Multi-Fidelity Learning in High-Throughput Materials Screening: A Review of Surrogate Strategies and Their Broken Promises
Year: 2025
Type: article
Abstract: The field now faces a clear choice.It can continue to publish incremental benchmark improvements that mask the gap between promise and delivery, or it can adopt the honest evaluation protocols outlined here and focus on achievable gains within explored chemical spaces while actively expanding those spaces through adaptive high-fidelity sampling.Only the latter path will convert methodological sophistication into tangible advances in computational and data-driven materials engineering.The next decade of multi-fidelity research should therefore be judged not by the elegance of the surrogate architectures but by the number of experimentally validated novel materials that would not have been found without them.
DOI: 10.68159/q586284494
Authors: Anna Svensson, Erik Lindberg
Institution: Uppsala University
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7214563550