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Please see the full solicitation for complete information about the funding opportunity. Below is a summary assembled by the Research & Innovation Office (RIO).

Program Summary

SciDAC will support interdisciplinary teams to establish partnerships between domain scientists in the fields of materials science, condensed matter physics, chemical sciences, geosciences, energy-related biosciences, applied mathematics, and computer science. Scientists will work synergistically to overcome barriers between these disciplines and advance discovery science through advanced computation. The integrated teams will engage with the SciDAC Institutes and allow full use of DOE’s classical HPC capabilities. In addition, applications must focus on one of the following three topics:

• Topic A - Complex dynamical systems for energy-relevant chemical and/or physical systems and materials: Applications must focus on revolutionary theoretical and computational model development going beyond the use of existing methods in their traditional regime. They must address the dynamics of energy-relevant chemical and/or physical systems and materials with many interacting particles or many-body interactions leading to emergent behavior. Applications addressing multiscale modeling spanning different time and length scales are encouraged. The model development must target at least one of the following subtopics: (i) systems chemistry approaches to control chemical transformations and energy transductions, (ii) coupled electron/ion/spin transport in functional materials and chemical systems, or (iii) nonlinear phenomena in quantum materials.
• Topic BÌý- Reliable and explainable Artificial Intelligence for chemical and/or physical mechanism extraction from phenomena: Applications must focus on the translation of Artificial Intelligence (AI) model insights into interpretable scientific hypotheses and testable models to extract chemical and/or physical mechanisms from observable phenomena^[B1-B3]. AI models should address a combination of phenomena in systems/materials of interest. Examples of phenomena are topology, chirality, nonlinearity, quantum interactions, quantum correlations, selectivity, self-assembly and self-repair, phase transitions, magnetism, superconductivity, and thermo- or ferroelectricity. To bridge the gap between data-driven AI models and domain-driven scientific models, the developed AI model must address explainability, interpretability, and reliability, and must demonstrate a path to automated mechanism discovery. Applications addressing fundamental science underpinning one of the following initiatives are especially encouraged: Microelectronics, Critical Materials, Sustainable Chemistry, and Transformative Manufacturing.
• Topic C - Foundation Models (FMs) for chemical and materials sciences: Applications must focus on large, trustworthy FMs^Ìýpretrained with multimodal data in self-supervised manners adaptable to transfer learning. They must address data availability, quality, and sparsity. Applications focusing on data generation are excluded and may be declined without review. The developed FMs must demonstrate their scalability and adaptability to different scientific challenges in chemical/physical systems/materials discovery and design; reaction or transformation pathways; reactive separation systems with high selectivity, capacity, and throughput; multiscale aspects of the structure and dynamics of fracture or dislocation systems; or predictive synthesis for energy relevant technologies.

Efforts aimed at revolutionary advances in models, mathematics, algorithms, data, and computing that can extend currently attainable length/time scales or increase complexity, and that algorithmically match efficiency enhancements offered by next-generation classical computers, will receive priority.

Deadlines

• CU Internal Deadline: 11:59pm MST February 3, 2025
• Sponsor Pre-Application Deadline: 3:00pm MST February 21, 2025
• Sponsor Application Deadline: 9:59pm MST April 25, 2025

Internal Application Requirements (all in PDF format)

• Research Narrative (3 pages maximum): Please include the following: 1) Background/Introduction: Explanation of the importance and relevance of the proposed work as well as a review of the relevant literature. 2) Project Objectives: This section should provide a clear, concise statement of the specific objectives/aims of the proposed project. 3) Proposed Research and Methods: Identify the hypotheses to be tested (if any) and details of the methods to be used including the integration of theoretical, computational, and applied mathematics research efforts. 4) Management Plan and Timetable of Activities: Describe the management structure, how effective collaborations among the participants will be fostered, how integration of computational and science efforts will be attained, and the timeline for all major activities including performance metrics and deliverables. It must clearly indicate the roles and responsibilities of the senior/key members and indicate how activities will be coordinated and communicated among team members.
• PI Curriculum Vitae
• Budget Overview (1 page maximum): A basic budget outlining project costs is sufficient; detailed OCG budgets are not required.

To access the online application, visit:

Eligibility

The PI on a pre-application or application may also be listed as a senior or key personnel on an unlimited number of separate submissions. Individual senior investigators are strongly encouraged not to participate on more than 2 submissions.

