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    Computing the exact and approximate Pareto frontier on tree-structured networks with application to reducing the adverse impacts of hydropower expansion on ecosystem services in the Amazon Basin
    Gomes-Selman, Jonathan M.; Shi, Qinru; Perez, Guillaume (2021-12-13)
    Multi-objective optimization plays a key role in the study of real-world problems, as they often involve multiple criteria. In multi-objective optimization, it is important to identify the so-called Pareto frontier, which characterizes the trade-offs between the objectives of different solutions. We provide a C++ implementation of exact and approximate dynamic programming (DP) algorithms for computing the Pareto frontier on tree-structured networks. The code uses a specialized divide-and-conquer approach for the pruning of dominated solutions. This optimization outperforms the previous approaches, leading to speed-ups of two to three orders of magnitude in practice. We apply a rounding technique to the exact dynamic programming algorithm that provides a fully polynomial-time approximation scheme (FPTAS). The FPTAS finds a solution set of polynomial-size, which approximates the Pareto frontier within an arbitrary small e factor and runs in time that is polynomial in the size of the instance and 1/ e. We illustrate the code by evaluating trade-offs in ecosystem services due to the proliferation of hydropower dams throughout the Amazon basin. In particular, we apply our algorithms to identify portfolios of hydropower dam sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals, at different scales, including the entire Amazon basin. The code can be easily adapted to compute the Pareto frontier of various multi-objective problems for other river basins or other tree-structured networks. This work is described in the manuscript by Flecker et al., entitled “Reducing adverse impacts of Amazon hydropower expansion” in press, Science, 2021
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    Data from: Autonomous synthesis of metastable materials
    Ament, Sebastian; Amsler, Maximilian; Sutherland, Duncan R.; Chang, Ming-Chiang; Guevarra, Dan; Connolly, Aine B.; Gregoire, John M.; Thompson, Michael O.; Gomes, Carla P.; van Dover, R. Bruce (2021-08-13)
    Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery and development. The mapping of non-equilibrium synthesis phase diagrams has recently been accelerated via high throughput experimentation but still limits materials research because the parameter space is too vast to be exhaustively explored. We demonstrate accelerated synthesis and exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis and characterization along with a hierarchy of AI methods that efficiently reveal the structure of processing phase diagrams. SARA designs lateral gradient laser spike annealing (lg-LSA) experiments for parallel materials synthesis and employs optical spectroscopy to rapidly identify phase transitions. Efficient exploration of the multi-dimensional parameter space is achieved with nested active learning (AL) cycles built upon advanced machine learning models that incorporate the underlying physics of the experiments as well as end-to-end uncertainty quantification. With this, and the coordination of AL at multiple scales, SARA embodies AI harnessing complex scientific tasks. We demonstrate its performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for kinetically stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies such as solid oxide fuel cells. This data supports the research described above.