The rapid evolution of next-generation electronics urgently demands high-performance functional materials. Two-dimensional (2D) semiconductors, characterized by tunable bandgaps, magnetic properties, and excellent optical and electronic properties, hold significant potential for applications in nanoelectronic devices, magnetic storage, and optoelectronics. However, the high computational cost of traditional Density Functional Theory (DFT) severely restricts large-scale high-throughput screening. Meanwhile, problems such as insufficient datasets and non-uniform data quality remain prevalent. Against this background, machine learning (ML), which captures intricate nonlinear correlations and accelerates the discovery of novel materials, has emerged as an efficient technical approach. This review systematically summarizes recent advances in ML-driven property prediction for 2D semiconductors. It first elaborates the fundamental properties and classifications of 2D semiconductors, and then compares traditional computational simulations with ML algorithms, clarifying the distinct advantages of data-driven approaches. Subsequently, this work focuses on the latest progress in predicting critical properties, including bandgap, magnetism, and other physical characteristics. For bandgap prediction, classical algorithms such as random forests are compared with deep learning models represented by graph neural networks. The results demonstrate that deep learning performs much better in low-data regimes and complex material systems. For magnetic property prediction, the impact of feature engineering strategies on model accuracy and efficiency is systematically analyzed. In addition, the research progress of other physical property prediction tasks is briefly summarized. Finally, future research directions for machine learning, including standardized materials databases, physics-informed machine learning, multimodal modeling, and the integration of machine learning with experimental and theoretical methods, are outlined to address challenges in data quality, model interpretability, and cross-system generalization ability. This work aims to provide a systematic theoretical foundation and methodological guidance for research on two-dimensional semiconductor materials assisted by machine learning.

Due to their high compliance, environmental adaptability, and inherent safety in human–robot interaction, soft robots have demonstrated considerable potential, including medical rehabilitation, compliant grasping, and operations in complex environments. Unlike conventional robots relying on rigid structures and discrete joint motions, soft robots use flexible materials such as elastomers and hydrogels to achieve motion and functionality through continuous deformation. Despite providing excellent adaptability, these compliant materials also introduce complex behaviors, including continuous morphological changes, material nonlinearities, time-varying properties, and environmental coupling, posing substantial challenges to modeling, control, and motion planning. In recent years, artificial intelligence (AI), through nonlinear representation, data-driven learning, adaptive optimization, and autonomous decision-making, has provided new technical pathways for addressing these challenges. Unlike previous reviews organized primarily by AI algorithm type, this work narratively reviews 39 studies and organizes AI-driven approaches according to the soft robotic system implementation process, encompassing modeling, control, and motion planning. Specifically, in modeling, neural networks, Koopman representations, and physics–data fusion methods have enhanced the representation of complex dynamical relationships. In control, learning-augmented control and reinforcement learning have reduced reliance on accurate system models and improved adaptability under uncertainty. In motion planning, learning from demonstration, reinforcement learning, and intelligent optimization methods have strengthened the capability for autonomous motion generation under complex constraints. Finally, this paper summarizes the current state of the field and discusses future trends in AI-driven soft robotics, emphasizing stronger coordination across functional modules. Closed-loop integration of prediction, decision-making, control, and feedback updates may further advance soft robots from stage-specific intelligence toward system-level autonomy.