecks prohibiting atomistically-detailed simulations of device-scale active layers and the coarse-graining and hardware acceleration strategies for overcoming them. We review coarse-grained simulations of organic photovoltaic active layers and show that high throughput simulations of experimentally-relevant length scales are now accessible. We describe a new Python package diffractometer that permits grazing-incidence X-ray scattering patterns of simulated active layers to be compared against experiments. We explain the accurate calculation of charge-carrier mobilities from coarse-grained active layer morphologies by using atomistic backmapping, quantum chemical calculations, and kinetic Monte Carlo simulations. We employ these simulations to show that ordering of poly(3-hexylthiophene-2,5-diyl) explains a factor of 1000 improvement in charge mobility. In concert, we present a suite of computational tools enabling large-scale electronic properties of organic photovoltaics to be studied and screened for by molecular simulations

Using Graphs to Quantify Energetic and Structural Order in Semicrystalline Oligothiophene Thin Films

In semicrystalline conjugated polymer thin films, the mobility of charges depends on the arrangement of the individual polymer chains. In particular, the ordering of the polymer backbones affects the charge transport within the film, as electron transfer generally occurs along the backbones with alternating single and double bonds. In this paper, we demonstrate that polymer ordering should be discussed not only in terms of structural but also energetic ordering of polymer chains. We couple data from molecular dynamics simulations and quantum chemical calculations to quantify both structural and energetic ordering of polymer chains. We leverage a graph-based representation of the polymer chains to quantify the transport pathways in a computationally efficient way. Next, we formulate the morphological descriptors that correlate well with hole mobility determined using kinetic Monte Carlo simulations. We show that the shortest and fastest path calculations are predictive of mobility in equilibrated morphologies. In this sense, we leverage graph-based descriptors to provide a basis for the quantitative structure property relationships

Molecular Interactions of Polydimethylsiloxane and Ni-Mn-Ga

Tiny pumps that can deliver microliters of fluid against high back-pressures can be made from Ni-Mn-Ga alloys. The fluid is transported in a movable pocket made between the alloy and a surrounding seal material, enabled by the magnetic shape memory properties of the alloy. The interaction between the sealant and the alloy is therefore critical, in particular how hey adhere and delaminate during pumping. This simulation study aims to quantify the interactions between the sealant poly-dimethylsiloxane (PDMS) and the Ni-Mn-Ga alloy. To study these PDMS-alloy interfaces, molecular dynamics simulations are performed using HOOMD-Blue on graphics processing units. We develop atomistic models for the interface components and investigate how the adhesion of short PDMS chains on Ni-Mn-Ga depend on model parameters, and temperature. We measure simulation speed as a function of system size on different processors to determine limits to computational feasibility and we identify conditions that facilitate adhesion. These simulations provide a first step towards more detailed studies of such organic/alloy interfaces and may help to identify more optimal sealant chemistries in the future

Detection of a glitch in the pulsar J1709-4429

We report the detection of a glitch event in the pulsar J1709$-$4429 (also known as B1706$-$44) during regular monitoring observations with the Molonglo Observatory Synthesis Telescope (UTMOST). The glitch was found during timing operations, in which we regularly observe over 400 pulsars with up to daily cadence, while commensally searching for Rotating Radio Transients, pulsars, and FRBs. With a fractional size of $\Delta\nu/\nu \approx 52.4 \times10^{-9}$, the glitch reported here is by far the smallest known for this pulsar, attesting to the efficacy of glitch searches with high cadence using UTMOST.Comment: 3 pages, 1 figur

Perspective on Coarse-Graining, Cognitive Load, and Materials Simulation

The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory. For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice

Multitrait genetic association analysis identifies 50 new risk loci for gastro-oesophageal reflux, seven new loci for Barrett’s oesophagus and provides insights into clinical heterogeneity in reflux diagnosis

