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.

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high-performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N₂ reduction, we apply interpretable machine learning (ML) to analyze high-throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon-supported candidates, specifically single-atom catalysts (SACs) supported by a BC₃ monolayer (TM@V_B/C-N_{n = 0–3}-BC₃) via high-throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non-eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (N_v) and nitrogen substitution (N_n), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C-N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high-throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

Developing high-performance catalysts using traditional trial-and-error methods is generally time consuming and inefficient. Here, by combining machine learning techniques and first-principle calculations, we are able to discover novel graphene-supported single-atom catalysts for nitrogen reduction reaction in a rapid way. Successfully, 45 promising catalysts with highly efficient catalytic performance are screened out from 1626 candidates. Furthermore, based on the optimal feature sets, new catalytic descriptors are constructed via symbolic regression, which can be directly used to predict single-atom catalysts with good accuracy and good generalizability. This study not only provides dozens of promising catalysts and new descriptors for nitrogen reduction reaction but also offers a potential way for rapid screening of new electrocatalysts.

Effectively controlling the selectivity of C₂ oxygenates is desirable for electrocatalytic CO₂ reduction. Copper catalyst has been considered as the most potential for reducing CO₂ to C₂ products, but it still suffers from low C₂ selectivity, high overpotential, and competitive hydrogen evolution reaction (HER). Here, we propose a design strategy to introduce a second metal that weakly binds to H and a functional ligand that provides hydrogen bonds and protons to achieve high selectivity of C₂ oxygenates and effective suppression of HER on the Cu(100) surface simultaneously. Seven metals and eleven ligands are screened using first-principles calculations, which shows that Sn is the most efficient for inhibiting HER and cysteamine (CYS) ligand is the most significant in reducing the limiting potential of ^*CO hydrogenation to ^*CHO. In the post C−C coupling steps, a so-called “pulling effect” that transfers H in the CYS ligand as a viable proton donor to the C₂ intermediate to form an H bond, can further stabilize the OH group and facilitate the selection of C₂ products toward oxygenates. Therefore, this heterogeneous electrocatalyst can effectively reduce CO₂ to ethanol and ethylene glycol with an ultra-low limiting potential of −0.43 V. This study provides a new strategy for effectively improving the selectivity of C₂ oxygenates and inhibiting HER to achieve advanced electrocatalytic CO₂ reduction.