NASA · NASA-41 · record R0122

Inverse Design of Materials

National Aeronautics and Space Administration · GRC: Glenn Research Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Discovering new materials is typically a mix of art and science, with timelines to create and robustly test a new material mix / manufacturing method ranging from ten to twenty years. This project seeks to enable rapid discovery, optimization, qualifaction and deployment of fit-for-purpose materials.

Expected benefits

The project is currently being utilized in an NESC investigation to improve SLS core stage weld quality. The technology will be used to select experiments for a fully autonomous robotic lab that is currently being procured to design better insulating materials for electrified aircraft. Bayesian optimization frameworks predicts the next best simulation to run to minimize a target. For the PMC example, for instance, the objective was to minimize weight while maintaining a minimum margin of safety.

System outputs

Outputs include recipes and approaches for new materials custom-tailored to applications with an 4x speedup for the overall materials discovery / design lifecycle, and potential 10x throughput for the same cycle based on parallizing discovery of multiple materials at once.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported