Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Datasets of published reactions were curated for Suzuki, Negishi, and C-N couplings, as well as Pauson-Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector.Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each dataset, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph-attention operation in the top-performing model. File list (2)download file view on ChemRxiv 2020-10-13_ChemRxiv.pdf (2.25 MiB) download file view on ChemRxiv 2020-10-13_ChemRxiv_SI.pdf (3.28 MiB)

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Self-driving vehicles need to anticipate a diverse set of future traffic scenarios in order to safely share the road with other traffic participants that may exhibit rare but dangerous driving. In this paper, we present LOOKOUT, an approach to jointly perceive the environment and predict a diverse set of futures from sensor data, estimate their probability, and optimize a contingency plan over these diverse future realizations. In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that allows us to cover a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models. Unlike previous work in diverse motion forecasting, our diversity objective explicitly rewards sampling future scenarios that require distinct reactions from the self-driving vehicle for improved safety. Our contingency planner then finds comfortable trajectories that ensure safe reactions to a wide range of future scenarios. Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a largescale self-driving dataset as well as safer and more comfortable motion plans in long-term closed-loop simulations than current state-of-the-art models.

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