@article{Chen2026, 
author = {Xinyu Chen and Qian Chen and Liang Ma and Qionghua Zhou and Jinlan Wang},
title = {Decoding plasticity in van der Waals crystals via causality-driven interpretable machine learning},
year = {2026},
journal = {Nano Research},
volume = {19},
number = {6},
pages = {94908374},
keywords = {symbolic regression, causal analysis, van der Waals materials, plasticity, flexible electronics},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908374},
doi = {10.26599/NR.2026.94908374},
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.}
}