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Anomaly detection is a significant and hence wellstudied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. As Generative Adversarial Networks (GANs) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task. ALAD then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. ALAD builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize GAN training, which results in significantly improved anomaly detection performance. ALAD achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published GAN-based method.

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Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment. The development of novel materials has long been stymied by a mismatch of time constants (Figure 1). Materials development typically occurs over a 15-25-year time horizon, sometimes requiring synthesis and characterization of millions of samples. However, corporate and government funders desire tangible results within the residency time of their leadership, typically 2-5 years. The residency time for postdocs and students in a research laboratory is usually 2-5 years; when a project outlasts the residency of a single individual, seamless continuity of motivation and intellectual property is often the exception, not the rule. Market drivers of novel materials development, informed by business competition and environmental considerations, often demand solutions within a shorter time horizon. This mismatch in time constants results in a historically poor return-on-investment of energy-materials (cleantech) research relative to comparable investments in medical or software development. 1