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Abstract The formation of biomolecular materials via dynamical interfacial processes, such as self-assembly and fusion, for diverse compositions and external conditions can be efficiently probed using ensemble Molecular Dynamics (MD). However, this approach requires many simulations when investigating a large composition phase space. In addition, there is difficulty in predicting whether each simulation will yield biomolecular materials with the desired properties or outcomes and how long each simulation will run. These difficulties can be overcome by rules-based management systems, including intermittent inspection, variable sampling, and premature termination or extension of the individual MD simulations. Automating such a management system can significantly improve runtime efficiency and reduce the burden of organizing large ensembles of MD simulations. To this end, a computational framework, the Pipelines for Automating Compliance-based Elimination and Extension (PACE²), is proposed for high-throughput ensemble biomolecular materials simulations. The PACE²framework encompasses Candidate pipelines, where each pipeline includes temporally separated simulation and analysis tasks. When a MD simulation is completed, an analysis task is triggered, which evaluates the MD trajectory for compliance. Compliant simulations are extended to the next MD phase with a suitable sample rate to allow additional, detailed analysis. Non-compliant simulations are eliminated, and their computational resources are reallocated or released. The framework is designed to run on local desktop computers and high-performance computing resources. Preliminary scientific results enabled by the use of PACE²framework are presented, which demonstrate its potential and validates its function. In the future, the framework will be extended to address generalized workflows and investigate composition-structure-property relations for other classes of materials. 

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections. 

The formation of biomolecular materials via dynamical interfacial processes such as self-assembly and fusion, for diverse compositions and external conditions, can be efficiently probed using ensemble Molecular Dynamics. However, this approach requires a large number of simulations when investigating a large composition phase space. In addition, there is difficulty in predicting whether each simulation is yielding biomolecular materials with the desired properties or outcomes and how long each simulation will run for. These difficulties can be overcome by rules-based management systems which include intermittent inspection, variable sampling, premature termination and extension of the individual Molecular Dynamics simulations. The automation of such a management system can significantly reduce the overhead of managing large ensembles of Molecular Dynamics simulations. To this end, a high-throughput workflows-based computational framework, Pipeline for Automating Compliance-based Elimination and Extension (PACE2), for biomolecular materials simulations is proposed. The PACE2 framework encompasses Simulation-Analysis Pipelines. Each Pipeline includes temporally separated simulation and analysis tasks. When a Molecular Dynamics simulation completes, an analysis task is triggered which evaluates the Molecular Dynamics trajectory for compliance. Compliant Molecular Dynamics simulations are extended to the next Molecular Dynamics phase with a suitable sample rate to allow additional, detailed analysis. Non-compliant Molecular Dynamics simulations are eliminated, and their computational resources are either reallocated or released. The framework is designed to run on local desktop computers and high performance computing resources. In the future, the framework will be extended to address generalized workflows and investigate other classes of materials.