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Molecular Crystal Polymorph Prediction: High Accuracy at Lower Computational Cost: University of California-Riverside

Gregory Beran

[email protected]

Professor Gregory Beran of the University of California-Riverside is supported by an award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division to develop new computational tools that will facilitate the prediction of three-dimensional crystal structures. Knowledge of molecular crystal structures is essential in pharmaceuticals and many other areas of chemistry. Different crystal packing arrangements, or ?polymorphs,? of the same molecule can exhibit vastly different properties. The occurrences of undesirable polymorphs have caused major drug recalls and other serious problems for patients and pharmaceutical manufacturers in the past. The pharmaceutical industry increasingly employs crystal structure prediction to complement their experimental drug formulation efforts and to reduce the potential for polymorphism-related problems. It has recently been discovered that the current theoretical models in widespread use exhibit significant problems for predicting the crystal structures of drug molecules. This project is developing new computational models that correct these weaknesses and improve the reliability with which crystal structures can be predicted. Software developed by this project will be released to the community as free, open-source<br/>software. Beyond the core research, Professor Beran is actively involved in pedagogical efforts to help train next-generation scientists, a large proportion of whom come from low-income, first-generation, and/or traditionally underrepresented minority demographics. <br/><br/>This research occurs in three parts. First, new computationally-practical electronic structure methods for modeling the non-covalent interactions that govern molecular conformation and crystal packing are being developed to enable identification of good initial crystal structures. Second, an approach that combines the strengths of the new electronic structure methods for describing intramolecular interactions with the lower computational costs of density functional theory for modeling intermolecular interactions is being developed to enable improved crystal structure predictions in pharmaceutical compounds. Finally, new lower-cost approximations for handling the vibrational contributions to crystalline stability are being investigated to allow investigation of how temperature affects polymorph stability. This project is developing new computational models that correct weaknesses in current theoretical models and improve the reliability with which crystal structures can be predicted. Software developed by this project will be released to the community as free, open-source software.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CCRI: ENS: Chipyard: University of California-Berkeley

Krste Asanovic

[email protected]

Advanced computing systems lie at the heart of many innovative products, from intelligent earbuds to autonomous vehicles, and these are increasingly built as customized systems-on-a-chip (SoCs). Customized SoCs incorporate a complex mix of general-purpose and customized processing logic, and both software and hardware must be highly tuned to achieve the needed performance and energy efficiency for the target application. Chipyard combines and extends existing community infrastructure components to provide a rich unified framework for research into SoC architecture and implementation, supporting activities ranging from research into new software and new architecture simulation techniques all the way down to test chip fabrication.<br/><br/>Chipyard is an integrated SoC design, simulation, and implementation environment to support research and development of specialized computing systems required to meet new application demands in the face of the slowdown in technology scaling. Chipyard is based around the widely used Rocket Chip SoC generator, which includes RISC-V processors, coherent caches, interconnect, and other IP blocks written in the Chisel HDL. Due to the widespread adoption of RISC-V in both academia and industry, there is extensive software support both in upstream open-source software projects as well as increasingly from commercial software providers. Chisel has a growing community, with a series of Chisel Community Conferences and multiple commercial tapeouts of Chisel-based designs. Chipyard IP modules can also be imported from legacy HDLs. RTL designs are converted into a common intermediate representation, FIRRTL, which supports powerful circuit transformations and experimentation with new hardware design tools. For fast, accurate simulation, Chipyard generates FireSim cloud-FPGA-accelerated simulators. FireSim can simulate entire datacenter racks at the RTL level with only a 100X slowdown. FireSim includes performance monitoring, analysis, and debugging tools to allow high observability of design behavior while running at high simulation speed. Chipyard also includes FireMarshal, a software workload management system that allows complete workloads to be easily packaged and recompiled to match an SoC configuration and execution environment. Chipyard integrates the Hammer modular physical design flow, which supports plugins for different tool chains and process technologies, and automates many tapeout steps. Chipyard will also integrate the Berkeley Analog Generator for mixed-signal and analog blocks. Overall, Chipyard provides an integrated environment where a single SoC description can be used to drive conventional open-source or commercial software RTL simulators, or pushed all the way to GDSII layout using industry-standard CAD tools and/or open-source ECAD tools once available. This proposal will fund further development of Chipyard capabilities, managed releases, and community engagement and outreach.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting: Virginia Polytechnic Institute and State University

