Papers

Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce ElectionFit, a novel framework that uses Large Language Models (LLMs) to build a ``computational laboratory'' of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g., candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deployed this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicated the real-world outcome, demonstrating the high fidelity of our ``virtual society''. The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. ElectionFit moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.

Solar energy is a clean and renewable resource, and the low-rise, unobstructed rural buildings of northern China provide ideal conditions for photovoltaic (PV) installation compared to shaded, high-density urban areas. Yet, progress in assessing rural solar potential is limited by the absence of accurate 3D building data. This study proposes a rapid estimation approach integrating deep learning, parametric modeling, and GPU-accelerated simulation. Convolutional neural net- works (CNNs) extract building footprints from satellite imagery, which are then processed in Grasshopper to generate refined vector outlines. Combined with digital surface model (DSM) data, these outlines produce precise 3D village models. Using Vitality 2.0 for GPU-based solar simulation, the method was applied to 31 villages in Tianjin, generating parametric 3D models and estimating their solar potential. Results show that low building heights and minimal mutual shading make photovoltaic capacity scale with roof area—larger villages have greater generation potential. Moreover, villages with metal roofs exhibit higher conversion efficiency and shorter cost-recovery periods than those with concrete or ceramic-tile roofs, due to better heat dissipation. Overall, the workflow offers a practical and efficient solution for estimating rural solar potential in data-scarce regions to guide renewable energy planning and investment.

Solar energy is a clean and renewable resource, and the low-rise, unobstructed rural buildings of northern China provide ideal conditions for photovoltaic (PV) installation compared to shaded, high-density urban areas. Yet, progress in assessing rural solar potential is limited by the absence of accurate 3D building data. This study proposes a rapid estimation approach integrating deep learning, parametric modeling, and GPU-accelerated simulation. Convolutional neural net- works (CNNs) extract building footprints from satellite imagery, which are then processed in Grasshopper to generate refined vector outlines. Combined with digital surface model (DSM) data, these outlines produce precise 3D village models. Using Vitality 2.0 for GPU-based solar simulation, the method was applied to 31 villages in Tianjin, generating parametric 3D models and estimating their solar potential. Results show that low building heights and minimal mutual shading make photovoltaic capacity scale with roof area—larger villages have greater generation potential. Moreover, villages with metal roofs exhibit higher conversion efficiency and shorter cost-recovery periods than those with concrete or ceramic-tile roofs, due to better heat dissipation. Overall, the workflow offers a practical and efficient solution for estimating rural solar potential in data-scarce regions to guide renewable energy planning and investment.

The development of high-performance thermal management materials holds significant importance in fields such as chips, data centers and batteries. Materials informatics, which integrates big data and artificial intelligence, is emerging as the fourth paradigm for materials research. Over the past few years, our team has undertaken preliminary explorations in the development of advanced thermal management materials empowered by big data and artificial intelligence. In this work, we introduce three successful materials informatics applications on thermal management materials design, the construction of machine learning interatomic potentials for thermal property calculations, the discovery and generative design of high-thermal-conductivity materials, and the intelligent design of micro/nano structures for thermal transport. Those successful cases have shown great advantage for thermal management materials design via materials informatics.

This paper addresses the challenge of enhancing the equity of welfare resource distribution to tackle long-term unemployment in Germany, where traditional bureaucratic processes are often inefficient and biased. This issue is critical as it significantly affects economic productivity and social stability. The integration of AI into welfare systems presents challenges such as data quality, inherent biases, and policy integration complexity. We propose a machine learning-based framework utilizing fairness-aware algorithms and data augmentation techniques to predict and allocate resources more equitably. Our methodology involves developing a shallow Multi-Layer Perceptron (MLP) model trained on a TF-IDF vectorized dataset, alongside a simulated bureaucratic expansion as a baseline. Experimental results show that our machine learning approach, particularly in its best-performing runs, achieves higher equity, maintaining an Equity Gap Metric of 0.0, while also delivering competitive accuracy. This demonstrates the potential of AI-driven methods to outperform traditional bureaucratic approaches in fairness and efficiency, offering valuable insights for policymakers seeking to optimize resource distribution in public policy.

This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the rendering of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling.

Abstract. Solar energy, as a clean and renewable resource, has gained significant global attention. In contrast to urban areas, where buildings vary in height and are often obstructed, the relatively flat ru-ral buildings in northern China provide optimal conditions for solar panel installation. Consequently, the solar energy potential of northern rural areas has attracted significant attention from researchers. Traditional studies typically rely on solar radiation simulation software and 3D models to estimate solar radiation and the solar energy potential of buildings. However, the lack of comprehensive and accurate 3D building model data for rural areas in China has significantly hindered progress in this field. To address this limitation, this study proposes a novel method for rapidly estimating the solar energy potential of rural buildings by integrating deep learning algorithms with parametric modeling platforms. Using convolution neural networks (CNNs), the proposed method efficiently and accurate-ly extracts building footprints from complex satellite imagery. These footprints are then imported in-to the Grasshopper parametric platform to generate and optimize vector outlines of buildings. By combining these outlines with digital surface model (DSM) data containing building height infor-mation, the study constructs precise 3D building models. Furthermore, GPU-accelerated solar simula-tion software, Vitality 2.0, is used for rapid solar energy potential estimation. The study conducted building roof extraction based on satellite imagery for 31 villages in Tianjin and generated parametric three-dimensional village models. Through simulation, the research found that due to the relatively low height of village buildings and the absence of mutual shading between buildings, the larger the village scale, the greater the roof area, and consequently, the higher the photovoltaic power genera-tion capacity of the village. The study also revealed that metal roofs, which have better heat dissipa-tion, result in higher photovoltaic panel conversion efficiency. Therefore, compared to villages with roofs primarily made of concrete and ceramic tiles, villages dominated by metal roofs can recoup all the costs of photovoltaic panels in a shorter period.

Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports conte