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Machine learning-driven closed-loop discovery of hard multiple principal element alloys
Maitreyee Sharma Priyadarshini1,2, Edwin Gienger3, Jarett Ren1
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD-21218, USA. pclancy3@jhu.edu.
Materials Horizons
|July 22, 2026
Summary
This study used an active learning approach, PAL 2.0, to accelerate the discovery of high-hardness multi-principal element alloys (MPEAs). The AI successfully identified new MPEAs with exceptional hardness, including novel combinations of elements.
Area of Science:
- Materials Science
- Alloy Design
- Computational Materials Science
Background:
- Multi-principal element alloys (MPEAs) offer superior mechanical properties like high hardness and strength.
- The vast compositional space of MPEAs makes traditional discovery methods inefficient and time-consuming.
- Accelerating the identification of MPEAs with desired properties is crucial for materials innovation.
Purpose of the Study:
- To accelerate the discovery of high-hardness MPEAs using an active learning approach.
- To demonstrate the efficacy of the PAL 2.0 framework in navigating complex material compositions.
- To identify novel MPEA compositions with exceptional Vickers hardness values.
Main Methods:
- Application of the active learning framework, PAL 2.0, integrating Bayesian optimization.
- Utilizing a closed-loop system combining physics-based Gaussian process models with experimental validation.
- Synthesis of new MPEAs via rapid arc-melting based on AI-driven recommendations.
Main Results:
- Successfully synthesized 20 new MPEAs, with two exhibiting exceptionally high Vickers hardness (1269 and 1263).
- Doubled the number of known MPEAs with hardness over 1000, identifying five new compositions.
- Discovered an "out of distribution" high-hardness alloy containing silicon and tantalum, demonstrating AI's ability to suggest novel combinations.
Conclusions:
- PAL 2.0 significantly accelerates the discovery of high-performance MPEAs by efficiently exploring complex compositional spaces.
- The AI-driven approach reduces the search space for promising new materials, enabling rapid development.
- The materials-agnostic nature of PAL 2.0 provides a scalable pathway for discovering advanced materials across various scientific domains.
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