Leveraging domain knowledge for optimal initialization in Bayesian materials optimization
Bayesian optimization (BO) has emerged as an effective strategy to accelerate the discovery of new materials by efficiently exploring complex and high-dimensional design spaces.
Authors
- James D. Paramore (ORCID: https://orcid.org/0000-0001-8058-7662)
- Brady G. Butler (ORCID: https://orcid.org/0000-0002-7677-9685)
- Raymundo Arróyave (ORCID: https://orcid.org/0000-0001-7548-8686)
- Trevor Hastings (ORCID: https://orcid.org/0000-0003-4614-7019)
Institutions
- Texas A&M University System (US)
- Texas A&M University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1039/d5dd00361j
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Army
- Army Research Laboratory