Exact Contract Story
COLLABORATIVE RESEARCH: DMREF: DATA-DRIVEN DISCOVERY OF THE PROCESSING GENOME FOR HETEROGENOUS SUPERALLOY MICROSTRUCTURES -THIS DESIGNING MATERIALS TO REVOLUTIONIZE AND ENGINEER OUR FUTURE (DMREF) PROJECT AIMS TO REVOLUTIONIZE THE CREATION OF NOVEL ENGINEERING ALLOYS BY FOCUSING ON THE RELATIONSHIP BETWEEN THE PROCESS OF PRODUCTION AND THE MATERIAL'S RESULTANT MICROSTRUCTURE - THE INTERNAL STRUCTURE INVISIBLE TO THE NAKED EYE. THIS RELATIONSHIP IS A CRUCIAL BUT LESS-EXPLORED SEGMENT OF THE MATERIAL SCIENCE LIFECYCLE, YET DIRECTLY CONNECTS PROCESSING TO PERFORMANCE. USING CUTTING-EDGE MACHINE LEARNING AND DATA SCIENCE, THE PROJECT TEAM WILL BUILD A PLATFORM, NAMED AS DRAGONS (DATA-DRIVEN RECURSIVE AI-POWERED GENERATOR OF OPTIMIZED NANOSTRUCTURED SUPERALLOYS), TO DISCOVER THESE CONNECTIONS, ENABLING MORE NUANCED CONTROL OVER MATERIAL PRODUCTION. DRAGONS WILL PRESCRIBE IDEAL PROCESSING CONDITIONS TO ACHIEVE A SPECIFIC MATERIAL MICROSTRUCTURE. THIS PROJECT CARRIES BROAD SIGNIFICANCE, WITH THE POTENTIAL TO DRIVE ADVANCEMENTS IN DIVERSE SECTORS THAT RELY ON NOVEL MATERIALS INCLUDING ELECTRONICS, HEALTHCARE, ENERGY, AND TRANSPORTATION. ADDITIONALLY, THE PROJECT'S EDUCATIONAL OUTREACH ACTIVITIES AIM TO INSPIRE THE NEXT GENERATION OF SCIENTISTS AND ENGINEERS BY PROVIDING A MORE DIVERSE, INCLUSIVE, AND SUSTAINABLE PATHWAY INTO THESE FIELDS. HENCE, THIS RESEARCH CARRIES POTENTIAL TO CATALYZE SCIENTIFIC ADVANCEMENT, FOSTER ECONOMIC GROWTH, AND ENHANCE EDUCATIONAL OUTCOMES. THIS DMREF PROJECT FOCUSES ON HARNESSING MACHINE LEARNING AND DATA SCIENCE TO ADVANCE UNDERSTANDING OF THE PROCESSING-MICROSTRUCTURE RELATIONSHIPS IN THE PRODUCTION OF NOVEL MATERIALS, A KEY, YET UNDEREXPLORED FACET OF THE MATERIALS GENOME INITIATIVE (MGI). THE RESEARCH TEAM WILL DEVELOP A DATA-DRIVEN PLATFORM, NAMED AS DATA-DRIVEN RECURSIVE AI-POWERED GENERATOR OF OPTIMIZED NANOSTRUCTURED SUPERALLOYS (DRAGONS), TO DEMYSTIFY THE COMPLEX RELATIONSHIPS INHERENT IN THE CREATION OF MULTI-PHASE, HETEROGENEOUS NANOSTRUCTURED MATERIALS (HNMS). DRAGONS WILL UTILIZE PREDICTIVE MODELS TO INTERPRET MICROSTRUCTURE ATTRIBUTES BASED ON GIVEN PROCESSING CONDITIONS AND, IN A RECIPROCAL MANNER, PROVIDE PROCESSING PARAMETERS REQUIRED TO GENERATE A PREDEFINED MICROSTRUCTURE. CAPITALIZING ON EXPERTISE IN MAGNETRON SPUTTERING AND HEAT TREATMENT (MS+HT), THE RESEARCH TEAM AIMS TO ENGINEER INTRICATE HETEROGENEOUS DESIGNS IN NI-BASED SUPERALLOYS. AN ITERATIVE RESEARCH FRAMEWORK ENCOMPASSES SYNTHESIS AND MICROSTRUCTURAL DESIGN, MICROSTRUCTURE CHARACTERIZATION, ATOMISTIC SIMULATION, AND MESOSCALE MODELING, AND EACH CYCLE WILL REFINE DRAGONS, FOSTERING STRONGER LINKS BETWEEN PROCESSING DESCRIPTORS AND MICROSTRUCTURE FEATURES. THE BROADER IMPACTS OF THIS WORK SPAN THE POTENTIAL TO RESHAPE ENGINEERED ALLOY DEVELOPMENT AND TO FOSTER COLLABORATIONS WITH NIST SCIENTISTS. FURTHERMORE, EDUCATIONAL PROGRAMS TARGETED AT DEVELOPING A DIVERSE, SKILLED WORKFORCE IN MATERIALS ENGINEERING UNDERSCORE THE PROJECT'S COMMITMENT TO SOCIETY. 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.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
COLLABORATIVE RESEARCH: DMREF: DATA-DRIVEN DISCOVERY OF THE PROCESSING GENOME FOR HETEROGENOUS SUPERALLOY MICROSTRUCTURES -THIS DESIGNING MATERIALS TO REVOLUTIONIZE AND ENGINEER OUR FUTURE (DMREF) PROJECT AIMS TO REVOLUTIONIZE THE CREATION OF NOVEL ENGINEERING ALLOYS BY FOCUSING ON THE RELATIONSHIP BETWEEN THE PROCESS OF PRODUCTION AND THE MATERIAL'S RESULTANT MICROSTRUCTURE - THE INTERNAL STRUCTURE INVISIBLE TO THE NAKED EYE. THIS RELATIONSHIP IS A CRUCIAL BUT LESS-EXPLORED SEGMENT OF THE MATERIAL SCIENCE LIFECYCLE, YET DIRECTLY CONNECTS PROCESSING TO PERFORMANCE. USING CUTTING-EDGE MACHINE LEARNING AND DATA SCIENCE, THE PROJECT TEAM WILL BUILD A PLATFORM, NAMED AS DRAGONS (DATA-DRIVEN RECURSIVE AI-POWERED GENERATOR OF OPTIMIZED NANOSTRUCTURED SUPERALLOYS), TO DISCOVER THESE CONNECTIONS, ENABLING MORE NUANCED CONTROL OVER MATERIAL PRODUCTION. DRAGONS WILL PRESCRIBE IDEAL PROCESSING CONDITIONS TO ACHIEVE A SPECIFIC MATERIAL MICROSTRUCTURE. THIS PROJECT CARRIES BROAD SIGNIFICANCE, WITH THE POTENTIAL TO DRIVE ADVANCEMENTS IN DIVERSE SECTORS THAT RELY ON NOVEL MATERIALS INCLUDING ELECTRONICS, HEALTHCARE, ENERGY, AND TRANSPORTATION. ADDITIONALLY, THE PROJECT'S EDUCATIONAL OUTREACH ACTIVITIES AIM TO INSPIRE THE NEXT GENERATION OF SCIENTISTS AND ENGINEERS BY PROVIDING A MORE DIVERSE, INCLUSIVE, AND SUSTAINABLE PATHWAY INTO THESE FIELDS. HENCE, THIS RESEARCH CARRIES POTENTIAL TO CATALYZE SCIENTIFIC ADVANCEMENT, FOSTER ECONOMIC GROWTH, AND ENHANCE EDUCATIONAL OUTCOMES. THIS DMREF PROJECT FOCUSES ON HARNESSING MACHINE LEARNING AND DATA SCIENCE TO ADVANCE UNDERSTANDING OF THE PROCESSING-MICROSTRUCTURE RELATIONSHIPS IN THE PRODUCTION OF NOVEL MATERIALS, A KEY, YET UNDEREXPLORED FACET OF THE MATERIALS GENOME INITIATIVE (MGI). THE RESEARCH TEAM WILL DEVELOP A DATA-DRIVEN PLATFORM, NAMED AS DATA-DRIVEN RECURSIVE AI-POWERED GENERATOR OF OPTIMIZED NANOSTRUCTURED SUPERALLOYS (DRAGONS), TO DEMYSTIFY THE COMPLEX RELATIONSHIPS INHERENT IN THE CREATION OF MULTI-PHASE, HETEROGENEOUS NANOSTRUCTURED MATERIALS (HNMS). DRAGONS WILL UTILIZE PREDICTIVE MODELS TO INTERPRET MICROSTRUCTURE ATTRIBUTES BASED ON GIVEN PROCESSING CONDITIONS AND, IN A RECIPROCAL MANNER, PROVIDE PROCESSING PARAMETERS REQUIRED TO GENERATE A PREDEFINED MICROSTRUCTURE. CAPITALIZING ON EXPERTISE IN MAGNETRON SPUTTERING AND HEAT TREATMENT (MS+HT), THE RESEARCH TEAM AIMS TO ENGINEER INTRICATE HETEROGENEOUS DESIGNS IN NI-BASED SUPERALLOYS. AN ITERATIVE RESEARCH FRAMEWORK ENCOMPASSES SYNTHESIS AND MICROSTRUCTURAL DESIGN, MICROSTRUCTURE CHARACTERIZATION, ATOMISTIC SIMULATION, AND MESOSCALE MODELING, AND EACH CYCLE WILL REFINE DRAGONS, FOSTERING STRONGER LINKS BETWEEN PROCESSING DESCRIPTORS AND MICROSTRUCTURE FEATURES. THE BROADER IMPACTS OF THIS WORK SPAN THE POTENTIAL TO RESHAPE ENGINEERED ALLOY DEVELOPMENT AND TO FOSTER COLLABORATIONS WITH NIST SCIENTISTS. FURTHERMORE, EDUCATIONAL PROGRAMS TARGETED AT DEVELOPING A DIVERSE, SKILLED WORKFORCE IN MATERIALS ENGINEERING UNDERSCORE THE PROJECT'S COMMITMENT TO SOCIETY. 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.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Discovery Data
