Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume 60, Issue 45, Pages 24144-24152Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202108116
Keywords
Bayesian optimization; complex solid solutions; density functional calculations; electrochemistry; high-entropy alloys
Categories
Funding
- Danish National Research Foundation Center for High-Entropy Alloy Catalysis (CHEAC) [DNRF-149]
- Danish Ministry of Higher Education and Science (Structure of Materials in Real Time (SMART) grant)
- VILLUM FONDEN [9455]
- Deutsche Forschungsgemeinschaft (DFG) [EXC 2033-390677874-RESOLV]
- European Research Council (ERC) [CasCat [833408]]
- DFG [LU1175/26-1]
Ask authors/readers for more resources
This study utilizes Bayesian optimization and DFT modeling to predict the most active compositions in high-entropy alloys, verifies the discovered optima through experimental validation, and offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys.
Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys. This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available