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We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inception Distance (FID). FID assumes that images featurized using the penultimate layer of Inception-v3 follow a Gaussian distribution, an assumption which cannot be violated if we wish to use FID as a metric. However, we show that Inception-v3 features of the ImageNet dataset are not Gaussian; in particular, every single marginal is not Gaussian. To remedy this problem, we model the featurized images using Gaussian mixture models (GMMs) and compute the 2-Wasserstein distance restricted to GMMs. We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs. We experimentally show the advantages of WaM over FID, including how FID is more sensitive than WaM to imperceptible image perturbations. By modelling the non-Gaussian features obtained from Inception-v3 as GMMs and using a GMM metric, we can more accurately evaluate generative network performance. 

The objective of this work is to predict the morphology and material properties of crosslinking polymers used in aerospace applications. We extend the open-source dybond plugin for HOOMD-Blue to implement a new coarse-grained model of reacting epoxy thermosets and use the 44DDS/DGEBA/PES system as a case study for calibration and validation. We parameterize the coarse-grained model from atomistic solubility data, calibrate reaction dynamics against experiments, and check for size-dependent artifacts. We validate model predictions by comparing glass transition temperatures measurements at arbitrary degree of cure, gel-points, and morphology predictions against experiments. We demonstrate for the first time in molecular simulations the cure-path dependence of toughened thermoset morphologies. 

The presence of the top electrode on hafnium oxide‐based thin films during processing has been shown to drive an increase in the amount of metastable ferroelectric orthorhombic phase and polarization performance. This “Clamping Effect,” also referred to as the Capping or Confinement Effect, is attributed to the mechanical stress and confinement from the top electrode layer. However, other contributions to orthorhombic phase stabilization have been experimentally reported, which may also be affected by the presence of a top electrode. In this study, it is shown that the presence of the top electrode during thermal processing results in larger tensile biaxial stress magnitudes and concomitant increases in ferroelectric phase fraction and polarization response, whereas film chemistry, microstructure, and crystallization temperature are not affected. Through etching experiments and measurement of stress evolution for each processing step, it is shown that the top electrode locally inhibits out‐of‐plane expansion in the HZO during crystallization, which prevents equilibrium monoclinic phase formation and stabilizes the orthorhombic phase. This study provides a mechanistic understanding of the clamping effect and orthorhombic phase formation in ferroelectric hafnium oxide‐based thin films, which informs the future design of these materials to maximize ferroelectric phase purity and corresponding polarization behavior.

 

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery. 

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments. 

Modulation of autophagy, specifically its inhibition, stands to transform the capacity to effectively treat a broad range of cancers. However, the clinical efficacy of autophagy inhibitors has been inconsistent. To delineate clinical and epidemiological features associated with autophagy inhibition and a positive oncological clinical response, a retrospective analysis of patients is conducted treated with hydroxychloroquine, a known autophagy inhibitor. A direct correlation between smoking status and inhibition of autophagy with hydroxychloroquine is identified. Recognizing that smoking is associated with elevated circulating levels of carbon monoxide (CO), it is hypothesized that supplemental CO can amplify autophagy inhibition. A novel, gas‐entrapping material containing CO in a pre‐clinical model is applied and demonstrated that CO can dramatically increase the cytotoxicity of autophagy inhibitors and significantly inhibit the growth of tumors when used in combination. These data support the notion that safe, therapeutic levels of CO can markedly enhance the efficacy of autophagy inhibitors, opening a promising new frontier in the quest to improve cancer therapies.