Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys
Artikel i vetenskaplig tidskrift, 2024

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 Fe66.8Co28Ni5.2and 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.

Bayesian optimization

soft magnetic materials

accelerated materials discovery

machine learning

Författare

Shakti P. Padhy

Nanyang Technological University

Varun Chaudhary

Chalmers, Industri- och materialvetenskap, Material och tillverkning

Yee Fun Lim

Agency for Science, Technology and Research (A*STAR)

Ruiming Zhu

Nanyang Technological University

Muang Thway

Nanyang Technological University

Kedar Hippalgaonkar

Nanyang Technological University

R. V. Ramanujan

Nanyang Technological University

iScience

25890042 (eISSN)

Vol. 27 5 109723

Drivkrafter

Hållbar utveckling

Ämneskategorier

Materialteknik

Data- och informationsvetenskap

Fysik

Kemi

Styrkeområden

Produktion

Energi

Materialvetenskap

Infrastruktur

Chalmers materialanalyslaboratorium

DOI

10.1016/j.isci.2024.109723

Mer information

Senast uppdaterat

2024-06-11