Cayelan Carey

[email protected]

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software ? FLARE: Forecasting Lake And Reservoir Ecosystems ? that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.<br/><br/>The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project?s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.org<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting: University of Florida

Renato Figueiredo

[email protected]

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software ? FLARE: Forecasting Lake And Reservoir Ecosystems ? that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.<br/><br/>The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project?s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.org<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSFOpen Education ResourcesThe Research University (TRU)

CAREER: A Novel and Fast Open-Source Code for Global Simulation of Stratified Convection and Magnetohydrodynamics of the Sun: Clarkson University

Chunlei Liang

[email protected]

Non-technical: <br/>The goal of this project is to create a unique capability for predicting density-stratified magnetohydrodynamics of the Sun. This research is expected to lay a foundation for developing methods for predicting extreme space weather, e.g. the event of a "super solar flare" followed by an extreme geomagnetic storm. Scientific results of this research can help resolve several contradictory predictions from previous studies of the solar convection zone. The Principal Investigator (PI) will develop and disseminate a powerful open-source software package to the space weather and solar physics communities. The success of predicting severe space weather events has significant societal and economic impacts. PI will design high-order accurate computational algorithms suitable for exascale simulations that can perform a billion billion calculations per second. This software will run on massively parallel distributed-memory computers to predict coupled global and local dynamics of the sun. PI will reach out to K-12 students and demonstrate that science of the sun and high-performance computing are exciting and important to society. Furthermore, PI will leverage outreach efforts with the High Altitude Observatory of the National Center for Atmospheric Research and other research centers. This project, thus, serves the national interest as stated by NSF's mission: to promote the progress of science and to advance the national welfare.<br/><br/>Technical: <br/>The goal of this research program is to develop a novel, fully compressible model and an open-source community code for global simulations of the solar convection zone that includes the top near surface shear layer of the Sun. Current leading global simulations use an elastic approximation whose computational domains extend from the base of the solar convection zone and must stop at about 0.96 solar radius, stopping short of the top near surface shear layer where Mach number could reach unity. This research program will create a powerful open-source community code CHORUS++ to simulate magnetohydrodynamics of the solar convection zone. CHORUS stands for Compressible High-ORder Unstructured-grid Spectral difference code which has been co-developed by the PI for hydrodynamics of the solar convection zone. CHORUS++ will be equipped with variable mesh resolution capability to focus on targeted regions of interests. A fast local time-stepping algorithm will be designed and equipped for CHORUS++ for long-period time integration on massively parallel computers. These technical accomplishments can accelerate the original CHORUS code by a factor over 100. The PI will conduct a series of global simulations of magnetohydrodynamics of the solar convection zone with unprecedented resolutions for predicting the differential rotation, meridional circulation, giant cells, and super-granulation of the sun.

NSFOpen Education ResourcesThe Research University (TRU)

Empirical Macrofinance: Open-source Textbook and Data-sharing Platform: Princeton University

Atif Mian

[email protected]

Abstract<br/><br/>The importance of the linkages between finance, debt, and macroeconomic outcomes, such as growing inequality, become evident since the Great Financial Crisis. To date, no single initiative has organized this recent empirical literature into a meaningful whole. Similarly, to date, data sources used to address macro-finance research questions are not integrated. This project fills these gaps by disseminating the new techniques and datasets used in macro-financial research and by creating an open-source textbook that grounds each topic to its theoretical foundations. <br/><br/>The project will develop an open-source textbook and data-sharing platform to promote teaching and research about empirical finance and macroeconomics (macro-finance), filling the gap for an integrated approach in this important area. Data and instructional resources will be freely available to students and researchers via the project website. The codebase will be accessible on a platform for open-source software development. In addition to standardizing currently available data on macro-financial variables, the project will digitize new data on (i) US County Business Patterns, (ii) Global Sectoral Credit and National Accounts, (iii) International Loan Contracts.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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