All,
Two new FOA’s for development of a DOE program in Cybersecurity for Clean Energy Manufacturing and Data Science for Knowledge Discovery for Chemical and Materials Research. This comes from Kristen Bennett.
"Two calls to consider:
"(1) Clean Energy Manufacturing Innovation Institute: Cybersecurity in Energy Efficient Manufacturing”.
https://www.energy.gov/articles/doe-announces-notice-intent-issue-funding-opportunity-establishing-cybersecurity-institute
"(2) FYI: First ‘Data Science’ call from BES for Chem/Geo/Bio. A companion FOA for Materials/Eng should also show up shortly. Just released. Pre-Apps Due March 8. Invites April 5. Full Apps May 15. — Please review the topics below but also the topics not applicable. This is part 1. Part 2 will be a companion FOA for MSE."
https://www.grants.gov/web/grants/view-opportunity.html?oppId=312711
The DOE SC program in Basic Energy Sciences (BES) announces its interest in receiving new applications in Data Science for Knowledge Discovery for Chemical and Materials Research with the aim of advancing the use of modern data science approaches (artificial intelligence, machine learning, graph theory, uncertainty quantification, etc.) to accelerate discovery in chemical and materials sciences. This funding opportunity is the first in this topical area sponsored by BES.
The program will support Single Investigator/Small Group efforts (up to $500,00 per year) for research with a focus on applying data science approaches and tools for experimental, theoretical/computational, or synergistic experimental/theoretical/computational research in areas supported by BES. Although the research may involve the development of new data science approaches, the focus of the effort should be on advancing understanding of fundamental properties and processes in chemical and materials systems.
Data science combines computer science, applied mathematics, and statistics with domain science to discover new knowledge from often complex (such as unstructured) data sets generated from experimental and/or computational studies.
As part of data science, machine learning and artificial intelligence methods are rapidly evolving and leading to more accurate predictions and trustworthy decisions and actions. Thus, these methods are being applied widely in society. While scientific research has benefited greatly in many areas such as the chemical and materials sciences as well as bioinformatics, medicine, drug discovery, systems control, astronomy and particle physics, many opportunities remain for data science to accelerate the rate of fundamental discovery for complex chemical processes and materials.
Recent public reports outline some of the emerging opportunities, such as the National Academies report titled Data Science: Opportunities to Transform Chemical Sciences and Engineering; some of the DOE-BES Basic Research Needs Workshops; the NSF-sponsored report on Framing the Role of Big Data and Modern Data Science in Chemistry; and the multi-agency sponsored report on the Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence. These reports and other publications conclude that data science is contributing to a new paradigm in scientific research methodology.
Synergistically complementing theory, experimentation, and simulation, data science has already demonstrated some success in accelerating molecular and materials discovery.
The next goal for this methodology is to generate new fundamental understanding of physical and chemical behavior, leading to successful predictions well outside the range of the original data, and possibly forcing modifications to the theoretical approaches.
One specific area of interest to BES-supported research is that of complex chemical and materials processes, defined as those whose macroscopic properties, reaction mechanisms and dynamic behavior cannot be predicted from a known combination of the properties of the individual components, hence are outside the scope of existing theoretical approaches.
Complex systems involve massive combinatorial spaces and nonlinear processes, which are being slowly tackled with large experimental, theoretical and computational efforts. Data science approaches, in combination with standard experimental and theoretical methods, are expected to accelerate the discovery of fundamentally new chemical mechanisms and material systems with exceptional properties and dynamic behavior, as well as new physical principles or laws. The specific energy-related topics of interest for this FOA, and those excluded, are mentioned below.
Address at least one of the following topics:
• Reaction chemistry across multiple scales in complex environments important in geosciences, catalysis, biochemistry or electrochemistry
• Synthesis science including nucleation, growth and restructuring of hybrid, hierarchical or other complex materials
• Far from equilibrium phenomena where dynamics is fast, such as in transport and separation in complex systems
• Behavior of properties and processes in extreme environments (e.g., radiation, corrosion, stress, pressure, temperature, electric and magnetic fields)
• Discovery of quantum materials and/or their collective, coherent, and strong correlation phenomena
This FOA will NOT consider proposals in certain topics or areas including:
• Quantum computing or quantum systems for quantum information science; energy storage; ‘omics’ data and systems biology approaches; applied research, such as design or optimization of instruments, devices, tools or processes; an exclusive focus on database development and management; studies demonstrating the feasibility or performance of methods as the main or only goal; developments contributing to proprietary products; and research that could be supported by other programs, such as SBIR/STTR.
• Areas covered by the BES Scientific User Facilities Division, such as the development of data science approaches for reduction or analysis of large volumes of data from scientific user facilities as the main goal.
• Streaming data analytics, data management, visualization, and software and computational technologies such as supported by the Office of Advanced Scientific Computing Research and mentioned in the FY 2019 Continuation of Solicitation from the Office of Science (pp. 5-6).
-- Mark Riley