Decoding plasticity in van der Waals crystals via causality-driven interpretable machine learning
Year: 2025
Type: article
Abstract: Plastic deformation in van der Waals (vdW) crystals is essential for flexible and wearable electronics. However, its evaluation typically requires destructive testing or costly first-principles calculations. To address this, we present a causality-driven, interpretable machine learning framework that extracts accurate plasticity descriptors from easily accessible material features, avoiding empirical assumptions and high computational costs. By leveraging causal discovery, we clarify the roles of interlayer and intralayer interactions in plasticity and identify key features that govern plastic behavior. A causality-constrained symbolic regression method then generates a compact, physically meaningful descriptor with 96% accuracy in distinguishing plastic from brittle vdW materials. This descriptor enables rapid screen of large material databases and identifies 29 previously unrecognized semiconductors with superior plasticity. This framework provides a generalizable approach to convert empirical data into actionable materials knowledge, accelerating functional materials design.
Source: Nano Research
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,290
OpenAlex ID: W7117756760