Details

Autor(en) / Beteiligte

• Padhy, Shakti P.
• Chaudhary, Varun
• Lim, Yee-Fun
• Zhu, Ruiming
• Thway, Muang
• Hippalgaonkar, Kedar
• Ramanujan, Raju V.

Titel

Experimentally validated inverse design of multi-property Fe-Co-Ni alloys

Ist Teil von

• iScience, 2024-05, Vol.27 (5), p.109723-109723, Article 109723

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This study presents a machine learning (ML) framework aimed at accelerating the discovery of multi-property optimized Fe-Ni-Co alloys, addressing the time-consuming, expensive, and inefficient nature of traditional methods of material discovery, development, and deployment. We compiled a detailed heterogeneous database of the magnetic, electrical, and mechanical properties of Fe-Co-Ni alloys, employing a novel ML-based imputation strategy to address gaps in property data. Leveraging this comprehensive database, we developed predictive ML models using tree-based and neural network approaches for optimizing multiple properties simultaneously. An inverse design strategy, utilizing multi-objective Bayesian optimization (MOBO), enabled the identification of promising alloy compositions. This approach was experimentally validated using high-throughput methodology, highlighting alloys such as Fe66.8Co28Ni5.2 and Fe61.9Co22.8Ni15.3, which demonstrated superior properties. The predicted properties data closely matched experimental data within 14% accuracy. Our approach can be extended to a broad range of materials systems to predict novel materials with an optimized set of properties. [Display omitted] •A machine learning framework for multi-property optimized Fe-Co-Ni alloy design•Predictive models and multi-objective Bayesian optimization predict promising alloys•Experimental validation confirms superior multi-property performance of the alloys•Fe66.8Co28Ni5.2 and Fe61.9Co22.8Ni15.3 are the identified alloy compositions Physics; Materials science; Materials synthesis