Decoding non-linearity and complexity: deep tabular learning approaches for materials science
Materials datasets often contain skewed distributions, broad feature ranges, and multimodal behavior, posing challenges for machine learning. These complexities call for models that capture non-linearity and structure in tabular data.
Authors
- Raymundo Arróyave (ORCID: https://orcid.org/0000-0001-7548-8686)
- Vahid Attari (ORCID: https://orcid.org/0000-0003-3507-8847)
Institutions
- Texas A&M University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00166h
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59
Funders
- DEVCOM Army Research Laboratory