Objective: Gastro-oesophageal reflux disease (GERD) has heterogeneous aetiology primarily attributable to its symptom-based definitions. GERD genome-wide association studies (GWASs) have shown strong genetic overlaps with established risk factors such as obesity and depression. We hypothesised that the shared genetic architecture between GERD and these risk factors can be leveraged to (1) identify new GERD and Barrett's oesophagus (BE) risk loci and (2) explore potentially heterogeneous pathways leading to GERD and oesophageal complications. Design: We applied multitrait GWAS models combining GERD (78 707 cases; 288 734 controls) and genetically correlated traits including education attainment, depression and body mass index. We also used multitrait analysis to identify BE risk loci. Top hits were replicated in 23andMe (462 753 GERD cases, 24 099 BE cases, 1 484 025 controls). We additionally dissected the GERD loci into obesity-driven and depression-driven subgroups. These subgroups were investigated to determine how they relate to tissue-specific gene expression and to risk of serious oesophageal disease (BE and/or oesophageal adenocarcinoma, EA). Results: We identified 88 loci associated with GERD, with 59 replicating in 23andMe after multiple testing corrections. Our BE analysis identified seven novel loci. Additionally we showed that only the obesity-driven GERD loci (but not the depression-driven loci) were associated with genes enriched in oesophageal tissues and successfully predicted BE/EA. Conclusion: Our multitrait model identified many novel risk loci for GERD and BE. We present strong evidence for a genetic underpinning of disease heterogeneity in GERD and show that GERD loci associated with depressive symptoms are not strong predictors of BE/EA relative to obesity-driven GERD loci

The UTMOST pulsar timing programme II:Timing noise across the pulsar population

While pulsars possess exceptional rotational stability, large scale timing studies have revealed at least two distinct types of irregularities in their rotation: red timing noise and glitches. Using modern Bayesian techniques, we investigated the timing noise properties of 300 bright southern-sky radio pulsars that have been observed over 1.0-4.8 years by the upgraded Molonglo Observatory Synthesis Telescope (MOST). We reanalysed the spin and spin-down changes associated with nine previously reported pulsar glitches, report the discovery of three new glitches and four unusual glitch-like events in the rotational evolution of PSR J1825$-$0935. We develop a refined Bayesian framework for determining how red noise strength scales with pulsar spin frequency ($\nu$) and spin-down frequency ($\dot{\nu}$), which we apply to a sample of 280 non-recycled pulsars. With this new method and a simple power-law scaling relation, we show that red noise strength scales across the non-recycled pulsar population as $\nu^{a} |\dot{\nu}|^{b}$, where $a = -0.84^{+0.47}_{-0.49}$ and $b = 0.97^{+0.16}_{-0.19}$. This method can be easily adapted to utilise more complex, astrophysically motivated red noise models. Lastly, we highlight our timing of the double neutron star PSR J0737$-$3039, and the rediscovery of a bright radio pulsar originally found during the first Molonglo pulsar surveys with an incorrectly catalogued position.Comment: Accepted by MNRAS. 28 pages, 8 figures, 8 table

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve

Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke

Genetic factors have been implicated in stroke risk but few replicated associations have been reported. We conducted a genome-wide association study (GWAS) in ischemic stroke and its subtypes in 3,548 cases and 5,972 controls, all of European ancestry. Replication of potential signals was performed in 5,859 cases and 6,281 controls. We replicated reported associations between variants close to PITX2 and ZFHX3 with cardioembolic stroke, and a 9p21 locus with large vessel stroke. We identified a novel association for a SNP within the histone deacetylase 9(HDAC9) gene on chromosome 7p21.1 which was associated with large vessel stroke including additional replication in a further 735 cases and 28583 controls (rs11984041, combined P = 1.87×10−11, OR=1.42 (95% CI) 1.28-1.57). All four loci exhibit evidence for heterogeneity of effect across the stroke subtypes, with some, and possibly all, affecting risk for only one subtype. This suggests differing genetic architectures for different stroke subtypes

A comprehensive re-assessment of the association between vitamin D and cancer susceptibility using Mendelian randomization.

Previous Mendelian randomization (MR) studies on 25-hydroxyvitamin D (25(OH)D) and cancer have typically adopted a handful of variants and found no relationship between 25(OH)D and cancer; however, issues of horizontal pleiotropy cannot be reliably addressed. Using a larger set of variants associated with 25(OH)D (74 SNPs, up from 6 previously), we perform a unified MR analysis to re-evaluate the relationship between 25(OH)D and ten cancers. Our findings are broadly consistent with previous MR studies indicating no relationship, apart from ovarian cancers (OR 0.89; 95% C.I: 0.82 to 0.96 per 1 SD change in 25(OH)D concentration) and basal cell carcinoma (OR 1.16; 95% C.I.: 1.04 to 1.28). However, after adjustment for pigmentation related variables in a multivariable MR framework, the BCC findings were attenuated. Here we report that lower 25(OH)D is unlikely to be a causal risk factor for most cancers, with our study providing more precise confidence intervals than previously possible