- Mission
- program
- Awarded on
- 2025-04-15
- Obligated amount
- Not disclosed
- Agency
- n/a
- Customer
- n/a
- Recipient
- THE JOHNS HOPKINS UNIVERSITY
- Actions
- 4
- Notices
- 0
- Spending points
- 1
- Bidders
- 0
- Exact source records
- 0
Links
Contract Family
ASST_NON_2502667_049|
Awardee: THE JOHNS HOPKINS UNIVERSITY • Base award: 2025-04-15
Opportunity Notices
No notices available.
Contract Detail FAQ
Search-first answers for this contract entity and its source identifiers.
- What sources feed the contract data on this site?
- Contract entities combine USAspending award references with SAM.gov-normalized procurement records (including PIID-linked actions, notices, and spending rows when available).
- Why is there a canonical /contracts URL when program pages already exist?
- Program pages keep mission context, while /contracts URLs consolidate duplicate contract entities into one indexable canonical URL so search engines attribute ranking signals to a single record.
- Which identifiers should I search to find a specific government contract?
- Use any of these identifiers: USAspending Award ID, PIID, contract key, solicitation ID, notice ID, recipient/awardee name, or agency/customer name.
- How often do contract pages update?
- Contract pages revalidate on a 10-minute cadence, while upstream source data refresh timing depends on ingest jobs and source-side publication timing.
- What is the difference between SAM.gov and USAspending in these records?
- USAspending primarily provides award and obligation visibility, while SAM.gov captures procurement lifecycle context such as solicitation notices and related action thread signals.
- Why can the contract amount differ from another source?
- Amounts can differ across snapshots because some sources report base award value while others include modification deltas, cumulative obligations, or later adjustments.
- Can one contract appear in more than one program section?
- Yes. A contract may appear in multiple program contexts; canonical entities are designed to consolidate those overlaps into a single URL for indexing and discovery.
- What is a PIID on a contract detail page?
- PIID stands for Procurement Instrument Identifier. It is the contracting identifier used to track related awards, actions, and notices across a procurement thread.
- Where should I verify the official source record for this contract?
- Use the Source record link on the contract detail page. The page also links back to the program-native detail page and, when available, the Artemis story page for thread context.
- Why are actions, notices, or spending rows sometimes missing?
- Missing rows usually mean no matched records were returned yet for that identifier set in the current source snapshot, not that the contract entity itself is invalid.
- What exact terms should I search to verify this specific contract?
- Use these identifiers in search: ASST_NON_2502667_049| | 2502667 | ASST_NON_2502667_049. Add terms like "USAspending", "SAM.gov", or the awardee name for faster exact matching.