Limited Submission Guidelines

Applicant institutions are limited to no more than 2 pre-applications and applications as the lead institution.

Award Information

Award Amount: $1M - $2.5M per year

Award Duration: 4 years

Review Criteria

The internal reviewers will use following criteria to guide evaluations:Ìý

1. Scientific and/or Technical Merit of the Project
   • What is the scientific innovation of the proposed research?
   • What is the likelihood of achieving valuable results?
   • How might the results of the proposed work impact the direction, progress, and thinking in relevant scientific fields of research?
   • How does the proposed work compare with other efforts in its field, both in terms of scientific and/or technical merit and originality?
   • Does the application specify at least one scientific hypothesis or challenging scientificÌý question motivating the proposed work? Is the investigation of the specified hypothesis/hypotheses or challenging scientific question(s) scientifically valuable?
   • How will the research accelerate scientific discovery through computation on DOE HPC systems?
   • Is the Data Management Plan suitable for the proposed research? To what extent does it support the validation of research results? To what extent will research products, including data, be made available and reusable to advance the field of research?
   • To what extent does the Data Management Plan address the specific requirements for FAIR data principles?
2. Appropriateness of the Proposed Method or Approach
   • How logical and feasible are the research approaches?
   • Does the proposed research employ innovative concepts or methods?
   • How does the approach proposed concretely contribute to our understanding of the validity of the specified scientific hypothesis/hypotheses or challenging scientific question(s)?
   • What are the strengths and weaknesses of the conceptual framework, methods, and analyses?
   • How well are these justified, adequately developed, and likely to lead to scientifically valid conclusions?
   • How does the applicant recognize significant potential problems and consider alternative strategies?
   • How will the proposed research exploit existing resources or contribute new resources (e.g., algorithms, software) and avoid duplication of existing resources?
   • How does the proposed research plan recognize and attempt to address the mathematical, algorithmic, software, or architectural challenges arising in the relevant computations on DOE HPC systems?
3. Competency of Applicant’s Personnel and Adequacy of Proposed Resources
   • What is the expertise and qualification of the research team to carry out the proposed research?
   • Are the research environment and facilities adequate for performing the research?
   • Does the proposed work take advantage of unique DOE HPC facilities and capabilities?
4. Reasonableness and Appropriateness of the Proposed Budget
   • Are the proposed budget and staffing levels adequate to carry out the proposed research?
   • Is the budget reasonable and appropriate for the scope and all aspects of the collaboration?
   • Does the budget (or allocation of time) provide for adequate commitment by senior contributors? Do any components of the project rely upon efforts by unpaid contributors?
5. Quality and Efficacy of the Promoting Inclusive and Equitable Research Plan
   • How well integrated is the Promoting Inclusive and Equitable Research (PIER) Plan with the proposed project?
   • What aspects of the PIER plan are likely to contribute to the goal of creating and maintaining an equitable, inclusive, encouraging, and professional training and research environment and supporting a sense of belonging among project personnel?
   • How does the proposed plan include intentional mentorship of project personnel?
   • How are the proposed resources and budget for the PIER Plan reasonable and appropriate?
   • To what extent is the PIER plan likely to lead to participation of individuals from diverse backgrounds, including individuals historically underrepresented in the research community?
6. Synergy Among the PIs, including Cohesion and Integration of the Computational and Science Activities
   • Comment on how the applicant’s approach addresses domain science, computer science, and applied mathematics in an integrated and coherent manner.
   • Evaluate the likelihood that the proposed team will work synergistically together as a team. Are the applied mathematicians and computer scientists conducting significant research and are they well integrated?
   • Will the proposed collaboration result in advances that would not have been accomplished by those same researchers working separately? Have the applicants defined scientific problems that are likely to be addressed only through close collaboration among the researchers on the application?
7. Strength of the Management Plan
   • Assess the management plan, including the description of the roles/responsibilities of the PIs, thrust leaders, plans for communication and integration of the team, and experience of the Lead PI in managing similar sized teams.
   • What are the strengths and weaknesses of the proposed timeline?
   • Comment on the flexibility of the management structure to adapt quickly to changing technical challenges and scientific needs, and how success and failure will be evaluated.
   • How does the project demonstrate a functional collaboration between domain scientists and applied mathematicians or computer scientists?