Experimentally validated inverse design of multi-property Fe-Co-Ni alloys
Shakti P. Padhy,
Varun Chaudhary,
Yee-Fun Lim,
Ruiming Zhu,
Muang Thway,
Kedar Hippalgaonkar,
Raju V. Ramanujan
Affiliations
Shakti P. Padhy
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore; Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA
Varun Chaudhary
Industrial and Materials Science, Chalmers University of Technology, SE-41296 Gothenburg, Sweden; Corresponding author
Yee-Fun Lim
Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A∗STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore; Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science Technology and Research (A∗STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
Ruiming Zhu
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
Muang Thway
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
Kedar Hippalgaonkar
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore; Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A∗STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore; Corresponding author
Raju V. Ramanujan
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore; Corresponding author
Summary: 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.
