Year: 2026
Type: article
Abstract: Materials design often faces an exploration blind spot where trial-and-error methods are inefficient and data-driven approaches struggle to discover new precipitate phases. Here, we propose High-Throughput Precipitate Informatics (HTPI), integrating physics-driven discovery, data-driven optimization, thermodynamic validation, and experimental verification. HTPI screens over 2 million crystallographic combinations within 2 min and identifies a previously unreported AlLiSi phase as a promising nanoscale precipitate in the age-hardening-blind Al-Si system. Machine-learning-guided Al-0.25Si-0.2Li (-0.2Mg) alloys exhibit pronounced age hardening, demonstrating HTPI as an extendable route for precipitate discovery across diverse alloy systems.
Source: Materials Research Letters
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,459
Award: G8136105750
OpenAlex ID: W7225110741