MatterGen: a generative model for inorganic materials design

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating entirely novel materials given desired property constraints. Despite recent progress, current generative models have low success rate in proposing stable crystals, or can only satisfy a very limited set of property constraints. Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, we introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice. We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset. Compared to prior generative models, structures produced by MatterGen are more than twice as likely to be novel and stable, and more than 15 times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, novel materials with desired chemistry, symmetry, as well as mechanical, electronic and magnetic properties. Finally, we demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk. We believe that the quality of generated materials and the breadth of MatterGen's capabilities represent a major advancement towards creating a universal generative model for materials design.Comment: 13 pages main text, 35 pages supplementary informatio

Pushing the limits of RFID: Empowering RFID-based Electronic Article Surveillance with Data Analytics Techniques

False-positive classification is a central issue for RFID environments with limited process control, such as in-store settings. In the case of electronic article surveillance, false positives not only lead to incorrect inventory data but also trigger false alarms, which impair customer satisfaction. A typical counter measure is to reduce antenna power, which in turn leads to greatly diminished detection rates. In contrast, the present study investigates the applicability of data analytics to achieve high detection rates while retaining low false positives. In contrast to prior research, our test setting acknowledges the lack of process control in retail environments. We consider various walking paths and speeds as well as RFID tags concealed by shopping bags. To distinguish theft from non-theft events, we derive predictors, which are not just aggregations of the signal strength. Rather, individual reads are put into temporal relation to one another and are augmented with antenna information

Adversarial Training for Graph Neural Networks

Despite its success in the image domain, adversarial training does not (yet) stand out as an effective defense for Graph Neural Networks (GNNs) against graph structure perturbations. In the pursuit of fixing adversarial training (1) we show and overcome fundamental theoretical as well as practical limitations of the adopted graph learning setting in prior work; (2) we reveal that more flexible GNNs based on learnable graph diffusion are able to adjust to adversarial perturbations, while the learned message passing scheme is naturally interpretable; (3) we introduce the first attack for structure perturbations that, while targeting multiple nodes at once, is capable of handling global (graph-level) as well as local (node-level) constraints. Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations