arXiv:2606.11870v1 Announce Type: cross Abstract: Machine learning is increasingly applied to accelerate the discovery of novel materials by exploring large compositional and structural design spaces. Yet, the scarcity of high-quality data and the frequent need for out-of-distribution prediction introduce substantial uncertainty, making the assessment of model reliability essential. In this work, we investigate uncertainty quantification as a means to evaluate model confidence in the context of permanent magnet research. In a first study, we benchmark classical and modern machine learning mode
The increasing availability of computational power and advancements in machine learning techniques are enabling more sophisticated applications in materials science, particularly for complex inverse design problems.
This development allows for more efficient and reliable discovery of novel materials, accelerating innovation in critical sectors like high-performance computing, energy, and defense technology.
The ability to quantify uncertainty in ML models for material science will lead to more trustworthy predictions, reducing experimental trial-and-error and improving the speed and cost of material development.
- · Materials science research institutions
- · High-tech manufacturing
- · Renewable energy sector
- · AI/ML software providers
- · Traditional materials discovery processes
- · Companies reliant on conventional R&D cycles
Faster development and optimization of new materials with specific properties.
Accelerated innovation in areas dependent on advanced materials, such as more efficient permanent magnets for electric vehicles and wind turbines.
Potential for a competitive advantage for nations and companies that master AI-driven material discovery, impacting industrial leadership and supply chains.
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